Ma@etic Resonance Ima@t& Vol. 10, pp. 47 1-485. 1992 Printed in the USA. Al rights reserved.

0730-725x/92 $5.00 + 40 1992 Pergamon Press Ltd.

??Technical Note

SPECTROSCOPIC IMAGING DISPLAY AND ANALYSIS A. A. MAUDSLEY,*$

E. LIN,*$

and M. W. Weiner*t$.

Departments of *Radiology and tMedicine, University of California San Francisco, and $DVA Medical Center, San Francisco, CA 94121 USA A system for display of magnetic resonance (MR) spectroscopic imaging (SI) data is described which provides for efflcient review and analysis of the multidimensional spectroscopic and spatial data format of this technique. Features include the rapid display of spectra from selected image voxels, formation of spectroscopic images, spectral and image data processing operations, methods for correlatlng spectroscopic image data with high resolution ‘H MR images, and hardcopy facilities. Examples are shown for 31P and ‘H spectroscopic imaging studies obtained in human and rat brain. Keywords: Magnetic resonance; Spectroscopic imaging; Software; Image processing.

cations from this group. 3-13Al1 programs were written in FORTRAN on VAX workstations (Micro VAX, Digital Equipment, Maynard, MA) running under VMS with VWS/UIS workstation display software and using an 8-plane color display.

INTRODUCTION

Magnetic resonance spectroscopic imaging (SI) techniques’s’ enable acquisition of high resolution NMR spectra from multiple contiguous regions distributed throughout the imaging volume. The resultant data can be viewed as spectra from multiple volumes or as images of the distributions of one or more spin resonances, enabling the formation of metabolite maps with a number of potential applications for biomedical studies. The analysis of the combined spatial and spectra1 information, which is typically contained within large data sizes, requires a number of unique display and analysis features which are generally not available with conventional image display systems. We have developed software that provides for efficient processing and display of multidimensional SI data, and runs on moderately priced computer workstations. The features of this software are described, and demonstrated for proton and phosphorus data taken from human SI studies at 2.0 T (Gyroscan, Philips Medical Systems) and for rat brain SI studies at 7.0 T (QUEST 4400, Nalorac Cryogenics Corp., Martinez, CA). This report also describes operational features which have been found to be particularly useful in our experience with using SI studies for biomedical applications. Full details of the MR spectroscopic imaging techniques used and of the specific studies carried out are given elsewhere in a number of publi-

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Al1 SI studies were carried out using gradient phase encoding in two or three spatial dimensions.‘-‘3 For phosphorus SI studies, data acquisition was carried out as a short spin-echo sequence with acquisition of the second half of the spin echo only.3 For proton SI studies, spin-echo observation was used, with acquisition of either the full spin-echo data, or as an asymmetric observation with sampling of either 16% (for al1 human studies) or 35% (for animal studies with TE = 136 msec) of the first half of the spin echo.4,7J0 The acquired three or four dimensional data were then transferred to a computer workstation where reconstruction was carried out by multidimensional Fourier transformation with standard processing options such as zerofilling, digital filtering, phase correction, and so on. Filtering was applied in both spatial and spectra1 dimensions, with the available options being multiplication by an exponential, Gaussian, convolution difference (in spectra1 dimension only), Hamming function, and any function contained in a data file.

RECEIVED10/11/91; ACCEPTED 12/22/91. Address correspondence to: Andrew A. Maudsley, PhD,

