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0
073&725X/92 $5.00 + .oo 1992 Pergamon PressLtd.
Original Contribution SEMIAUTOMATED QUALITY ASSURANCE FOR QUANTITATIVE MAGNETIC RESONANCE IMAGING G.J.
BARKER AND
P.S.TOFTS
Institute of Neurology, Queen Square, London WClN 3BG, United Kingdom It is now well establishedthat MRI can be used for quantitative (as opposed to simply qualitative) measurements, and good accuracy and precision have been obtained in phantom experiments. To make routine quantitative measurements as part of a clinical scanning protocol, however, quality assurance (QA) methods particularly suited to quantification must be developed. We describe a set of QA tests using clinical protocols on test phantoms, with which we have assessed quantitative performance of our Picker 0.5-T scanner (Picker International, Cleveland, OH) over 2 years. We also describe the automated data processing methods we have developed to deal with the large amounts of data generated by these tests. Keywords: Magnetic resonance imaging; Quality assurance; Data processing; Automation. INTRODUCTION
aspects of the MR imager must be monitored (and optimised where necessary). The first of these, stability, is a measure of long-term reproducibility, which is required for serial measurements (which may last several years) and when a large group of subjects is to be measured over an extended period. The second, precision, is a measure of short-term reproducibility, while the third, accuracy, determines how close the measurements made are to the true values of the parameters being studied. Accuracy brings two benefits, first that comparison can be made with accurate measurements made on other instruments, and second that accurate measurements are, necessarily, stable. Note that “calibration” of the imager is of no value as a way of compensating for inaccuracy unless the source of inaccuracy is known to be stable. An ideal QA programme for quantitative MRI should take initial measurements of accuracy and precision of the machine and should determine stability as long-term measurements become available. It should then detect changes in any of these aspects of machine performance with both sensitivity and speed. It should consume a minimum of machine and operator time while performance is good (since repetitive andstedious operations may dissuade the operator from doing QA tests at all), but should allow for more detailed testing when performance is poor. An ideal system would acquire and analyze the necessary scans automatically
It is now well established that MRI can be used for quantitative measurements of tissue parameters, including T,and T2,intra- and extracellular water content, blood-brain barrier permeability and potentially perfusion, diffusion, and flow. ’ The aim of these measurements is to discriminate between human or animal tissues of different disease (i.e., pathophysiological) states. High sensitivity is required, so avoidable variations due to both data acquisition and data analysis must be minimised. Variations due to the imager must be small with respect to the intrinsic variability of the tissue, and data analysis must introduce no extra sources of variability (such as operator dependence). Good accuracy and precision have been obtained in phantom experiments, but for routine quantitative measurements as part of a clinical scanning protocol, quality assurance (QA) methods particularly suited to quantification must be developed. In this paper we present a conceptual framework for designing QA programs for quantitative measurements; we review the relevant instrumental measurements which can be made, and describe the measurement of relevant phantom and tissue parameters. We have measured a subset of these parameters over a period of 18 months and present typical results. To use quantitative MRI in a clinical setting three RECEIVED
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and then notify the operator of any changes or possible problems. Manufacturers are working towards such systems, but none are currently available which address the needs of quantitative MR. Much has been written on QA in qualitative imaging2T3and tests of parameters such as those in Table 1 are usually sufficient to maintain acceptable image quality. For quantitative work these tests are equally important, but on their own are not sufficient to ensure accuracy and precision. The most sensitive measures of possible problems in quantitation are the quantitative data themselves, and the most effective QA tests therefore use standard clinical protocols on phantoms (physical models) and control subjects. Phantoms can (in principle at least) be constructed to model any tissue parameter and the true value of each parameter can be determined by techniques other than MRI. The stability, precision and accuracy of MRI measurements can then be determined relative to these “gold standards.” (Suitable techniques to provide these standard measurements depend on the parameter to be measured; for MR parameters such as relaxation times, MRS [magnetic resonance spectroscopy] is the only technique available.) Phantoms must be as realistic as
Table 1. Quality control of basic instrumental Parameter Static field Amplitude Homogeneity Gradients Amplitude Homogeneity Linearity Eddy current ,compensation Transmitter B1 Amplitude Homogeneity Linearity Receiver B1 Sensitivity Uniformity Other Slice profile
performance
possible (simulating as many as possible of the effects found when imaging tissue), but should allow short measurement times when this is consistent with the realism required. The advantage of phantom measurements over those on real tissue is their convenience and their constancy; the disadvantage is that unforseen differences between the phantom and real tissue may lead to an incorrect estimation of machine performance. In general, therefore, measurements on both phantoms and normal, stable subjects are required. The latter should be carried out at the same time as (and by the same workers as) those on the patients being studied, and provide an additional validation of the quantitative data which is independent of the physicist’s phantom tests. (Note that if the phantom measurements are unstable then there may be no need to make tissue measurements. The converse is not true, however, so phantom measurement are necessary even if tissue measurements are unstable, in order to establish the source of the instability). We review the basic instrumental, phantom and normal tissue measurements necessary for quantitative MRI in more detail below. A set of basic instrumental parameters, grouped by the main subsystems of an MR imager, is shown in Table 1, along with suggested methods for measuring each parameter. Table 2 shows examples of suitable phantom and normal tissue measurements for neurological MRI; for other areas of the body different normal tissue measurements would be made.
Measurement
Instrumental Measurements Resonant frequency Field map Phantom size in read direction* Field map Field map at specified gradients Field map (or use search coil) Signal after 90” or 180” pulse* Stimulated echo image Search coil response to RF ramp* Signal-to-noise of uniform loading phantom* Uniform phantom Read profile direction
in slice select
*A measurement routinely performed at the Institute of Neurology; see text for details.
Static field Bo. The most basic parameter of the static field (B,,) is its amplitude, which is directly measurable through the MR resonance frequency. A small drift in this parameter (of the order of 2-5 ppm/wk) is normal for superconducting magnets, and is usually compensated for (possibly without the operator even being aware of it) as part of normal patient setup and tuning. A sudden change in the drift rate, however, may signal a problem in the magnet cooling system and should be investigated immediately. The homogeneity of the B. field is also important, particularly for gradient echo or chemical shift selective imaging techniques. It can be measured either by a simple point by point method4 or by more complex imaging techniques.5-8 Adjustment of the homogeneity may be as simple as maximising the area under the FID (free induction decay) by altering the first order x,y, and z shims or may involve both passive shimming and adjustment of higher order shim coils.4 Gradients. The amplitude of the gradient fields used for spatial localisation in MRI can be determined by measuring the apparent extent (in the readout,
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Table 2. Quality control on phantoms and normal tissue
T T2 Proton density (PD) Signal intensity Multi-exponential T2 PDifw PDecw Tzicw T2ecw Blood-brain barrier permeability Volume *A measurement icw = intracellular
Normal tissue
Phantom
Parameter
Calibrated gels* D20/H20 ratio Relative signal from gels Mono-exponential gels* and bi-exponential mixtures As T, and T2 3-dimensional size of phantom*
White matter White matter White/grey ratio CSF and white matter (monoand bi-exponential) None Volume of brain or white matter
routinely performed at the Institute of Neurology; water;
ecw = extracellular
water;
see text for details. CSF = cerebrospinal fluid.
