Pediatr Radiol (2014) 44 (Suppl 3):S431–S439 DOI 10.1007/s00247-014-3102-1

IMAGE GENTLY ALARA CT SUMMIT: HOW TO USE NEW CT TECHNOLOGIES FOR CHILDREN

Iterative reconstruction: how it works, how to apply it James Anthony Seibert

Received: 23 April 2014 / Accepted: 19 June 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract Computed tomography acquires X-ray projection data from multiple angles though an object to generate a tomographic rendition of its attenuation characteristics. Filtered back projection is a fast, closed analytical solution to the reconstruction process, whereby all projections are equally weighted, but is prone to deliver inadequate image quality when the dose levels are reduced. Iterative reconstruction is an algorithmic method that uses statistical and geometric models to variably weight the image data in a process that can be solved iteratively to independently reduce noise and preserve resolution and image quality. Applications of this technology in a clinical setting can result in lower dose on the order of 20–40% compared to a standard filtered back projection reconstruction for most exams. A carefully planned implementation strategy and methodological approach is necessary to achieve the goals of lower dose with uncompromised image quality. Keywords Statistical iterative reconstruction . Filtered back projection reconstruction . CT reconstruction algorithms . Image quality . Pediatric . Computed tomography

represent about 12% of medical imaging exams overall, CT accounts for about two-thirds of the radiation dose attributed to all medical ionizing radiation procedures [1]. In addition, the occurrence of many radiation overexposure incidents in 2009 and 2010 [2] coupled with epidemiological studies indicating an increase in cancer risk caused by CT examinations [3] have generated much attention to the cause of lowering CT dose. This is a particularly important endeavor for the pediatric population, because children’s enhanced radiation sensitivity and longer expected lifespan result in a larger risk for a given amount of radiation dose [4]. Thus, there has been a great emphasis in the last several years on software and hardware enhancements coupled with CT protocol adjustments to lower radiation dose. Notable are tube current modulation, kV modulation, prospective gating, and mechanical collimation overbeaming prevention [5], as well as iterative statistical reconstruction methods [5–17] that have contributed to achieving a significantly lower radiation dose without a major sacrifice of image quality. This paper describes iterative reconstruction in terms of how it works and how it can be applied in a clinical CT setting.

Introduction The technological advances of CT have led to an explosion of the number of CT exams performed each year in the United States because of the speed, availability and great diagnostic capabilities of modern CT scanners. Although CT procedures J. A. Seibert (*) Department of Radiology, University of California Davis Medical Center, 4860 Y St., Ste. 3100, Sacramento, CA 95817, USA e-mail: [email protected]

Data acquisition All CT scanners acquire projection data from a collimated Xray source and detector array mounted on a gantry that rotates around the patient [5]. A uniform beam of incident X-rays are modulated by the attenuation characteristics of the anatomy and are absorbed by a large number of discrete detectors to generate a projection at each angular position of the X-ray tube. The resultant data are stored in a two-dimensional sinogram array in which each line represents an angular position (Fig. 1).

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Fig. 1 CT data acquisition and reconstruction of a tomographic image. a Simplified schema of the data acquisition process produces one-dimensional X-ray projections representing integrated X-ray attenuation of the object at different angle positions (top); more complete projection sampling and resultant sinogram (repository of the forward projection data) are also illustrated (bottom). b Image reconstruction proceeds with back projection of the data creating an image-space tomographic representation of the object (top); the simple back projection results in a blurring caused by convolution of the projections, while filtered back projection results in a more accurate estimate by implementation of a specific mathematical filter to remove the blurring through a deconvolution process