DVA Medical Center, 4150 Clement St., llM, cisco, CA 94121. 471

San Fran-

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Typical spectra1 smoothing parameters for each experiment type were a line broadening of 10 Hz for 2-T 31P brain SI; of 5 Hz for 2T ‘H brain SI; and of 15 Hz for 7T ‘H rat bram SI. For asymmetrie echo sampling, a filter function was constructed that applied a Hamming apodization function over the initial part of the spin-echo data only, with any additional line broadening applied to the full echo data. For a 35”Io sampling asymmetry factor there was negligible difference in the lineshapes observed for real and magnitude data, and magnitude spectra are generally displayed. For 16% asymmetry, greater differences became apparent, and real spectra are generally displayed. In the spatial dimensions a mild Gaussian multiplication was generally used, with typical smoothing functions corresponding to multiplication of the final k-space values by exp(-0.3). One option which is available during the SI Fourier reconstruction is the formation of an additional image data set, known as the reference image, which represents the spin density distribution integrated over a predefined spectra1 region. This image is used as a spatial reference in the SI data display. The display of al1 slices of this data set has also been found to be particularly useful for initial review of multislice data. The spectra1 region used for the formation of the reference image depends on the application, and may typically be the total spin density distribution for 31P SI or the N-acetyl-asparate (NAA) resonance for ‘H brain SI studies. The spectra1 regions to be integrated are selected by the operator prior to Fourier transformation using the total volume spectrum, obtained from the zero phase-encoding data acquisition, to identify the resonances to be included. Alternatively, these regions may be automatically selected, with signal integration being carried out over al1 regions where the intensity of the total volume spectrum exceeds a threshold value. Individual spectroscopic images may also be defined by this procedure and created during the data reconstruction; however, in practice greater flexibility and accuracy in selecting spectra1 regions is possible after reconstruction. This is due to the effects of field inhomogeneity which cause line-broadening and increased overlap of resonances in the total volume spectrum, making identification of the individual resonances difficult. Following reconstruction the individual voxel spectra are considerably less affected by macroscopic field inhomogeneity, making identification of resonances easier. In addition, the spatially dependent field shifts wil1 result in inaccurate signal integration over some image regions if the spectra1 region is too narrow.i4 However, if the operation is performed on the individual 2D slices of a 3D data set, this error is re-

duced. The spatially dependent frequency shifts, however, stil1 remain a significant problem and methods of field inhomogeneity correction are included, as described below . Multidimensional SI data sets are typically large, making the efficiency of data manipulation and storage a concern. To reduce data sizes, the selection of smaller regions in both spatial and spectral dimensions has been included as a processing option. For a typical 4D 31P SI study,3J the acquired data consists of 12 x 12 x 12 phase-encoding measurements, each of 256 complex data points. The data are then reconstructed with zerofilling to 5 12 spectra1 data points at 32 x 32 in-plane voxels and 16 slices. Spatial and spectral selection is then taken, to produce a data set of 32 x 32 x 8 x 256 complex data points. The final image data size is 16 Mb, with an intermediate data set of 8 Mb also being required during processing. For a complete SI study the data also include a multislice, two echo, ‘H MRI and the reference SI data. For the 31P SI study described, the combined total data sizes may therefore reach over 28 Mb per study. For 3D ‘H studies, these data sizes become even larger. To minimize the time for transferring the data to and from disk during data processing and display, the software makes considerable use of overlapped disk I/O while processing is carried out by the CPU. For the data sizes just described, the Fourier reconstruction takes 25 min on a Micro VAX 11 or 3.3 min on a VAX 3 100/76. In the latter example the average CPU usage varies between 84 and 93%, indicating that for this case the processing time is limited by disk 110 capability. Al1 processing options and acquisition parameters are defined in text files and each data set has an associated parameter file. For the final reconstructed data, relevant data processing parameters such as filter functions are maintained. Separate parameter files for the SI data and the scout MRI are used by the SI display. SPECTROSCOPIC IMAGING DISPLAY Overview

The SI Display (SID) consists of a collection of windows which control access to the different types of information contained in the SI data. The program operation makes considerable use of the windowing environment of a workstation and of the mouse, which we refer to with the references to “pointing” and “clicking.” Primary communication with each window is carried out through the use of the mouse and selection of program options is performed via a mousedriven menu selection, though keyboard input is at

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times required. The number of windows visible on the screen at any one time varies depending on the actions of the user. Each window may be moved, resized or shrunk to an icon in the normal manner provided by the workstation window manager, and while SID is active the normal operation of the workstation is continued; for example additional process windows may be opened if desired. The basic functions of the program include: plotting of spectra from any region, creating and displaying and processing spectroscopic images, displaying high resolution MRI “scout” images, and copying images or plots to disk or hardcopy devices. On starting, the program displays the reference and scout images, if these exist, and a blank spectroscopic image window and plot window. By using the mouse as a pointer within these image windows the operator can select the locations from which the spectra1 data are displayed and from the plot window, select the frequency regions