phase-encoding, or slice selection direction) of an image of a phantom of known size collected with the relevant gradient along each of the three physical axes. Measurements made in the phase-encode direction have the advantage that they are not affected by any distortion caused by BOinhomogeneity; measurements made in the read direction take less time because a large number of phase-encode steps are not required. Measurements in the slice selection direction, although imprecise because of the relatively large slice thickness typically
used, may be required to allow the object’s dimension in this direction to be checked. The homogeneity of the gradients can be determined by the field mapping techniques described above, and repeating these maps while applying gradients of different strengths allows the linearity of the gradient amplifiers to be determined. While the use of actively shielded gradient coils’ is becoming more common, eddy currents and incorrect gradient pre-emphasis can be major sources of error when conventional (unshielded) gradients are used. Eddy currents can again be investigated by field mapping techniques, or the actual shape of a gradient pulse can be determined by an NMR experiment. lo Radiofrequency (RF). The amplitude of the RF excitation field B, can be set by observing the signal returned after a 90” or 180” pulse and respectively maximising or minimising it (using a long enough TR to allow complete relaxation between excitations). It can also be set by maximising the signal from a spinecho (90~7-180-~-acquire) sequence or minimising one of the several echoes from a (90-~1-90-~2-90-~3-acquire) sequence. I1 The B, homogeneity can be plotted using a modified spin-echo imaging sequence,” while the linearity of the transmitter can be measured by observing the response to an RF pulse of known shape (say a stepped ramp) of a small search coil placed within the RF transmitter coil. Any defect in the receiver coil or amplifiers will usually become apparent through a degradation in the
SNR (signal-to-noise ratio) of a known phantom. The SNR can be measured by taking two images of a homogeneous loading phantom under identical conditions so that subtraction of one image from the other eliminates the contribution to the standard deviation of any non-uniform response. The mean signal is measured on one image over a region of interest; the standard deviation of the pixel values over the same region is measured on the image formed by subtraction; the SNR over the region is then ~6 * (mean signal)/(standard deviation). 13,14A problem in the receiver coil will usually be accompanied by a change in the coil tuning capacitance, while a preamplifier error will affect SNR only, so coil problems can be detected by noting the capacitance required to tune each coil for maximum signal from a known (loading) phantom. The images used for the SNR test can also be used to check the uniformity of the RF receiver coil, which should be a function of coil geometry only.” (Notice, however, that eddy current effects, due to poor gradient pre-emphasis, may also cause image inhomogeneity.16) Normally the coil geometry will not change, but physical distortion can occur when, for example, a patient lies on a surface coil used for spinal imaging. The final instrumental parameter which can have a marked effect on the accuracy of quantitative MR measurements is the slice profile. l7 This can be measured by setting up a pulse sequence with its readout gradient along the slice-select direction and using this to obtain a profile through a uniform phantom. Alternatively a standard pulse sequence can be used to image a phantom containing a thin slab at a known angle to the imaging plane and the slice width calculated from the apparent width of the slab in the resulting image. lse20 Phantom Measurements Phantoms for studying Tl and T2can be constructed from bottles containing agarose gels doped with paramagnetic salts, and have been shown to have the nec-
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essary long-term stability for quality assurance work 2’ By changing both the gel and paramagnetic concentration,22 or by using a mixture of paramagnetic substances,23 the T, and T2 can be independently varied over a wide range to match those found in vivo. By adding D20 to the tubes, the concentration of H20 (and thus the proton density [PD]) can also be altered. The true values of all three parameters (T, , T2, and PD) can be found by using the imager in “spectrometer mode” with all gradients turned off,24 and these can then be compared to those calculated from the spin-echo, inversion recovery, and partial saturation imaging sequences used clinically. (Typically, a pair of spin-echo sequences with different echo times would be used for calculating T2, and either a spin-echo sequence and an inversion recovery or two partial saturation sequences would be used for T, calculations.) Note that quantitative T, measurements also allow the effect of paramagnetic contrast agents to be quantified,25 so these tests also determine the accuracy of blood-brain barrier permeability and leakage space measurements. The principal problem with these phantoms is their lack of realism. Although their relaxation behaviour matches that of tissue, RF losses in real tissues are difficult to model and the effects of imperfect slice profiles may not be apparent in bottles, which are much larger than the slice thickness. The temperature of the gels must also be carefully controlled, since their relaxation times typically alter by about 2% per degree Centigrade. (Phantoms materials much less sensitive to temperature variations are available,26g27 but are not in common use). To test the ability of an MR imager to detect bi- (or multi-) exponential T2 relaxation a single compartment sample (preferably filled with pure water) can be imaged with a multi-echo sequence, and the resulting intensities can be fitted to a mono-exponential function. Since pure water has a long, single T2, any decrease in the fitted T2 or any increase in the residual sum of squares of this fit indicates problems, usually due either to increased noise or incorrectly refocussed transverse magnetisation. The latter may arise from incorrectly set 180” pulses or from gradient problems, to which the sequence is particularly sensitive because its operation requires the areas of the multiple slice selection and readout gradients to be correctly balanced. A final measurement for which phantom scans are useful is that of volume measurement, which can be monitored by checking the 3-dimensional size of a phantom. (Note that this is not necessarily the same as measuring the linear dimension of the phantom in a single slice, as described above, but may involve either a multislice or 3-dimensional [volume] acquisition.) Notice that no explicit test of image resolution is
necessary, although a scan of a “resolution phantom” can usually be combined with other tests (for instance a phantom size measurement) if visual confirmation of system performance is required. Notice, however, that in contrast to other modalities such as X-ray computed tomography (CT), the image resolution is always equal to the “digital resolution.” This is equal to the pixel size unless spatial filtering is applied to the image during the reconstruction process or there are very severe problems with the imager. Such problems will be apparent on other tests, such as that of the signal to noise ratio of an image. Normal Tissue Measurements The T,, T,, and PD of normal white matter are known,28 and white matter provides the best tissue for checking the quantification of the parameters in the brain, since quite large areas can be found that show little contamination from other tissue types. Since fairly large areas of grey matter can also be found, the ratio of the signal from grey and white matter provides a check on signal intensity which is independent of factors such as overall imager gain. (Since MS lesions, in which we have a particular interest, have a similar intensity to grey matter, this ratio also gives an indication of the sensitivity for lesion detection of the sequence being used.) Multi-exponential relaxation can be monitored by observing the CSF (which behaves in an MR image as a single compartment of pure water), and the white matter (which has some extracellular water and therefore shows a slight bi-exponential behaviour). Normal blood-brain barrier permeability is too low to measure by MRI, so the only test that can be made on normal volunteers is of the accuracy of the Tl measurement used in the quantification, which can be measured as described above. The accuracy of volume measurements can be assessed by following the volume of the whole brain and of the white matter, both of which are relatively easily segmented from an MR image. In order to detect and follow changes in any of these tissue measurements with time the same normal volunteer must be repeatedly scanned over the course of weeks, months, or years. (The same is true, of course, of the patients being studied.) Repositioning (registration) between clinical scans can be achieved by using oblique pilot scans to orient the final single or multislice sets relative to internal anatomical landmarks in the brain. Repositioning errors of less than 2.5 mm can be achieved29 along each axis without the use of software registration schemes. METHODS
There are many sources of variance present in MR images; these include random image noise, slice and
Quality assurance for quantitative MRI 0 G.J.