Back projection reconstruction Back projection is a method to generate a tomographic image by projecting each line in the sinogram across a computer matrix with equal weighting of the data (Fig. 1). The back projection process of summing and normalizing the contributions over all angles generates a tomographic image with significant contributions to the center of the image, diminishing with radius, r, as a 1/r falloff. A blurring and imperfect reproduction of the objects in the reconstructed image occurs. Filtered back projection (FBP) overcomes the intrinsic blurring by using a deconvolution process to remove the 1/r blurring with mathematical functions known as filter kernels. The result is an improved rendering of the image, with image noise and detail determined by the X-ray dose (statistical variation) and the weighting characteristics of the filter kernel. Each manufacturer provides a set of filter kernels that smooth noise to improve contrast-to-noise of large objects, emphasize mid-size objects, or enhance high-contrast detail of the anatomy, with a variety of manufacturer-dependent descriptors such as “standard,” “lung,” “bone” and “detail.” Figure 2 illustrates images reconstructed with a soft-tissue filter kernel and detail filter kernel. Filtered back projection is a closed analytical solution to the reconstruction task, characterized by efficient, high-speed reconstruction; however, there is no opportunity to independently reduce the noise and maintain the detail in the reconstructed image at the same time. Therefore, attempts at reducing CT dose by lowering tube current (mA) result in noisy sinogram data, which can be

partially ameliorated by using a smoothing filter kernel to reduce statistical noise in the reconstruction, but with the loss of image detail. If image detail is to be preserved, then a detail filter can be used, but the reconstructed images are excessively noisy and contrast-to-noise ratios are decreased.

Iterative reconstruction Reconstruction algorithms have recently been developed by CT manufacturers with multiple evaluations and comparisons of the original FBP reconstruction with adaptable non-linear processing to independently refine the noise and detail characteristics of the processed tomographic image in an iterative process. Iterative reconstruction (IR) can be categorized into three distinct methods based on input from: (1) image space statistics, (2) projection space (sinogram) statistics through forward and back projection comparisons; and (3) projection and system-model space characterization that includes, in addition to the statistics, image degradations caused by focal spot, detector array sampling, and system geometry among several system model inputs. Each method has advantages and disadvantages for clinical implementation. Iterative reconstruction in image space is the fastest but least flexible method of adaptive noise filtering. No raw data generated by the CT scanner are used, so third-party software vendors can provide solutions to iterative methods applied to low-dose, high-noise FBP reconstructed images. Typically this process decomposes an image into multiple frequency

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Fig. 2 Filtered back projection using soft-tissue (left column) and detail (right column) kernels is shown for a high-contrastresolution image from the CT accreditation phantom (top row) and images of a patient head scan with wide window width (middle row) and a narrow window width (bottom row). Clearly visualized is the improved contrast resolution of the soft-tissue kernel with lower spatial resolution, and higher spatial resolution of the detail kernel with lower contrast resolution and higher noise

ranges through Fourier transformation in order to separate the low-frequency (large area) from the mid-frequency (medium size) and high-frequency (detail) contributions. Variable smoothing and weighting of these frequency bands allows selective signal processing to reduce noise in the large-area Fig. 3 Image-space filtering process depicts a method using Fourier Transform (FT) and frequency domain decomposition to selectively reduce noise in slowly varying areas of the image yet maintain resolution in detail areas

image regions without reducing the high-frequency detail. Non-linear data processing that maintains detail yet reduces noise of the image segments is performed in the recomposition of the noise-filtered image. A schematic of the process is shown in Fig. 3. Methods such as “steering kernel

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Fig. 4 Back projection of sinogram data at specific angles (left) is used to create the tomographic image. The reconstructed image can be used to synthesize forward projection data (right), which can be “regularized” and ultimately used to create another reconstructed image (an iteration). Printed with permission [5]

regression” [18] have also been implemented. A downside to this method is that streaks in the low-dose FBP image appear like edges because of the highly correlated noise. Iterative reconstruction in projection space uses the FBP image as the starting point to generate synthesized forward projection data (essentially simulates the CT acquisition process) with comparison to the measured projection data. The forward- and back-projection process is illustrated in Fig. 4. For a perfect reconstruction, there would be no difference between the synthesized and measured data, but because of the approximate nature of the FBP process and statistical variations, a deviation exists. The amount of deviation

subsequently derives correction projections using non-linear processing called “regularization” to stabilize and maintain spatial resolution while at the same time minimizing the statistical variation essential to achieve noise reduction [13]. The corrected projections are then used to reconstruct an update candidate image to compare with the current image. This loop proceeds until there is little difference (typically at a pre-defined limit) between the candidate image and the current image. Thus with appropriate processing, the excessive statistical noise caused by low dose can be reduced in the iterative process with minimal loss of spatial resolution, as illustrated in Fig. 5.