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for formation of spectroscopic images. The initial screen display is shown in Fig. 1, where in addition to the primary image and plot windows, can also be seen a slider input window, for setting the image lookup table leve1 and width (brightness and contrast) values, the initial menu selection window, and a submenu. Additional windows may be created by the operator as required for the many additional display features provided. Not shown in Fig. 1 is the main dialogue window, to which al1 keyboard entries are directed and error messages are written. The lookup table used for the image display may be as greyscale or color, as wel1 as combinations provided in the dual-lookup table and dual-parameter display features described below. A linear interpolation of images is provided during display which may be turned off if desired. By using a factor of two interpolation by zerofilling during Fourier reconstruction, plus an additional factor of two interpolation on display, a

Fig. 1. The primary SID windows include the scout MRI (top left); the reference spectroscopic image (bottom left); the main spectroscopic image (center top); and the main plot window. Also shown here are the image lookup-table and an example menu selection. Data from a human 31P brain study is shown, with the windows having been grouped together for the purpose of this photograph.

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substantial improvement in the appearance of the low resolution images, which are typical of SI studies, is provided. While a stil1 greater degree of Fourier interpolation may provide some further visual improvement, the increased data sizes and data processing times make this option unattractive for genera1 use. For 3D (volumetric) SI data, the program provides for selection of spectra and spectroscopic images from a single slice at a time. However, al1 planes of the scout MRI or the SI reference data may be viewed together, as is shown in Fig. 2. The initial multislice display is done with a smal1 image size, typically requiring subsampling for MRI data, to avoid using too large an area of the display screen. The display of al1 slices in a single window allows the full data set to be quickly reviewed and provides a convenient method for selecting a slice for more detailed analysis by clicking on the desired image. The slice selection can also be changed by using the second and third mouse buttons in any of the primary image windows, to decrease or increase the slice number.

Spectra1 Display Spectra may be plotted from any voxel in the currently selected slice by using the mouse to point to the desired position in any of the main image windows. The quick response provided by the mouse-driven selection allows spectra from a wide region to be rapidly viewed. The images displayed in both the SI reference and scout image windows are from the same slice, and provide the visual reference for selecting the regions for the spectra1 display. When selection is made from the scout MRI window, any differences in the field of view (FOV) or spatial offset of this data from the SI are automatically accounted for . A number of options are available for altering the displayed plot format, including: displaying the real, imaginary, complex or magnitude data; changing the phase of the displayed data; automatically staling each plot or using a fixed staling factor, with altered scaling also possible in factors of two; smoothing the data using a three-point running average; changing the frequency readout units and the zero reference position;

Fig. 2. Any selection of planes of the reference SI randMRI data can be displayed. Shown here are selected planes of the 3D total 3’P SI data in human brain (left), and of the multiplane scout MRI (right). The separation between SI slices correspo ‘nds to thre e MRI slices.

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and copying (saving) the plot data to a new save-plot window where several selected spectra may be viewed together. A summed-plot option enables spectra to be summed from any selection of voxels in three spatial dimensions. The selected regions are outlined on the reference image window. An example of the sum-plot feature is shown in Fig. 3, together with a copy of the save-plot window which has been output to the laser printer. The data shown are from a ‘H SI study of normal rat brain.9 The regions corresponding to each of the summed plots are shown on the reference image along with numbers indicating the corresponding plot. A record of the region from which the resultant spectrum was obtained, can be made to the printer or to a file. This information is required for documentation, as wel1 as for comparing data from the same region in sequentially acquired data sets and for calculating volumes for quantitative measurements. Muitiple spectra may also be displayed in a stackplot format, showing data selected from a line or column through the SI data set. This display format provides a convenient means of viewing spatial variations of al1 metabolite resonances. Figure 4 shows a stackplot of ‘H spectra taken for al1 points along a line across the head of a rat, passing through the brain, from the same data set shown in Fig. 3. Stackplot options also include disabling the hidden line feature, providing a horizontal stackplot (as distinguished from the perspective view shown in Fig. 4), and altering the viewpoint and staling factor used. The line or column from which the stackplot is made is selected from the reference window, and marked onto that image. By redirecting the function of the mouse selection in this window only, this stil1 allows for selection of single voxel spectra using the other two primary image windows. Spectroscopic Image Display To create a spectroscopic image, spectra are integrated over an operator-defined spectral region for al1 points in the displayed slice. The spectra1 region is defined by selecting the starting and ending positions from the main plot window, using the mouse to drag either of two cursors displayed there. Each selection causes the corresponding spectra1 image to be immediately calculated and displayed in the spectroscopic image window. Options available for the generation of this image include: taking magnitude or real data, with phase correction applied if enabled; subtraction of a baseline determined from the average of the edges of the selected region; and fixing the image staling factor. The baseline subtraction feature allows improved image formation of narrow resonances which are su-