measurements
and phantom
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recovery and multiple spin echo sequences, with the imaging gradients turned off, on each bottle in turn. A range of inversion times (between 50 msec and 1000 msec) and echo times (between 40 msec and 600 msec) were used and the resulting values were corrected for the bias introduced by the magnitude measurement.31 T, was then calculated by a least squares fit to the inversion recovery data; T, from the spin echo data. For measured T, values between 275 msec and 1000 msec and T2 values between 50 msec and 400 msec (covering the typical clinical range of interest) the standard error in the fitted parameters was always less than + 1.5%). The total time, including set up time, for these tests was approximately 100 min, although if problems were found then more detailed (and time consuming) tests were performed to isolate the causes. The number of images produced meant that analyzing each image by hand would be very slow, so a set of data processing routines were developed to perform this analysis offline, on Sun workstations (Sun Microsystems, Mountain View, CA). The processing used simple image processing techniques (implemented using a commercial image processing package (Semper 6 [Synoptics Ltd., Cambridge, UK]) and standard Unix@commands) to reduce operator intervention to a minimum, and in doing so also reduced any operator bias in the measurements. Prior knowledge of the shape and size of the uniform phantom (though not its exact position or orientation), allowed the software to make all the calculations needed for the signal-to-noise and phantom size measurements. The number, size, and shape of the bottles making up the relaxation time phantom, along with a measurement of the background noise present in each image, allowed each bottle to be detected and
region of interest repositioning errors, short- and longterm imager instability, imager upgrades and inter- and intrapatient variation. 3o On our Picker 0.5-T scanner (now replaced by a General Electric 1.5-T Signa) we initiated a program to measure these sources of variance which included a set of QA tests performed regularly on the system. Because of time constraints we restricted ourseives to a few basic instrument measurements (marked * in Table 1) and phantom scans which matched our clinical protocols (marked * in Table 2). The instrumental measurements were chosen to cover the most important parameters of each subsystem of the imager without either taking excessive scanning time, or requiring the use of pulse sequences not available on the system (such as stimulated echo or field mapping pulse sequences). The B1 amplitude was first calibrated by minimising the signal after a 180” pulse, and the transmitter linearity was checked by measuring its response to a stepped ramp input. Once the RF transmitter was known to be correctly calibrated, the x, y, and z shim values were adjusted to maximise the FID after a 90” pulse. Four scans were then made of a uniform phantom in various orientations (as indicated in Table 3); two of the scans were for SNR calculations, three were for phantom size (and thus gradient amplitude) measurements. Finally a total of 10 scans (also detailed in Table 3) were performed on a phantom consisting of 12 bottles of doped agarose gels, of known T, and T2 to allow relaxation time measurements to be checked. Since high spatial resolution is unnecessary, only 64 phase-encode steps were used, allowing short imaging times. (Before starting our QA program we determined baseline Tl and T2values for each of the bottles in this phantom by running the standard inversion
Table 3. Instrumental
BARKER AND P.S. TOFTS
scans performed
at the Institute
of Neurology,
Pulse sequence
Orientation
SE SE SE SE
500/40 500/40 500/40 500/40
Transverse Transverse Transverse Sag&al
Size Size S/N Size
SE 1500/40 SE 1500/120 IR 1500/40/500
Transverse Transverse Transverse
Ti T2calculation T2 calculation Tl calculation
Single slice Single slice Single slice
SE 2000/40 SE 2000/120 IR 2000/40/500
Transverse Transverse Transverse
T, and T2 calculation T, calculation T, calculation
Multislice Multislice Multislice
SE 2000/40 IR 4480/40/
Coronal Coronal
T, calculation T, calculation
Multislice Multislice
Transverse Transverse
Multi-exponential MuIti-exponential
150
SE 2OUO/40,80...640 SE 2OC0/40,80...640
Use measurement measurement calculation measurement
and S/N calculation
T2 test T, test
at 0.5 T
Other details Read Read Read Read
along along along along
x y y z
Single slice, 16 echo, read along x SingIe slice, 16 echo, read along y
590
measured in a similar manner. Notice that the use of “intelligent” software means that the phantom need not consist of a regular grid or other fixed geometry (as described by Carson et al.32), nor is its exact positioning within the magnet bore important. The complete data analysis process started with input of the non-image data from the RF pulse calibration and the RF linearity test. This was folIowed by calculation of T, maps from each pair of SE and IR scans (both with TE = 40 msec) and T2 maps from each pair of SE scans (with TE = 40 msec and 120 msec). The calculation software used was identical to that used on clinical scans; it forms lookup tables of T,or T2 values indexed by the possible ratios of pixel intensities in the input images and then uses these to determine the relaxation time for each pixel. The calculation of the lookup table is exact for any particular TE/TI/TR combination, so the uncertainty in the final relaxation time is determined only by the step size of the table. In this work the lookup tables were incremented in I-msec steps, giving a maximum error in T, or T2 of f 0.5 msec. The image analysis software was used to determine the position of each bottle within the calculated T, and T2 images. To do this, it first determined the background noise level by sampling a small region at the edge of the image and used a multiple of this value as the lower limit for the intensity of “real” objects. It then searched for all connected regions within the image which had an intensity greater than this threshold and an area greater than a further threshold based on the known bottle size. Finally the average T,and T2 were found from regions of interest centered at the positions of these objects. The same software was then used to measure the signal from each bottle in each echo of the multi-echo scans. These values were fed to a graphing package, where a linear regression was performed to fit the data to a mono-exponential function, and thus determine how close the signal decay was to a single exponential. (As the initial spectroscopic measurements on the gels showed only mono-exponential relaxation, any deviations reflected machine imperfections.) Note that the use of “intelligent” software allowed the same procedure to be used on all scans, regardless of orientation and exact phantom positioning, and greatly reduced the amount of operator intervention required during data processing. It also allowed all the results (both calculated and operator entered) to be automatically archived so that plots of any parameter against time (such as those shown in the results section) could be plotted if required. The total time for data analysis was less than 15 min on a Sun Sparcstation lo, with operator intervention required only during the first and last few minutes.
1992
RESULTS The automated data analysis proved to be very reliable, and the image processing software never failed to detect the features visible in the phantom data. Figures 1A and 1B show two examples of spin-echo images of the relaxation phantom with different orientations, different phase-encode directions and different bottle positions. Figures 1C and 1D show corresponding images of the bottle positions as determined by the image analysis software; each grey level in these images represents a separate object in the original image whose size, mean intensity, and so on can be calculated. Problems did occur on occasions when machine performance was so poor that one or more of the bottles fell below the noise level and became invisible. Even in this case, however, the remainder of the bottles were correctly detected and identified by allowing the software to assume that if a smaller number of bottles than expected were found then it wouId be those with the shortest T2 that were missing. We performed most of the QA tests described above at between weekly and monthly intervals for a period of 18 months. (Single slice Tland T2 measurements were made for approximately 9 months.) The tests rep-
Fig. 1. Spin-echo images of a 12-bottle relaxation phantom in (A) the coronal and (B) the transverse plane. (C) and (D) show the positions of the bottles in (A) and (B) as determined by the software described in the text.
Quality assurance for quantitative MRI 0 G.J. BARKER AND P.S. TOFTS
M 2 gradient D----tz Y gradient
\ I
4’
1 60
0
1 120
I 180
240
1 300
591
360
-
X gradient
420
460
540
i 600
Time (days since start of study)
Fig. 2. Variation with time of x, y, and z shim values.
resent more than 70 individual measurements, each of which was repeated on 30 separate occasions (more than 2000 measurements in all), a volume of data which would have been impractical to deal with without the automated techniques described. Analysis of the test results allowed us to detect problems which might oth-
180 :
-2 H -
-True length = 200mm Y - True length = 190mm X - True length = 190mm
170
I 80
3 120
0
erwise have gone unnoticed; Fig. 2, for instance, shows a systematic drift in one of the gradient shim values which was traced, after several months, to a slowly deteriorating component in a gradient preemphasis circuit, which was’then replaced. Figure 3 shows a second example of the long term measure-
I ( I I ” 300 420 240 360 180 Time (days since start of study)
I 480
”
540
600
Fig. 3. Variation with time of apparent phantom size, measured with the read gradient along different axes.