Fig. 5 The projection-space iterative process requires several steps for convergence. From left to right: the current reconstruction creates forward projection data from all angles (one angle at 0 degrees is shown below each image); the projection data are statistically analyzed and regularized

and then back-projected to create the update candidate reconstruction. The iterative process repeats until an acceptable convergence is achieved or a predetermined number of iterations is performed. On the right is an ideal noiseless image and corresponding projection

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A block diagram of the process is illustrated in Fig. 6. By itself, the output IR image typically presents with a correlated noise image appearance that is unacceptable to a radiologist interpreting the images, described as “plastic,” “blotchy,” “cartoon-like” and “impressionistic,” among a host of names describing an undesirable outcome. Fortunately, the projection space iterative reconstruction method has the ability to blend a fraction of iterative reconstruction output with the original FBP image, to produce an image with acceptable noise characteristics. More on this issue is described in the clinical implementation section. Iterative reconstruction in projection space with modelbased inputs represents the next level of complexity to include models of the acquisition geometry, focal spot effects, X-ray beam spectrum, X-ray scatter, detector response and detector sampling, among other characteristics of the CT acquisition system [6, 15], as shown in Fig. 7. These model-based inputs consider imperfect acquisition conditions and artifacts to modify the synthesized projections more accurately, in addition to the statistical processing model. Because of the number of input model conditions, image reconstruction times for a CT dataset with currently deployed computer hardware are extremely long, often on the order of 45 min to an hour, certainly incompatible with rapid turnaround for typical clinical applications. Nevertheless the results are very compelling with respect to image quality for very-low-dose CT imaging, when compared to statisticalbased iterative reconstruction. As technical advances provide increased computer processing speeds, model-based methods will have mainstream applications.

Fig. 6 The projection space iterative reconstruction flow diagram. The initial (noisy) filtered back projection (measured projection) data undergo comparisons with a statistics model that incrementally modifies the projection data and reconstructs the first iteration (“candidate image”). A comparison is performed between the candidate image and the image from which the projection data were obtained to determine whether the process should be repeated based upon a pre-determined threshold level of convergence. With no convergence, the candidate image becomes the current estimate, synthesized forward projections are created, the statistical model is applied, and the process repeats. The iterations stop when the threshold levels are attained or when a preset number of iterations has occurred

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Fig. 7 Inputs and data flow for a model-based iterative reconstruction algorithm. In this algorithm, there are several inputs from the X-ray characteristics (e.g., focal spot), the image data (e.g., anatomy, geometry), and the detector (e.g., sampling), collectively defined as system optics. Instead of assuming an ideal model, each of these inputs refines the synthesized projection data prior to statistical model manipulation, after which the candidate image is reconstructed. A comparison of the candidate image with the current estimate is performed to determine convergence. With no convergence, the candidate becomes the current estimate, and the process repeats with incremental refinement of the model-based system optics and statistics-based comparisons, until convergence is achieved or a preset number of iterations has occurred