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perimposed on a broad resonance, for example in the detection of lactate in the presence of broad lipid resonances. The formation of spectroscopic images from closely separated resonances in the presence of field inhomogeneity may lead to signal overlap, and in this situation it is necessary to correct for the effects of the field inhomogeneity. This may be performed by using the spectra1 alignment option, which may be called from within the data processing menu of the SID program. This feature is described in a later section. The data displayed in the main spectroscopic image window may be saved to a new image window, referred to as a copy-window. As each new window is created it is initially identified in the window title with an image number, and the frequency limits used for its creation. This title can, however, easily be changed by the operator. Once the spectroscopic image data have been moved to a new window, additional operations are available, including image arithmetic operations and saving the data to a disk file. A typical analysis procedure involves generation of spectroscopic images from al1 observed resonances, with subsequent generation of processed images, for example, addition of multiple resonances of a single metabolite, or generation of metabolite ratio images. Image Arithmetic Operations Several arithmetic operations may be performed on the image data which have been moved to a copy-window. These include add, subtract , multiply and divide, of multiple image data sets, as wel1as scalar operations, logarithm or square root of a single image data set. Combined arithmetic operations may be used, for example, 1/( 1 + 2) wil1divide the image data in copy-window 1, by the sum of the images in windows 1 and 2. Additional image processing operations are also available for generating frequency shift images, for example, to obtain pH or BO field maps. For this purpose a Differente function is available which creates an image of the frequency differente of a peak position from either a reference peak or a fixed frequency position. Before using this operation, two spectra1 regions must first be selected by generating the corresponding spectral images. For generating a pH map with 3’P SI, these regions wil1 typically correspond to images from the Pi and PCr resonances. For a BO field map, one image wil1 be derived from a spectra1 region which covers the resonance whose position is to be mapped, and the reference wil1 be a single frequency position. The differente image data wil1 include values determined from the noise in image regions outside of the object or where there is insufficient signal to map. To improve the visual display, these data may be

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Fig. 3. Example of the summed region plot, and comparison of multiple spectra using the plot output of the save-plot w indow. 1:he MRI scout image (A) and the reference image derived from the NAA and lipid region (B) are shown for a ‘H SI study crf rat brain obtained at TE = 136 msec. Spectra have,been summed from al1 voxels for each of the three regions mark :ed on the reference image. The corresponding magnitude spectra from each region is then shown in (C). (Figure continued Otl facing Page. 1

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masked using a threshold operation based on the image intensity of any displayed image. With this operation, the target image data are set to zero when the intensity of the mask image falls below a given threshold value. For pH maps, the total 3’P image displayed in the reference image is typically used as the mask. Figure 5 illustrates the use of the Differente and Threshold operations. Figure 5A shows an image derived from the NAA resonance of a ‘H SI acquisition

Fig. 4. Stackplot of ‘H SI spectra in the rat head, for al1 voxels along a horizontal line passing through the region of the brain, from the same data as shown in Fig. 3.

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in a human head. The NAA distribution is seen only over the PRESS selected volume 7~15and the outline of the head is indicated by the white overlay. The signal intensities at the bottom of the NAA image are due to remaining lipid contamination from the scalp region. Figure 5B shows the result of the Differente operation, which shows at each point in space the relative frequency offset of the maximum spectra1 data point over the frequency range used to generate the NAA image. This was chosen to cover the full range of al1 NAA resonance frequenties and is 0.488 ppm. This corresponds to the full range of the color scale used, which has been further windowed, as shown on the lookup-table window. Figure 5B includes spurious data from regions where the NAA signal intensity is below the noise level, which is removed by using the threshold operation as shown in Fig. 5C, where for this example the same NAA SI data of Fig. 5A were used as the mask, with a 20% threshold value. Finally, in Fig. 5D, is shown the field map after correcting for BO shifts, using the procedure described in a later section. While some noise is present on this image, the field variation can be seen to have been significantly reduced. Image Profile Plots A 1D plot of a section taken through an image of a metabolite distribution provides a convenient method of identifying regional intensity variations. A profileplot option opens a new plot window and allows a profile to be obtained from across either the spectroscopic image or the reference image data. This is generated in any arbitrary direction by using the mouse to choose the starting and ending points on the corresponding image window. Multiple plot windows may be opened, which provides a convenient method for comparison of multiple data sets. Scout Image Display Options SI data are typically acquired with a reduced spatial resolution in comparison with that available using ‘H MRI. Visualization of the SI data can therefore be substantially improved by inclusion of a ‘H MR image, acquired from the same region as the SI data, which we refer to as the scout image. By using knowledge of the spatial parameters from each image data set (FOV, offset and inter-slice separation) the scout image may be used to select the corresponding slice of interest for multislice SI data, and regions may be identified on the scout image from which spectral data are to be viewed. The slice separation and offset of the MRI and SI studies need not necessarily correspond. For example, in our 31P SI studies a thinner MRI slice