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900 t 1 W 800 1 M
t -
z?
i
7
True T, = 940 +I- 6 ms True T, = 550 +I- 4 ms True T, = 355 J- 5 ms
600 1
i
2
500 i-
5
400 L 300
~--
t Unresolved problems
100 1
0’
0
“I 60
120
‘I
180
‘I
240
1 300
360
‘I
420
I 480
1
540
”
600
Time (days since start of study)
Fig. 4. The variation images.
with time of the measured
r, of three bottles
ments made; it shows the variation of phantom size with time, and indicates that the x and y gradients were much more stable than the z one. During the period over which they were followed, T, values in the range of clinical interest (300-700 msec)
400,
from the 1Zbottle
phantom,
calculated
from single slice
measured by single slice techniques were accurate and reproducible within approximately 5% (excluding dates when there were known hardware problems) (Fig. 4). The reproducibility of T2 measurements within the range of clinical interest (50-150 msec) was
I
I
I
_
I
300
;i 250 z. r $ 200 z I 2
[fil True T, = 290 +I- 3 ms MTrueT,=150+/-2ms \.c-----y True T, = 80 +A 2 ms
150
100 1 50
0
Fig. 5. The variation images.
60
120
with time of the measured
180 240 300 360 420 Time (days since start of study)
T2of three bottles
from the la-bottle
480
540
phantom,
600
calculated
from single slice
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Quality assurance for quantitative MRI 0 G.J. BARKER AND P.S. TOFTS
1
M
Slice4 - True T, = 940 +I-6 ms
B - -0 Slice H Slice A -A Slice id Slice
120
3 - True 4 - True 3 - True 4 True
T, T, T, T.
= = = =
940 550 550 355
+I-6 ms +/-4 ms +I-4 ms +I- 5 ms
420 160 240 300 360 Time (days since start of study)
460
Fig. 6. The variation with time of the T, of three bottles from the 1Zbottle phantom,
also very good (Fig. 5), although the measured T2values were always too low. (A low value is to be expetted, since any refocusing errors will reduce the apparent T,.) The corresponding T,and T2measurements made using multislice or multiecho techniques (Figs. 6 and 7) were neither accurate nor precise, with
w--xSlice3-TrueT,=
0
60
120
160
240
s’oo
calculated from multislice images.
measurements of T2varying by up to 25% of their mean value, and measurements of Tlby up to 40%. Notice that the errors (thought to be mainly due to problems with gradient pre-emphasis, and never resolved) show no correlation with simple instrumental parameters such as signal to noise ratio (Fig. 8), and
?iF 250 5 I-” ‘0, 200
oi’,“‘l”“‘N
540
300
360
‘N’N’l” 420
604.Zms
460
540
600
Time (days since start of study)
Fig. 7. The variation with time of the T2of three bottles from the 1Zbottle phantom,
calculated from multislice images.
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,
,
/
------~,
240 1
i
180 160
80 1 60
0
60
120
180
240
300
360
420
480
540
600
Time (days since start of study)
Fig. 8. The variation
with time of the signal-to-noise
could have gone unnoticed if relaxation time measurements had not been explicitly tested as part of the QA procedure. CONCLUSIONS By careful choice of QA tests we can monitor the quantitative performance of our machine at frequent intervals for a relatively small time investment. Phantom and normal tissue measurements allow faults to be detected earlier than otherwise and aid in the diagnosis of their cause. This does not necessarily mean, however, that the problems can be rectified and measurements can be very variable despite intensive efforts to analyze and solve the problems uncovered. In our case some tissue investigations had to be abandoned for over 12 months. The engineers working on MR systems are usually trained to return the machine to service as quickly as possible, and are likely to regard an image free from obvious artifacts as the desired end result of their work, even if quantitative measurements show that contrast, for instance, is not as it should be. For good results the engineers who service the imager must understand the special requirements of quantitative MR, and if accuracy and precision are to be maintained it may be necessary to accept some “down-time” even when the imager is producing images that are (qualitatively) quite acceptable. Since the QA results described are computer generated, record keeping is simple and long term trend
ratio.
analysis can be performed. This allows questions about the accuracy of clinical measurements, such as “How accurate is the T2image of Mr. Smith calculated from scans performed on 21 May last year?“, or “Has SNR deteriorated over the last three months?” to be answered, but does not overcome problems caused by the step changes that inevitably occur (due to machine repairs, upgrades, and software changes). Time-interleaved measurements on normal subjects are the only protection against such changes causing an apparent difference between clinical groups being studied. Acknowledgments-The Institute of Neurology MR Research Group is funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland. We would like to thank Dr. T.W. Redpath for helpful discussions and acknowledge the authors of the image display software (dispimage-D.L. Plummer, University College, London, UK) and graph plotting software (grtool-P. Turner, Oregon Graduate Institute, USA) used in our data analysis. Tables 2 and 3 are based on similar tables in reference 1 and are included with the permission of Wiley-Liss Inc.
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ity Control. Madison, WI: Medical Physics Publishing Corp. 1988. Bratement, L.; Bowman, T.T.; Donstrup, S.; Scott, K.N. Magnetic field shimming by Fourier analysis. Magn. Resort. Imaging 6459-473; 1988. Maudsley, A.A.; Oppelt, A.; Ganssen, A. Rapid measurement of magnetic field distributions using nuclear magnetic resonance. In: Siemens Forsch.- u. EntwicklBer Bd. 8, Nr.6: Springer-Verlag; 1979: p. 326. Willcott III, M.R.; Mee, G.L.; Chesick, J.P. Magnetic field mapping in NMR imaging. Magn. Resort. Imaging 5:301-306; 1987. Kawanaka, A.; Takagi, M. Estimation of static magnetic field and gradient fields from NMR image. J. Phys. E: Sci. Instrum. 19:871-875; 1986. Kim, Y.S.; Cho. Z.H. Eddy-current-compensated fieldinhomogeneity mapping in NMR imaging. J. Magn. Reson. 78:459-471; 1988. Mansfield, P.; Chapman, B. Active magnetic screening of gradient coils in NMR imaging. J. Magn. Reson. 66: 573-576; 1986. Yamamoto, E.; Kohno, H. Gradient time-shape measurement by NMR. J. Phys. E: Sci. Instrum. 19:708711; 1986. Perman, W.H.; Bernstein, M-A.; Sandstrom, J.C. A method for correctly setting the rf flip angle. Magn. Reson. Med. 9:16-24; 1989. Oh, C.H.; Hilal, S.K.; Cho, Z-H.; Mun, I.K. Radio frequency field intensity mapping using a composite spinecho sequence. Magn. Reson. Imaging 8:21-25; 1990. Bencomo, J.A.; Nitz, W.R. Acceptance test and quality assurance for magnetic resonance imaging equipment. Med. Phys. World 5(2): 16-20; 1989. Edelstein, W.A.; Bottomley, P.A.; Pfeifer, L.M. A signal to noise calibration procedure for magnetic resonance imaging systems. Med. Phys. 11:180-185; 1983. McVeigh, E.R.; Bronskill, M.J.; Henkelman, R.M. Phase and sensitivity of receiver coils in magnetic resonance imaging. Med. Phys. 13(6):806-814; 1986. Wicks, D.A.G.; Barker, G.J.; Tofts, P.S. Image uniformity correction for simple segmentation and volume measurement of Multiple Sclerosis lesions. SMRM 10th Annual Meeting, New York, Book of abstracts: 440; 1990. Redpath, T.W.; Calibration of the Aberdeen NMR imager for proton spin-lattice relaxation time measurements in vivo. Phys. Med. Biol. 27:1057-1065; 1982. McRobbie, D.W.; Lerski, R.A.; Straughan, K.; Quilter, P.; Orr, J.S. Investigation of slice characteristics in nuclear magnetic resonance imaging. Phys. Med. Biol. 31(6):613-623; 1986. Lerski, R.A.; McRobbie, D.W.; de Certaines, J.D. Pro-
