Combining ordered subsets and momentum for accelerated X-ray CT image reconstruction

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

BACKGROUND

Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the acquisition scan time is to decrease of the number of projections, but this method generates streak artifacts on breast specimen images. Furthermore, the presence of a metallic-needle marker on a breast specimen causes metal artifacts that are prominently visible in the images. In this work, we propose a deep learning-based approach for suppressing both streak and metal artifacts in CBCT.

METHODS

In this work, sinogram datasets acquired from CBCT and a small number of projections containing metal objects were used. The sinogram was first modified by removing metal objects and up sampling in the angular direction. Then, the modified sinogram was initialized by linear interpolation and synthesized by a modified neural network model based on a U-Net structure. To obtain the reconstructed images, the synthesized sinogram was reconstructed using the traditional filtered backprojection (FBP) approach. The remaining residual artifacts on the images were further handled by another neural network model, ResU-Net. The corresponding denoised image was combined with the extracted metal objects in the same data positions to produce the final results.

RESULTS

The image quality of the reconstructed images from the proposed method was improved better than the images from the conventional FBP, iterative reconstruction (IR), sinogram with linear interpolation, denoise with ResU-Net, sinogram with U-Net. The proposed method yielded 3.6 times higher contrast-to-noise ratio, 1.3 times higher peak signal-to-noise ratio, and 1.4 times higher structural similarity index (SSIM) than the traditional technique. Soft tissues around the marker on the images showed good improvement, and the mainly severe artifacts on the images were significantly reduced and regulated by the proposed.

METHOD

CONCLUSIONS

Our proposed method performs well reducing streak and metal artifacts in the CBCT reconstructed images, thus improving the overall breast specimen images. This would be beneficial for clinical use.

Spatiotemporal image reconstruction to enable high-frame-rate dynamic photoacoustic tomography with rotating-gantry volumetric imagers

Significance

Dynamic photoacoustic computed tomography (PACT) is a valuable imaging technique for monitoring physiological processes. However, current dynamic PACT imaging techniques are often limited to two-dimensional spatial imaging. Although volumetric PACT imagers are commercially available, these systems typically employ a rotating measurement gantry in which the tomographic data are sequentially acquired as opposed to being acquired simultaneously at all views. Because the dynamic object varies during the data-acquisition process, the sequential data-acquisition process poses substantial challenges to image reconstruction associated with data incompleteness. The proposed image reconstruction method is highly significant in that it will address these challenges and enable volumetric dynamic PACT imaging with existing preclinical imagers.

Aim

The aim of this study is to develop a spatiotemporal image reconstruction (STIR) method for dynamic PACT that can be applied to commercially available volumetric PACT imagers that employ a sequential scanning strategy. The proposed reconstruction method aims to overcome the challenges caused by the limited number of tomographic measurements acquired per frame.

Approach

A low-rank matrix estimation-based STIR (LRME-STIR) method is proposed to enable dynamic volumetric PACT. The LRME-STIR method leverages the spatiotemporal redundancies in the dynamic object to accurately reconstruct a four-dimensional (4D) spatiotemporal image.

Results

The conducted numerical studies substantiate the LRME-STIR method's efficacy in reconstructing 4D dynamic images from tomographic measurements acquired with a rotating measurement gantry. The experimental study demonstrates the method's ability to faithfully recover the flow of a contrast agent with a frame rate of 10 frames per second, even when only a single tomographic measurement per frame is available.

Conclusions

The proposed LRME-STIR method offers a promising solution to the challenges faced by enabling 4D dynamic imaging using commercially available volumetric PACT imagers. By enabling accurate STIRs, this method has the potential to significantly advance preclinical research and facilitate the monitoring of critical physiological biomarkers.

Performance evaluation of quantitative material decomposition in slow kVp switching dual-energy CT

BACKGROUND

Slow kVp switching technique is an important approach to realize dual-energy CT (DECT) imaging, but its performance has not been thoroughly investigated yet.

OBJECTIVE

This study aims at comparing and evaluating the DECT imaging performance of different slow kVp switching protocols, and thus helps determining the optimal system settings.

METHODS

To investigate the impact of energy separation, two different beam filtration schemes are compared: the stationary beam filtration and dynamic beam filtration. Moreover, uniform tube voltage modulation and weighted tube voltage modulation are compared along with various modulation frequencies. A model-based direct decomposition algorithm is employed to generate the water and iodine material bases. Both numerical and physical experiments are conducted to verify the slow kVp switching DECT imaging performance.