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Fig. 5. An example of the operation of the Differente and Threshold operations for generation of a BO field map. In (Pi) is shown the spectroscopic image generated from the NAA resonance over a 2-cm thick axial section in a human head, toget .her with a white outline showing the edge of the head. Using the frequency range used in the generation of the NAA image, the Differcante operation maps the frequency offset of the peak signal at each point in space (B). This image includes noise fr‘om points outside of the head, which can be set to zero (C) by using the image data of (A) as a mask. For this BO field mal 3, a freque ncy range of 0.488 ppm corresponds to the full image scale, which has been further windowed for display as shown I on the collor lookup table. (D) shows the BO field map after correction of BO Shifts.

provides three scout images for each SI slice. Two options are available in this situation. Firstly, a single MRI may be displayed which most closely corresponds to the center of the SI slice. The displayed scout plane may then be incremented or decremented using mouse buttons two and three in the scout image window. The second option is to display al1 scout slices which correspond to the SI slice, together with a new scout image which is obtained from the sum of al1 of the MRI slices which correspond to the thicker SI slice. This mode better enables the effects of partial volume to be evaluated, for example, in the region of the ventricles which may be largely encompassed within one SI slice, though may only be partially viewed within a single thin-section MRI slice. This display mode is shown in Fig. 6, for a 31P SI study from which a single slice, corresponding to 2.5 cm FWHM slice thickselection

ness, is shown in Fig. 6A. The summed scout is shown in Fig. 6B, which is obtained from three images, each of 6-mm slice thickness, shown in Fig. 6C. The summed MRI shows a different appearance of the ventricles, and display of this type of data frequently improves interpretation of the SI data. A direct visual correlation of the MRI and SI images is frequently difficult due to differences in spatial resolution and information content of the two image types. In addition, it is not always necessary, and frequently undesirable, for the SI image data to have the same FOV as the scout, and for studies such as human ‘H SI it is frequently necessary to incorporate some volume preselection in order to avoid inclusion of excessive lipid signals . l5 To provide for improved visualization of the SI data and a direct correlation of scout and SI features, several methods are available

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Fig. 6. The display of multiple MRI slices, which cover a distance corresponding to a single SI slice, aids in the analysis of SI data. The total 31Pimage in the human head (A) corresponds to a FWHM slice thickness of 2.5 cm. The MRI which corresponds to a similar slice thickness (B) is obtained by summing three MRI images (C) which are obtained with a slice thickness of 6 mm and slice to slice separation of 7.2 mm.

for overlaying spatial information derived from the MRI onto the spectroscopic image. One technique which makes use of image overlays for comparison of MRI and SI has been used in Fig. 5, namely using a simple outline obtained by detecting the outer edge of the scout image data. The image intensity value at which the outline is drawn is automatically determined to be three times greater than the

background noise value. Figure 7 shows examples of additional overlay techniques, again using the NAA image of the same ‘H SI brain study. In Fig. 7A the MRI (TE = 90 msec) is shown with a red overlay box, which represents the extent of the SI FOV and a blue box which shows the selected volume used for the SI study. In Fig. 7B, the MRI (TE = 30 msec) is shown with a contour overlay generated from the NAA im-