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tocols and test objects for the assessment of MRI equipment. Magn. Reson. Imaging 6: 195-199; 1988. Lerski, R.A.; McRobbie, D.W.; Straughan, K.; Walker, P.M.; de Certaines, J.D.; Bernard, A.M. Multi-centre trial with protocols and prototype test objects for the assessment of MRI equipment. Magn. Resort. Imaging 6: 201-214; 1988. Walker, P.; Lerski, R.A.; Mathur-De Vre, R.; Binet, J.; Yane, F. Preparation of agarose gels as reference substances for NMR relaxation time measurement. Magn. Reson. Imaging 6:215-222; 1988. Walker, P.M.; Balmer, C.; Ablett, S.; Lerski, R.A. A test material for tissue characterisation and system calibration in MRI. Phys. Med. Biol. 34(1):5-22; 1989. Schneiders, N.J. Solutions of two paramagnetic ions in nuclear magnetic resonance phantoms. Med. Phys. 15(1):12-16; 1988. Johnson, G.; Ormerod, I.E.C.; Barnes, D.; Tofts, P.S.; MacManus, D. Accuracy and precision in the measurement of relaxation times from nuclear magnetic resonance images. Brit. J. Radiol. 60:143-153; 1987. Tofts, P.S.; Kermode, A.G. Measurement of the BloodBrain Barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts. Magn. Reson. Med. 17:357-367; 1991. Kraft, K-A.; Fatouros, P.P.; Clarke, G.D., Kishore, P.R.S. An MRI phantom material for quantitative relaxometry. Magn. Reson. Med. 5:555-562; 1987. Tofts, P.S.; Shuter, B.; Pope, J. Ni-DTPA doped agarose gel. A tissue equivalent material. Magn. Reson. Zmaging. (in press). MacManus, D.; Bartlett, P. Image contrast in nuclear magnetic resonance imaging. Radiology 52(603): 103108; 1986. MacManus, D.G.; Kermode, A.G.; Tofts, P.S. A repositioning technique for cerebral magnetic resonance imaging of patients with multiple sclerosis. SMRM 8th Annual Meeting, Amsterdam, Book of abstracts: 617; 1989. Harvey, I.; Tofts, P.S.; Morris, J.K.; Wicks, D.A.G.; Ron, M. Sources of Tr variance in normal human white matter. Magn. Reson. Imaging. 9:53-59; 1991. Tofts, P.S.; Johnson, G. Correction for Raleigh noise in an NMR magnitude image when measuring T, at low values of signal to noise ratio. SMRM 5th Annual Meeting, Montreal, Canada, Book of Abstracts:1450; 1986. Carson, P.L.; Ellis, J.; Murphy, B.W.; Zhang, Y.; Liu, G. Automated analysis of magnetic resonance imaging system performance. In: R.L. Dixon (Ed). MRI: Acceptance Testing and Quality Control. Madison, WI: Medical Physics Publishing Corp.; 1988: pp. 151-173.
l
in the USA.
Vol. 10,pp. 585-595, 1992 Copyright
All rights reserved.
0
073&725X/92 $5.00 + .oo 1992 Pergamon PressLtd.
Original Contribution SEMIAUTOMATED QUALITY ASSURANCE FOR QUANTITATIVE MAGNETIC RESONANCE IMAGING G.J.
BARKER AND
P.S.TOFTS
Institute of Neurology, Queen Square, London WClN 3BG, United Kingdom It is now well establishedthat MRI can be used for quantitative (as opposed to simply qualitative) measurements, and good accuracy and precision have been obtained in phantom experiments. To make routine quantitative measurements as part of a clinical scanning protocol, however, quality assurance (QA) methods particularly suited to quantification must be developed. We describe a set of QA tests using clinical protocols on test phantoms, with which we have assessed quantitative performance of our Picker 0.5-T scanner (Picker International, Cleveland, OH) over 2 years. We also describe the automated data processing methods we have developed to deal with the large amounts of data generated by these tests. Keywords: Magnetic resonance imaging; Quality assurance; Data processing; Automation. INTRODUCTION
aspects of the MR imager must be monitored (and optimised where necessary). The first of these, stability, is a measure of long-term reproducibility, which is required for serial measurements (which may last several years) and when a large group of subjects is to be measured over an extended period. The second, precision, is a measure of short-term reproducibility, while the third, accuracy, determines how close the measurements made are to the true values of the parameters being studied. Accuracy brings two benefits, first that comparison can be made with accurate measurements made on other instruments, and second that accurate measurements are, necessarily, stable. Note that “calibration” of the imager is of no value as a way of compensating for inaccuracy unless the source of inaccuracy is known to be stable. An ideal QA programme for quantitative MRI should take initial measurements of accuracy and precision of the machine and should determine stability as long-term measurements become available. It should then detect changes in any of these aspects of machine performance with both sensitivity and speed. It should consume a minimum of machine and operator time while performance is good (since repetitive andstedious operations may dissuade the operator from doing QA tests at all), but should allow for more detailed testing when performance is poor. An ideal system would acquire and analyze the necessary scans automatically
It is now well established that MRI can be used for quantitative measurements of tissue parameters, including T,and T2,intra- and extracellular water content, blood-brain barrier permeability and potentially perfusion, diffusion, and flow. ’ The aim of these measurements is to discriminate between human or animal tissues of different disease (i.e., pathophysiological) states. High sensitivity is required, so avoidable variations due to both data acquisition and data analysis must be minimised. Variations due to the imager must be small with respect to the intrinsic variability of the tissue, and data analysis must introduce no extra sources of variability (such as operator dependence). Good accuracy and precision have been obtained in phantom experiments, but for routine quantitative measurements as part of a clinical scanning protocol, quality assurance (QA) methods particularly suited to quantification must be developed. In this paper we present a conceptual framework for designing QA programs for quantitative measurements; we review the relevant instrumental measurements which can be made, and describe the measurement of relevant phantom and tissue parameters. We have measured a subset of these parameters over a period of 18 months and present typical results. To use quantitative MRI in a clinical setting three RECEIVED
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ACCEPTED
Address correspondence
3116192.
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to G.J. Barker.
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and then notify the operator of any changes or possible problems. Manufacturers are working towards such systems, but none are currently available which address the needs of quantitative MR. Much has been written on QA in qualitative imaging2T3and tests of parameters such as those in Table 1 are usually sufficient to maintain acceptable image quality. For quantitative work these tests are equally important, but on their own are not sufficient to ensure accuracy and precision. The most sensitive measures of possible problems in quantitation are the quantitative data themselves, and the most effective QA tests therefore use standard clinical protocols on phantoms (physical models) and control subjects. Phantoms can (in principle at least) be constructed to model any tissue parameter and the true value of each parameter can be determined by techniques other than MRI. The stability, precision and accuracy of MRI measurements can then be determined relative to these “gold standards.” (Suitable techniques to provide these standard measurements depend on the parameter to be measured; for MR parameters such as relaxation times, MRS [magnetic resonance spectroscopy] is the only technique available.) Phantoms must be as realistic as
Table 1. Quality control of basic instrumental Parameter Static field Amplitude Homogeneity Gradients Amplitude Homogeneity Linearity Eddy current ,compensation Transmitter B1 Amplitude Homogeneity Linearity Receiver B1 Sensitivity Uniformity Other Slice profile
performance
possible (simulating as many as possible of the effects found when imaging tissue), but should allow short measurement times when this is consistent with the realism required. The advantage of phantom measurements over those on real tissue is their convenience and their constancy; the disadvantage is that unforseen differences between the phantom and real tissue may lead to an incorrect estimation of machine performance. In general, therefore, measurements on both phantoms and normal, stable subjects are required. The latter should be carried out at the same time as (and by the same workers as) those on the patients being studied, and provide an additional validation of the quantitative data which is independent of the physicist’s phantom tests. (Note that if the phantom measurements are unstable then there may be no need to make tissue measurements. The converse is not true, however, so phantom measurement are necessary even if tissue measurements are unstable, in order to establish the source of the instability). We review the basic instrumental, phantom and normal tissue measurements necessary for quantitative MRI in more detail below. A set of basic instrumental parameters, grouped by the main subsystems of an MR imager, is shown in Table 1, along with suggested methods for measuring each parameter. Table 2 shows examples of suitable phantom and normal tissue measurements for neurological MRI; for other areas of the body different normal tissue measurements would be made.