Clinical implementation of iterative reconstruction software Implementation of iterative reconstruction capabilities requires a team approach, with participants at a minimum including the vendor applications specialist, senior CT technologist, radiologist, and if at all possible a medical physicist. An initial validation of the iterative reconstruction software should be performed with phantoms (CT accreditation phantom, anthropomorphic phantom) using established acquisition techniques for routine procedures (e.g., head and abdomen), with subsequent imaging using progressively less mAs. At a minimum, phantom examinations should be performed with 0%, 20% and 40% less mAs, and reconstructed with standard (soft tissue) and detail (bone) filter kernels using FBP and IR algorithms. For projection-space IR algorithms providing a blending capability, the noisy FBP image should be composited with the noise-suppressed IR image in percentages or levels, depending on the manufacturer. For percentage-based methods, increments of 10% up to 100% are used, where 0% is full FBP and 100% is full IR. For level-based methods, each level approximates to 20% blending, in which level 1 represents a blend of ~80% FBP and ~20% IR and level 5 is 100% IR. Qualitative and quantitative evaluations of phantom images are helpful to demonstrate image appearance and quantitative integrity (Hounsfield units [HU]) of the IR processing. Figure 8 illustrates images acquired from the American College of Radiology CT accreditation phantom comparing FBP with IR images, demonstrating a lower CT dose index volume (CTDI vol ) for the IR images, distinct differences in noise texture, similar HU values, lower noise standard deviation and higher contrast-to-noise ratio for the iterative reconstruction.

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Fig. 8 Reconstructed images are shown for the American College of Radiology CT accreditation phantom low-contrast module. Left: A conventional adult head acquisition technique delivering a volume CT dose index (CTDIvol) of 78.4 mGy and a contrast-to-noise ratio of 1.05 for the large low-contrast object. Right: An iterative reconstruction adult head acquisition technique with lower mAs resulting in a CTDIvol of 52 mGy and a contrast-to-noise ratio of 1.59. In both examples, the average HU

numbers in the object and background are essentially the same, with only the standard deviations lower in the iterative reconstruction image. Note the correlated noise appearance of the iterative reconstruction image (a level 3 blend and a low-pass filter were used). The choice of IR blending level and reconstruction filter kernel with higher-frequency response can partially ameliorate the correlated appearance, at the expense of greater noise. HU Hounsfield unit, IR iterative reconstruction

We have found a reasonable starting point to be the manufacturer-recommended default values regarding mAs reduction, reconstruction filter kernels, detector configuration, automatic exposure control settings (e.g., noise index or effective reference mAs) and amount of blending. Methods to produce and display sets of various image blends are typically available on modern CT systems, enabling the radiologist to appreciate the differences in noise and image quality for a given patient scan at a given radiation dose. Generally the amount of blending is dependent on how much the acquisition techniques are lowered; we have empirically determined that a 20–50% or level 1–3 blending provides acceptable image quality for both adult and pediatric body imaging applications for standard slice thickness (e.g., 5 mm, 3.75 mm) reconstructions, similar to other published studies [11, 16, 17] with a dose reduction about equal to the amount of blending percentage used. For thin-slice (0.6–1.25 mm) reconstructions chiefly used for multi-planar reformatting, we have found that a higher level of IR blending (up to 90% or level 4–5) beneficially reduces noise for coronal and sagittal reformatting.

Figure 9 illustrates a pediatric abdomen reduced-dose protocol showing the original FBP images and various levels of IR image blending for standard and thin-slice reconstructions. Our initial approach to implementation of IR, incrementally reducing dose, was off-base, because suboptimal performance of IR algorithms occurred when they were applied to reconstructions where the image noise was already low, resulting in unrealistic appearance — either too smooth or blotchy — even at the lowest blending level. This was particularly important for iterative reconstruction methods without blending (e.g., image-space algorithms), in which the lack of flexibility made implementation difficult if not possible (we are still trying to optimize or abandon this approach on a single scanner at the University of California Davis Medical Center). Poor image quality secondary to waxy or cartoon-like appearance can totally defeat the reason for having IR software because radiologists at the beginning of the process develop a poor impression of the technology and demand that it not be used.

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Fig. 9 Example of a low-dose CT scan comparing an original filtered back projection image and various percentages of IR blending for a 3.75mm slice thickness (top row) and 1.25-mm slice thickness (bottom row). Technique factors of 100 kV, automatic exposure control (tube current modulation), and noise index of 14 were used for the acquisition, resulting in a CTDIvol of 2.1 mGy. The University of California Davis Medical Center pediatric abdomen protocols currently use a 30% blend of