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(D) Fig. 7. SID provides several methods for correlating the anatomical information available from the higher resolution scout MRI and the SI data. The following examples are shown for a ‘H SI study of human brain; (A) the MRI scout image (TE = 90 msec) is shown with overlays showing the extent of the FOV (in red) and the selected volume (in blue) used for the SI study; (B) the MRI (TE = 30 msec) with a contour plot generated from the NAA image, drawn at 15% and 45% of maximum; (C) the NAA image with an overlay showing the contour plot from the MRI (TE = 80 msec) at levels of 30070,50% and 70%; and (D) the NAA image is shown with an overlay derived from a high pass filtered version of the scout. This study was obtained from an elderly patient demonstrating periventricular white matter signal intensity, which is most clearly identified on the TE = 30 msec MRI.

age, at 15 and 45% levels. Contour plots may be de-

rived from, and overlaid onto, any displayed image, and in Fig. 7C is shown a contour plot obtained from the MRI data, at 30,50, and 70% levels, superimposed

on the NAA image. Finally, Fig. 7D shows an image overlay generated from a high-pass filtered version of the scout. A Sobel filter16 was first applied to the scout data and the overlay image point then generated

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when the filtered data crossed over an operator-defined threshold value. Adjustment of threshold values allows the overlay data to include different image structures. The overlay method using edge detection is the most robust in the presence of sensitivity-related image intensity variations, for example, with MRI data obtained using surface coil acquisition. For many of our volume coil SI acquisitions, it is found that the simple MRI edge-detected overlay provides sufficient aid in registering the MRI and SI data, without obsturing details which can occur using the contour or high-pass filter overlays. Another method for MRI/SI correlation is direct superposition of the image data, which is discussed in the following section. Alternative Lookup Table Features The use of color for display of image overlays, such as shown in Fig. 7, provides an excellent means of visually distinguishing the different information. SID also provides for several color image lookup tables. The use of color for display of metabolite images should be considered with the same preferences and precautions as other biomedical image display applications; however, display of spectroscopic images provides additional examples where the use of multiple image lookup tables and of color displays may be used to advantage. One use of color is for the display of data having a wide dynamic range. An example of this is for the simultaneous display of multiple metabolite images which have widely differing image intensity, though need to be displayed with the same staling factor to allow comparison between images. There is a similar requirement for the display of MRI and SI data, though since these two image types are of differing contrast and signal-to-noise ratio (S/N), their combined display is better achieved using independent lookup tables for each image type. In SID, a dual-lookup table mode may be enabled with independent leve1 and width controls for each of two groups of image windows, and either color or greyscale tables can be enabled for each. Once enabled, any image window can be attached to either lookup table at any time. This feature is demonstrated in Fig. 5, where the NAA image is shown with a greyscale, and the field map with a color scale. An additional feature of SI display is the requirement for simultaneous display of multi-parametric information, for example as multiple metabolite images displayed together, or as a metabolite image combined with an MRI, for better visualization of metabolic and anatomie information. SID includes a dual-parameter display option for combined display of two independent parameters. Due to limitation of our image dis-

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play hardware which consists of a single 8-bit lookup table (256 levels), for completely independent lookup tables for display of dual-parameter information the number of possible levels is the square root of the number provided by the hardware, that is, a maximum of 16. However, this must be further reduced, because a number of table entries are reserved for display of window background or overlay colors, and in addition it is desirable to leave the main image windows displayed with a separate lookup table. SID provides some choice for allocating the number of lookup-table entries to each image group which allows for more intensity levels to be assigned for whichever display may require it. Typically the MRI data benefit most from a greater number of lookup-table entries due to their greater signal-to-noise ratio and resolution. To generate the dual parameter display with an 8-bit display system, the two data sets must be combined to use a single lookup table in the following manner. Labeling the two image data sets A and B, with each being displayed with a number of table entries NA and NB respectively, then each image data set is first scaled according to the leve1 and width values used for that image, in the normal mamter, to give the data sets “image_A” and ‘image_B,” which are valued between 0 to NA and 0 to NB. The resultant displayed data are then created as: displayed-image = image_A x NB + image_B. The image lookup table is created in a similar manner. Since the number of table entries is small, typically 15, to maintain image fidelity with reduced width values of the lookup table the image data are kept in memory, and the staling and combining operation performed each time the lookup table leve1 or width values are changed. The lookup table remains unchanged. This differs from the more customary, and faster, method of windowing where the image data remain unchanged and only the lookup table values are changed. i6 Figure 8 shows examples of dual parameter display. Figure 8A shows the ‘H SI NAA image shown in Fig. 7, now displayed using a red color table and combined with the T2-weighted MRI data (TE = 90 msec) which are displayed with a green lookup table. Here the extent of the selected volume of the SI study and the diminished NAA in the ventricles are clearly identified, while the presence of NAA in the periventricular white-matter lesions, better seen on the TE = 30 msec MRI shown in Fig. 7B, can also be seen. Figure 8B demonstrates the use of the dual parameter display to simultaneously show the distributions of two metabolites, namely NAA, shown in green, and lactate, shown in red. These data are obtained in rat bram, at 24 hr following permanent focal ischemia. ” The orientation of the image is the same as that shown in