Measurement
Instrumental Measurements Resonant frequency Field map Phantom size in read direction* Field map Field map at specified gradients Field map (or use search coil) Signal after 90” or 180” pulse* Stimulated echo image Search coil response to RF ramp* Signal-to-noise of uniform loading phantom* Uniform phantom Read profile direction
in slice select
*A measurement routinely performed at the Institute of Neurology; see text for details.
Static field Bo. The most basic parameter of the static field (B,,) is its amplitude, which is directly measurable through the MR resonance frequency. A small drift in this parameter (of the order of 2-5 ppm/wk) is normal for superconducting magnets, and is usually compensated for (possibly without the operator even being aware of it) as part of normal patient setup and tuning. A sudden change in the drift rate, however, may signal a problem in the magnet cooling system and should be investigated immediately. The homogeneity of the B. field is also important, particularly for gradient echo or chemical shift selective imaging techniques. It can be measured either by a simple point by point method4 or by more complex imaging techniques.5-8 Adjustment of the homogeneity may be as simple as maximising the area under the FID (free induction decay) by altering the first order x,y, and z shims or may involve both passive shimming and adjustment of higher order shim coils.4 Gradients. The amplitude of the gradient fields used for spatial localisation in MRI can be determined by measuring the apparent extent (in the readout,
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assurance
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MRI 0 G.J.
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Table 2. Quality control on phantoms and normal tissue
T T2 Proton density (PD) Signal intensity Multi-exponential T2 PDifw PDecw Tzicw T2ecw Blood-brain barrier permeability Volume *A measurement icw = intracellular
Normal tissue
Phantom
Parameter
Calibrated gels* D20/H20 ratio Relative signal from gels Mono-exponential gels* and bi-exponential mixtures As T, and T2 3-dimensional size of phantom*
White matter White matter White/grey ratio CSF and white matter (monoand bi-exponential) None Volume of brain or white matter
routinely performed at the Institute of Neurology; water;
ecw = extracellular
water;
see text for details. CSF = cerebrospinal fluid.
phase-encoding, or slice selection direction) of an image of a phantom of known size collected with the relevant gradient along each of the three physical axes. Measurements made in the phase-encode direction have the advantage that they are not affected by any distortion caused by BOinhomogeneity; measurements made in the read direction take less time because a large number of phase-encode steps are not required. Measurements in the slice selection direction, although imprecise because of the relatively large slice thickness typically
used, may be required to allow the object’s dimension in this direction to be checked. The homogeneity of the gradients can be determined by the field mapping techniques described above, and repeating these maps while applying gradients of different strengths allows the linearity of the gradient amplifiers to be determined. While the use of actively shielded gradient coils’ is becoming more common, eddy currents and incorrect gradient pre-emphasis can be major sources of error when conventional (unshielded) gradients are used. Eddy currents can again be investigated by field mapping techniques, or the actual shape of a gradient pulse can be determined by an NMR experiment. lo Radiofrequency (RF). The amplitude of the RF excitation field B, can be set by observing the signal returned after a 90” or 180” pulse and respectively maximising or minimising it (using a long enough TR to allow complete relaxation between excitations). It can also be set by maximising the signal from a spinecho (90~7-180-~-acquire) sequence or minimising one of the several echoes from a (90-~1-90-~2-90-~3-acquire) sequence. I1 The B, homogeneity can be plotted using a modified spin-echo imaging sequence,” while the linearity of the transmitter can be measured by observing the response to an RF pulse of known shape (say a stepped ramp) of a small search coil placed within the RF transmitter coil. Any defect in the receiver coil or amplifiers will usually become apparent through a degradation in the
SNR (signal-to-noise ratio) of a known phantom. The SNR can be measured by taking two images of a homogeneous loading phantom under identical conditions so that subtraction of one image from the other eliminates the contribution to the standard deviation of any non-uniform response. The mean signal is measured on one image over a region of interest; the standard deviation of the pixel values over the same region is measured on the image formed by subtraction; the SNR over the region is then ~6 * (mean signal)/(standard deviation). 13,14A problem in the receiver coil will usually be accompanied by a change in the coil tuning capacitance, while a preamplifier error will affect SNR only, so coil problems can be detected by noting the capacitance required to tune each coil for maximum signal from a known (loading) phantom. The images used for the SNR test can also be used to check the uniformity of the RF receiver coil, which should be a function of coil geometry only.” (Notice, however, that eddy current effects, due to poor gradient pre-emphasis, may also cause image inhomogeneity.16) Normally the coil geometry will not change, but physical distortion can occur when, for example, a patient lies on a surface coil used for spinal imaging. The final instrumental parameter which can have a marked effect on the accuracy of quantitative MR measurements is the slice profile. l7 This can be measured by setting up a pulse sequence with its readout gradient along the slice-select direction and using this to obtain a profile through a uniform phantom. Alternatively a standard pulse sequence can be used to image a phantom containing a thin slab at a known angle to the imaging plane and the slice width calculated from the apparent width of the slab in the resulting image. lse20 Phantom Measurements Phantoms for studying Tl and T2can be constructed from bottles containing agarose gels doped with paramagnetic salts, and have been shown to have the nec-
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essary long-term stability for quality assurance work 2’ By changing both the gel and paramagnetic concentration,22 or by using a mixture of paramagnetic substances,23 the T, and T2 can be independently varied over a wide range to match those found in vivo. By adding D20 to the tubes, the concentration of H20 (and thus the proton density [PD]) can also be altered. The true values of all three parameters (T, , T2, and PD) can be found by using the imager in “spectrometer mode” with all gradients turned off,24 and these can then be compared to those calculated from the spin-echo, inversion recovery, and partial saturation imaging sequences used clinically. (Typically, a pair of spin-echo sequences with different echo times would be used for calculating T2, and either a spin-echo sequence and an inversion recovery or two partial saturation sequences would be used for T, calculations.) Note that quantitative T, measurements also allow the effect of paramagnetic contrast agents to be quantified,25 so these tests also determine the accuracy of blood-brain barrier permeability and leakage space measurements. The principal problem with these phantoms is their lack of realism. Although their relaxation behaviour matches that of tissue, RF losses in real tissues are difficult to model and the effects of imperfect slice profiles may not be apparent in bottles, which are much larger than the slice thickness. The temperature of the gels must also be carefully controlled, since their relaxation times typically alter by about 2% per degree Centigrade. (Phantoms materials much less sensitive to temperature variations are available,26g27 but are not in common use). To test the ability of an MR imager to detect bi- (or multi-) exponential T2 relaxation a single compartment sample (preferably filled with pure water) can be imaged with a multi-echo sequence, and the resulting intensities can be fitted to a mono-exponential function. Since pure water has a long, single T2, any decrease in the fitted T2 or any increase in the residual sum of squares of this fit indicates problems, usually due either to increased noise or incorrectly refocussed transverse magnetisation. The latter may arise from incorrectly set 180” pulses or from gradient problems, to which the sequence is particularly sensitive because its operation requires the areas of the multiple slice selection and readout gradients to be correctly balanced. A final measurement for which phantom scans are useful is that of volume measurement, which can be monitored by checking the 3-dimensional size of a phantom. (Note that this is not necessarily the same as measuring the linear dimension of the phantom in a single slice, as described above, but may involve either a multislice or 3-dimensional [volume] acquisition.) Notice that no explicit test of image resolution is