IR (second image top row) for 3.75-mm reconstructed slices, and 90% blend of IR (fourth image bottom row) for 1.25-mm reconstructed slices used for coronal and sagittal reformatting. AEC automatic exposure control, ASiR adaptive statistical iterative reconstruction, CTDIvol CT dose index volume, DLP dose length product, FBP filtered back projection, IR iterative reconstruction

UC Davis CT protocols

reconstruction techniques have made a significant impact on the ability to reduce dose; our simultaneous involvement in reporting dose metrics as required by the State of California has led to the implementation of dose monitoring software and demonstration of the impact of IR on our patient population in comparing radiation dose on CT scanners utilizing IR to those without (Fig. 11). We have successfully used this information to justify the purchase of IR software on our other scanners from a different manufacturer, which are now achieving comparable low doses.

After 2 years of use at UC Davis Medical Center, we have adopted a low-dose strategy for CT examinations using automatic exposure control (AEC) (see the companion article by Cody elsewhere in this issue) in conjunction with IR noise reduction. A majority of pediatric patients are scanned on two scanners with identical capabilities. Body imaging protocols are weight-based in terms of determining a noise index that determines tube current settings based on patient attenuation estimates from the localizer, in addition to the set percentage of IR blend, as shown in Fig. 10. This has resulted in consistent patient dose reduction of 20–40% compared to non-AEC, non-IR CT procedures for the chest, abdomen and pelvis. For pediatric head procedures, the ability to lower radiation dose is less, and a 10–20% blend of IR with the standard FBP images is common. Virtually all pediatric and adult protocols employ AEC with IR. Iterative

Technological advances are pushing CT technique factors and dose even lower Improvements in modeling the overall CT process beyond statistical methods have been successfully achieved. Inclusion of a priori model-based system information can

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Fig. 10 UC Davis Medical Center’s pediatric protocols for CT abdomen studies as a function of patient size (weight) and various acquisition parameters for a GE VCT 64-detector row array scanner (GE Healthcare,

Milwaukee, WI). The protocols are based on weight, using CT automatic exposure control (Smart mA), a pre-determined noise index, and a blending of 30% adaptive statistical iterative reconstruction (ASIR)

provide substantial improvement in image quality, as shown in Fig. 12, which compares FBP with statistical IR and modelbased IR processing. However, the complexity introduced by the number of model inputs and permutations that affect

iterative processing extends reconstruction times to hours, certainly incompatible with routine clinical applications requiring fast turnaround times. Expect advances in computer hardware, processing speed, and model-based algorithms to achieve clinical relevance in the near future.

Conclusion

Fig. 11 Effective dose (mSv) for CT scanners with and without IR software at UC Davis Medical Center for adult abdomen-pelvis exams completed in 2012 and 2013. Top shows the distribution of doses for CT scanner operation without iterative reconstruction, and the bottom for CT scanner operation with iterative reconstruction. The range of effective dose is in quartiles from left to right: first, median, third. The median effective dose for no IR is 11.8 mSv, while that for IR is 6.0 mSv. Similar reductions for pediatric scans have also been achieved

Statistical iterative reconstruction techniques and their application to the CT reconstruction process can result in lower doses on the order of 10–40% compared to conventional filtered back projection, with comparable image quality. Implementation involves an update of CT system software and often hardware upgrades, the latter to achieve faster reconstruction times with dedicated processing power. Iterative reconstruction capabilities are a particularly important consideration for reducing CT dose for the pediatric patient population and should be a consideration for retrofitting older CT scanners and most certainly for

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Fig. 12 Example of model-based reconstruction results (right) compared to IR (middle) and standard filtered back projection (left), all reconstructed from the same raw data. Printed with permission [5]

implementation on newly purchased scanners. When used in conjunction with other CT dose reduction methods such as tube current modulation, a substantially lowered dose for these exams will significantly enhance the benefit-to-risk ratio and result in better patient care for all patients, but in particular pediatric patients.

Conflicts of interest Dr. Seibert has no financial interests, investigational or off-label uses to disclose.

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Iterative reconstruction: how it works, how to apply it.

Computed tomography acquires X-ray projection data from multiple angles though an object to generate a tomographic rendition of its attenuation charac...
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