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Fig. 8. Examples of the dual parameter display mode. (A) The NAA distribution in human brain, shown in red, combined with the corresponding scout MRI, shown in green, for the same image data as shown in Fig. 7. (B) Distributions of NAA in green, and Lactate in red, in rat brain at 24 hr following permanent cerebral ischemia. Yellow indicates the presence of both lactate and NAA in that region. The image orientation is as shown in the MRI of Fig. 3, and the outline of the head obtained from the MRI is indicated by the red overlay.

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the MRI of Fig. 3A. The different tissue regions can clearly be identified; the normal tissue region appears green because the lactate concentration is very small, the stroke region shows as red due to the high levels of lactate and loss of NAA, and the border region ap-

pears as yellow indicating the presence of high levels of lactate together with NAA. For combined display of two parameters, it is essential to use two independent colors such as red and green, and not, for example, one image displayed in

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color and the second as a greyscale image. In this latter case, for example with a red and greyscale combination, the second lookup table also contains the red color used for the first lookup table and the resultant image intensities are no longer independent. Spectra1 Alignment The formation of spectra1 images by integration between fixed frequency limits encounters errors in the presence of BO field inhomogeneity. This occurs when any part of the resonance line moves out of the selected region, resulting in reduced integrated signal intensity. These errors may be reduced by correcting for BO field shifts prior to formation of spectra1 images. 14*”A BO correction procedure similar to that previously described l4 has been implemented as a processing option of the SID software. This operates on the reconstructed frequency domain data in the following manner: A reference spectrum is selected and a spectra1 region identified which contains one or more characteristic resonances from which the BO field map can be generated. A summed region spectrum may also be used for improved SIN. ‘The spectra1 region may be some clearly identified resonance, such as NAA for ‘H brain studies or the full spectrum for 31P SI. It is advisable to avoid regions such as lipid resonances or residual water signal. The frequency shift at each location is determined by cross-correlation of the reference spectrum with the spectrum from

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that location, using the data from the selected spectral region only. A first pass BO field map is then generated. At the locations where the SI signal intensity falls below a threshold value, the field shift is initially left undetermined. A second pass through the field map data is then carried out to interpolate between neighboring determined points, to fill in holes in the data, and to extend the outermost determined values out to the edges of the data set. Finally these data are then smoothed. The resultant data then represent the BO field distribution in two or three dimensions, and this is then used to correct for the frequency shift for al1 spectra in the SI data set. The effect of the spectra1 alignment is shown in Fig. 9, for the ‘H brain study also shown in Fig. 5, with a stackplot of spectra running from top to bottorn through the image. In the uncorrected data of Fig. 9A, the most prominent resonance seen in the center region is that of NAA which can be seen to have a significant field shift over the object. These data, following BO shift correction, shown in Fig. 9B, show complete alignment of al1 resonances. In Figs. 9C and D are shown spectra obtained by integration over al1 voxels selected for the stackplot data. The BO-corrected summed data clearly show improved lineshape and a 50% increase in peak signal intensity over the original data. While this method of BO correction is relatively easy to implement and can be performed on almost any SI

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Fig. 9. Effect of the spectra1 alignment BOfield correction procedure. (A) Stackplot of spectra from al1 voxels along a column through the ‘H brain SI study, also shown in Figs. 5 and 7, showing severe spatial variation of the resonance frequencies. (B) The same data following spectra1 alignment. (C) and (D) The sum of al1 spectra from (A) and (B) respectively.