necessary, although a scan of a “resolution phantom” can usually be combined with other tests (for instance a phantom size measurement) if visual confirmation of system performance is required. Notice, however, that in contrast to other modalities such as X-ray computed tomography (CT), the image resolution is always equal to the “digital resolution.” This is equal to the pixel size unless spatial filtering is applied to the image during the reconstruction process or there are very severe problems with the imager. Such problems will be apparent on other tests, such as that of the signal to noise ratio of an image. Normal Tissue Measurements The T,, T,, and PD of normal white matter are known,28 and white matter provides the best tissue for checking the quantification of the parameters in the brain, since quite large areas can be found that show little contamination from other tissue types. Since fairly large areas of grey matter can also be found, the ratio of the signal from grey and white matter provides a check on signal intensity which is independent of factors such as overall imager gain. (Since MS lesions, in which we have a particular interest, have a similar intensity to grey matter, this ratio also gives an indication of the sensitivity for lesion detection of the sequence being used.) Multi-exponential relaxation can be monitored by observing the CSF (which behaves in an MR image as a single compartment of pure water), and the white matter (which has some extracellular water and therefore shows a slight bi-exponential behaviour). Normal blood-brain barrier permeability is too low to measure by MRI, so the only test that can be made on normal volunteers is of the accuracy of the Tl measurement used in the quantification, which can be measured as described above. The accuracy of volume measurements can be assessed by following the volume of the whole brain and of the white matter, both of which are relatively easily segmented from an MR image. In order to detect and follow changes in any of these tissue measurements with time the same normal volunteer must be repeatedly scanned over the course of weeks, months, or years. (The same is true, of course, of the patients being studied.) Repositioning (registration) between clinical scans can be achieved by using oblique pilot scans to orient the final single or multislice sets relative to internal anatomical landmarks in the brain. Repositioning errors of less than 2.5 mm can be achieved29 along each axis without the use of software registration schemes. METHODS
There are many sources of variance present in MR images; these include random image noise, slice and
Quality assurance for quantitative MRI 0 G.J.
measurements
and phantom
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recovery and multiple spin echo sequences, with the imaging gradients turned off, on each bottle in turn. A range of inversion times (between 50 msec and 1000 msec) and echo times (between 40 msec and 600 msec) were used and the resulting values were corrected for the bias introduced by the magnitude measurement.31 T, was then calculated by a least squares fit to the inversion recovery data; T, from the spin echo data. For measured T, values between 275 msec and 1000 msec and T2 values between 50 msec and 400 msec (covering the typical clinical range of interest) the standard error in the fitted parameters was always less than + 1.5%). The total time, including set up time, for these tests was approximately 100 min, although if problems were found then more detailed (and time consuming) tests were performed to isolate the causes. The number of images produced meant that analyzing each image by hand would be very slow, so a set of data processing routines were developed to perform this analysis offline, on Sun workstations (Sun Microsystems, Mountain View, CA). The processing used simple image processing techniques (implemented using a commercial image processing package (Semper 6 [Synoptics Ltd., Cambridge, UK]) and standard Unix@commands) to reduce operator intervention to a minimum, and in doing so also reduced any operator bias in the measurements. Prior knowledge of the shape and size of the uniform phantom (though not its exact position or orientation), allowed the software to make all the calculations needed for the signal-to-noise and phantom size measurements. The number, size, and shape of the bottles making up the relaxation time phantom, along with a measurement of the background noise present in each image, allowed each bottle to be detected and
region of interest repositioning errors, short- and longterm imager instability, imager upgrades and inter- and intrapatient variation. 3o On our Picker 0.5-T scanner (now replaced by a General Electric 1.5-T Signa) we initiated a program to measure these sources of variance which included a set of QA tests performed regularly on the system. Because of time constraints we restricted ourseives to a few basic instrument measurements (marked * in Table 1) and phantom scans which matched our clinical protocols (marked * in Table 2). The instrumental measurements were chosen to cover the most important parameters of each subsystem of the imager without either taking excessive scanning time, or requiring the use of pulse sequences not available on the system (such as stimulated echo or field mapping pulse sequences). The B1 amplitude was first calibrated by minimising the signal after a 180” pulse, and the transmitter linearity was checked by measuring its response to a stepped ramp input. Once the RF transmitter was known to be correctly calibrated, the x, y, and z shim values were adjusted to maximise the FID after a 90” pulse. Four scans were then made of a uniform phantom in various orientations (as indicated in Table 3); two of the scans were for SNR calculations, three were for phantom size (and thus gradient amplitude) measurements. Finally a total of 10 scans (also detailed in Table 3) were performed on a phantom consisting of 12 bottles of doped agarose gels, of known T, and T2 to allow relaxation time measurements to be checked. Since high spatial resolution is unnecessary, only 64 phase-encode steps were used, allowing short imaging times. (Before starting our QA program we determined baseline Tl and T2values for each of the bottles in this phantom by running the standard inversion
Table 3. Instrumental
BARKER AND P.S. TOFTS
scans performed
at the Institute
of Neurology,
Pulse sequence
Orientation
SE SE SE SE
500/40 500/40 500/40 500/40
Transverse Transverse Transverse Sag&al
Size Size S/N Size
SE 1500/40 SE 1500/120 IR 1500/40/500
Transverse Transverse Transverse
Ti T2calculation T2 calculation Tl calculation
Single slice Single slice Single slice
SE 2000/40 SE 2000/120 IR 2000/40/500
Transverse Transverse Transverse
T, and T2 calculation T, calculation T, calculation
Multislice Multislice Multislice
SE 2000/40 IR 4480/40/
Coronal Coronal
T, calculation T, calculation
Multislice Multislice
Transverse Transverse
Multi-exponential MuIti-exponential
150
SE 2OUO/40,80...640 SE 2OC0/40,80...640
Use measurement measurement calculation measurement
and S/N calculation
T2 test T, test
at 0.5 T
Other details Read Read Read Read
along along along along
x y y z
Single slice, 16 echo, read along x SingIe slice, 16 echo, read along y
590
measured in a similar manner. Notice that the use of “intelligent” software means that the phantom need not consist of a regular grid or other fixed geometry (as described by Carson et al.32), nor is its exact positioning within the magnet bore important. The complete data analysis process started with input of the non-image data from the RF pulse calibration and the RF linearity test. This was folIowed by calculation of T, maps from each pair of SE and IR scans (both with TE = 40 msec) and T2 maps from each pair of SE scans (with TE = 40 msec and 120 msec). The calculation software used was identical to that used on clinical scans; it forms lookup tables of T,or T2 values indexed by the possible ratios of pixel intensities in the input images and then uses these to determine the relaxation time for each pixel. The calculation of the lookup table is exact for any particular TE/TI/TR combination, so the uncertainty in the final relaxation time is determined only by the step size of the table. In this work the lookup tables were incremented in I-msec steps, giving a maximum error in T, or T2 of f 0.5 msec. The image analysis software was used to determine the position of each bottle within the calculated T, and T2 images. To do this, it first determined the background noise level by sampling a small region at the edge of the image and used a multiple of this value as the lower limit for the intensity of “real” objects. It then searched for all connected regions within the image which had an intensity greater than this threshold and an area greater than a further threshold based on the known bottle size. Finally the average T,and T2 were found from regions of interest centered at the positions of these objects. The same software was then used to measure the signal from each bottle in each echo of the multi-echo scans. These values were fed to a graphing package, where a linear regression was performed to fit the data to a mono-exponential function, and thus determine how close the signal decay was to a single exponential. (As the initial spectroscopic measurements on the gels showed only mono-exponential relaxation, any deviations reflected machine imperfections.) Note that the use of “intelligent” software allowed the same procedure to be used on all scans, regardless of orientation and exact phantom positioning, and greatly reduced the amount of operator intervention required during data processing. It also allowed all the results (both calculated and operator entered) to be automatically archived so that plots of any parameter against time (such as those shown in the results section) could be plotted if required. The total time for data analysis was less than 15 min on a Sun Sparcstation lo, with operator intervention required only during the first and last few minutes.
1992
RESULTS The automated data analysis proved to be very reliable, and the image processing software never failed to detect the features visible in the phantom data. Figures 1A and 1B show two examples of spin-echo images of the relaxation phantom with different orientations, different phase-encode directions and different bottle positions. Figures 1C and 1D show corresponding images of the bottle positions as determined by the image analysis software; each grey level in these images represents a separate object in the original image whose size, mean intensity, and so on can be calculated. Problems did occur on occasions when machine performance was so poor that one or more of the bottles fell below the noise level and became invisible. Even in this case, however, the remainder of the bottles were correctly detected and identified by allowing the software to assume that if a smaller number of bottles than expected were found then it wouId be those with the shortest T2 that were missing. We performed most of the QA tests described above at between weekly and monthly intervals for a period of 18 months. (Single slice Tland T2 measurements were made for approximately 9 months.) The tests rep-
Fig. 1. Spin-echo images of a 12-bottle relaxation phantom in (A) the coronal and (B) the transverse plane. (C) and (D) show the positions of the bottles in (A) and (B) as determined by the software described in the text.
Quality assurance for quantitative MRI 0 G.J. BARKER AND P.S. TOFTS
M 2 gradient D----tz Y gradient
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X gradient
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Time (days since start of study)
Fig. 2. Variation with time of x, y, and z shim values.
resent more than 70 individual measurements, each of which was repeated on 30 separate occasions (more than 2000 measurements in all), a volume of data which would have been impractical to deal with without the automated techniques described. Analysis of the test results allowed us to detect problems which might oth-
180 :
-2 H -
-True length = 200mm Y - True length = 190mm X - True length = 190mm
170
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erwise have gone unnoticed; Fig. 2, for instance, shows a systematic drift in one of the gradient shim values which was traced, after several months, to a slowly deteriorating component in a gradient preemphasis circuit, which was’then replaced. Figure 3 shows a second example of the long term measure-
I ( I I ” 300 420 240 360 180 Time (days since start of study)
I 480
”
540
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Fig. 3. Variation with time of apparent phantom size, measured with the read gradient along different axes.
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Magnetic Resonance Imaging 0 Volume 10, Number 4, 1992
900 t 1 W 800 1 M
t -
z?
i
7
True T, = 940 +I- 6 ms True T, = 550 +I- 4 ms True T, = 355 J- 5 ms
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i
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Fig. 4. The variation images.
with time of the measured
r, of three bottles
ments made; it shows the variation of phantom size with time, and indicates that the x and y gradients were much more stable than the z one. During the period over which they were followed, T, values in the range of clinical interest (300-700 msec)
400,
from the 1Zbottle
phantom,
calculated
from single slice
measured by single slice techniques were accurate and reproducible within approximately 5% (excluding dates when there were known hardware problems) (Fig. 4). The reproducibility of T2 measurements within the range of clinical interest (50-150 msec) was
I
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[fil True T, = 290 +I- 3 ms MTrueT,=150+/-2ms \.c-----y True T, = 80 +A 2 ms
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Fig. 5. The variation images.
60
120
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180 240 300 360 420 Time (days since start of study)
T2of three bottles
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540
phantom,
600
calculated
from single slice
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Quality assurance for quantitative MRI 0 G.J. BARKER AND P.S. TOFTS
1
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Slice4 - True T, = 940 +I-6 ms
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T, T, T, T.
= = = =
940 550 550 355
+I-6 ms +/-4 ms +I-4 ms +I- 5 ms
420 160 240 300 360 Time (days since start of study)
460
Fig. 6. The variation with time of the T, of three bottles from the 1Zbottle phantom,
also very good (Fig. 5), although the measured T2values were always too low. (A low value is to be expetted, since any refocusing errors will reduce the apparent T,.) The corresponding T,and T2measurements made using multislice or multiecho techniques (Figs. 6 and 7) were neither accurate nor precise, with
w--xSlice3-TrueT,=
0
60
120
160
240
s’oo
calculated from multislice images.
measurements of T2varying by up to 25% of their mean value, and measurements of Tlby up to 40%. Notice that the errors (thought to be mainly due to problems with gradient pre-emphasis, and never resolved) show no correlation with simple instrumental parameters such as signal to noise ratio (Fig. 8), and
?iF 250 5 I-” ‘0, 200
oi’,“‘l”“‘N
540
300
360
‘N’N’l” 420
604.Zms
460
540
600
Time (days since start of study)
Fig. 7. The variation with time of the T2of three bottles from the 1Zbottle phantom,
calculated from multislice images.
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Magnetic Resonance Imaging 0 Volume 10, Number 4, 1992
,
,
/
------~,
240 1
i
180 160
80 1 60
0
60
120
180
240
300
360
420
480
540
600
Time (days since start of study)
Fig. 8. The variation
with time of the signal-to-noise
could have gone unnoticed if relaxation time measurements had not been explicitly tested as part of the QA procedure. CONCLUSIONS By careful choice of QA tests we can monitor the quantitative performance of our machine at frequent intervals for a relatively small time investment. Phantom and normal tissue measurements allow faults to be detected earlier than otherwise and aid in the diagnosis of their cause. This does not necessarily mean, however, that the problems can be rectified and measurements can be very variable despite intensive efforts to analyze and solve the problems uncovered. In our case some tissue investigations had to be abandoned for over 12 months. The engineers working on MR systems are usually trained to return the machine to service as quickly as possible, and are likely to regard an image free from obvious artifacts as the desired end result of their work, even if quantitative measurements show that contrast, for instance, is not as it should be. For good results the engineers who service the imager must understand the special requirements of quantitative MR, and if accuracy and precision are to be maintained it may be necessary to accept some “down-time” even when the imager is producing images that are (qualitatively) quite acceptable. Since the QA results described are computer generated, record keeping is simple and long term trend
ratio.
analysis can be performed. This allows questions about the accuracy of clinical measurements, such as “How accurate is the T2image of Mr. Smith calculated from scans performed on 21 May last year?“, or “Has SNR deteriorated over the last three months?” to be answered, but does not overcome problems caused by the step changes that inevitably occur (due to machine repairs, upgrades, and software changes). Time-interleaved measurements on normal subjects are the only protection against such changes causing an apparent difference between clinical groups being studied. Acknowledgments-The Institute of Neurology MR Research Group is funded by the Multiple Sclerosis Society of Great Britain and Northern Ireland. We would like to thank Dr. T.W. Redpath for helpful discussions and acknowledge the authors of the image display software (dispimage-D.L. Plummer, University College, London, UK) and graph plotting software (grtool-P. Turner, Oregon Graduate Institute, USA) used in our data analysis. Tables 2 and 3 are based on similar tables in reference 1 and are included with the permission of Wiley-Liss Inc.
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