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data without requiring any additional measurement of the field distribution, Fig. 9 also demonstrates limitations of this method. Firstly, it does not correct for local, that is, intravoxel, field distortions, or for spectral distortions caused by eddy current effects. In Fig. 9B, broader NAA resonances can be seen at the bottom (e.g., line number 10). Secondly, the algorithm is unable to differentiate strongly overlapping resonances. The broad resonances seen at lines 3 and 4 are undoubtedly residual lipid resonances that have been considerably shifted so as to appear in the region of the NAA position. Improved correction may be performed using time domain deconvolution using a known reference signal. 18,19 Additional Features A new SI or scout data set may be opened at any time. This allows, for example, direct on-screen comparison and photography of different image data sets and for arithmetic operations to be carried out using different data sets. The displayed image and plot data may be saved in a variety of formats, including binary, HPGL (Hewlett Packard), Postscript (Adobe Systems, Inc.), ASCII and TIFF (Aldus Corporation) formats which allow for data to be read by a number of commercially available PC and workstation software packages. Saving of individual spectra in binary data formats also allows for additional spectra1 analysis routines to be used. Photography of images displayed in a windowing environment is most conveniently carried out using a film recorder, rather than a video camera, which uses the full display of the computer display terminal. For photographic output from SID we have developed software to drive a film recorder (Lasergraphics, Irvine CA), to produce a photograph directly from the image data or from a screen dump of either an individual window or of any operator-defined region of the screen. Output of image and plot data is also provided to a laser printer. Finally, SID also offers a number of options designed primarily for data presentation and photography. These include such features as changing window titles, adding text to the windows, and changing the colors of image overlays, for example to use white only for when doing black and white photography. DISCUSSION

While spectroscopic imaging techniques continue to evolve, the current experience clearly demonstrates the need for a flexible software package which is specifically designed for processing, display and analysis of multidimensional SI data. For clinical applications, the

ultimate goal for SI data processing techniques is the automatie formation and quantitation of metabolite distributions, with the resultant image intensity scale directly calibrated as a molar concentration of the MRobservable signal contribution. While this goal has a number of practica1 difficulties, initial studies are encouraging.* Our experience has found that the SID software provides a powerful and flexible tool for examination of clinical SI results by research investigators, while providing a platform for extending this work towards generating metabolite images in a format more suited for use by a physician. Our software package has been developed to rapidly review large data sets on a workstation platform of moderate price and performance. In part, this has been achieved by choosing a software design which accommodates a specific data format for the spectra1 and spatial dimensions. This data organization provides for rapid access to large sections of 3D data, where each section contains al1 spectra for one spatial plane of data, and is a subset of the full 4D data set. To view other spatial orientations, the data reorganization is performed prior to viewing with SID. While a more flexible approach could allow for other data organization formats and arbitrary viewing angles, this would be obtained at the tost of further programming complexity and reduced performance. Experience with using the SID software for analysis of clinical spectroscopic data has demonstrated a number of important requirements. First, the selection of program functions, and the spatial and spectral regions using the mouse and the rapid response are important. This allows a convenient “interactive” operation to quickly review a large number of spectra and metabolite images. Second, the combined display of MRI data and correlation of the MRI and SI spatial information are important. While this is largely necessitated by the relatively low spatial resolution currently obtained with SI, the MRI data also provide complementary information, essential to the review of al1 clinical MR studies. Third, multiple display options and combined spectra1 and image display are essential for this type of data. For example, stacked-plot displays are frequently displayed together with spectroscopic images to provide different perspectives of the same information, and SI data from different nuclei or study types may benefit from display by different methods. Finally, the ability to save the spectra and images which are generated in a variety of different formats, allows for 1) a much reduced amount of data to be saved, and 2) other display and analysis packages to be used for more specialized applications, for example, as presentation graphics or statistical analysis programs .

Spectroscopic imaging display 0 A.A.

Acknowledgment-This work was supported by PHS grants CA48815 and DK33293, the Department of Veterans Affairs Medical Research Service, the University of California Radiology Research and Education Foundation and Philips Medical Systems. Al1 spectroscopic imaging data shown were acquired at the MRS Unit of the DVA Medical Center, by Drs. J. Duijn, E.J. Fernandez, T. Higuchi, J. W. Hugg, R. Lara, G.B. Matson, D. J. Meyerhoff, and D.B. Twieg, and their permission for use of these data is greatly appreciated. We also acknowledge their many comments and suggestions which always leaves much more to be done.

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