RESULTS

Numerical and experimental results demonstrate that the material decomposition is less sensitive to beam filtration, voltage modulation type and modulation frequency. As a result, robust material-specific quantitative decomposition can be achieved in slow kVp switching DECT imaging.

CONCLUSIONS

Quantitative DECT imaging can be implemented with slow kVp switching under a variety of system settings.

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks

BACKGROUND

Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts.

PURPOSE

We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction.

METHODS

Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts.

RESULTS

The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clinical evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction).

CONCLUSIONS

For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motion artifacts.

An Investigation of Stochastic Variance Reduction Algorithms for Relative Difference Penalized 3D PET Image Reconstruction

Penalised PET image reconstruction algorithms are often accelerated during early iterations with the use of subsets. However, these methods may exhibit limit cycle behaviour at later iterations due to variations between subsets. Desirable converged images can be achieved for a subclass of these algorithms via the implementation of a relaxed step size sequence, but the heuristic selection of parameters will impact the quality of the image sequence and algorithm convergence rates. In this work, we demonstrate the adaption and application of a class of stochastic variance reduction gradient algorithms for PET image reconstruction using the relative difference penalty and numerically compare convergence performance to BSREM. The two investigated algorithms are: SAGA and SVRG. These algorithms require the retention in memory of recently computed subset gradients, which are utilised in subsequent updates. We present several numerical studies based on Monte Carlo simulated data and a patient data set for fully 3D PET acquisitions. The impact of the number of subsets, different preconditioners and step size methods on the convergence of regions of interest values within the reconstructed images is explored. We observe that when using constant preconditioning, SAGA and SVRG demonstrate reduced variations in voxel values between subsequent updates and are less reliant on step size hyper-parameter selection than BSREM reconstructions. Furthermore, SAGA and SVRG can converge significantly faster to the penalised maximum likelihood solution than BSREM, particularly in low count data.

A Fast Convergent Ordered-Subsets Algorithm With Subiteration-Dependent Preconditioners for PET Image Reconstruction

We investigated the imaging performance of a fast convergent ordered-subsets algorithm with subiteration-dependent preconditioners (SDPs) for positron emission tomography (PET) image reconstruction. In particular, we considered the use of SDP with the block sequential regularized expectation maximization (BSREM) approach with the relative difference prior (RDP) regularizer due to its prior clinical adaptation by vendors. Because the RDP regularization promotes smoothness in the reconstructed image, the directions of the gradients in smooth areas more accurately point toward the objective function's minimizer than those in variable areas. Motivated by this observation, two SDPs have been designed to increase iteration step-sizes in the smooth areas and reduce iteration step-sizes in the variable areas relative to a conventional expectation maximization preconditioner. The momentum technique used for convergence acceleration can be viewed as a special case of SDP. We have proved the global convergence of SDP-BSREM algorithms by assuming certain characteristics of the preconditioner. By means of numerical experiments using both simulated and clinical PET data, we have shown that the SDP-BSREM algorithms substantially improve the convergence rate, as compared to conventional BSREM and a vendor's implementation as Q.Clear. Specifically, SDP-BSREM algorithms converge 35%-50% faster in reaching the same objective function value than conventional BSREM and commercial Q.Clear algorithms. Moreover, we showed in phantoms with hot, cold and background regions that the SDP-BSREM algorithms approached the values of a highly converged reference image faster than conventional BSREM and commercial Q.Clear algorithms.

Truncation effect reduction for fast iterative reconstruction in cone-beam CT

Background

Iterative reconstruction for cone-beam computed tomography (CBCT) has been applied to improve image quality and reduce radiation dose. In a case where an object’s actual projection is larger than a flat panel detector, CBCT images contain truncated data or incomplete projections, which degrade image quality inside the field of view (FOV). In this work, we propose truncation effect reduction for fast iterative reconstruction in CBCT imaging.

Methods

The volume matrix size of the FOV and the height of projection images were extrapolated to a suitable size. These extended projections were reconstructed by fast iterative reconstruction. Moreover, a smoothing parameter for noise regularization in iterative reconstruction was modified to reduce the accumulated error while processing. The proposed work was evaluated by image quality measurements and compared with conventional filtered backprojection (FBP). To validate the proposed method, we used a head phantom for evaluation and preliminarily tested on a human dataset.

Results

In the experimental results, the reconstructed images from the head phantom showed enhanced image quality. In addition, fast iterative reconstruction can be run continuously while maintaining a consistent mean-percentage-error value for many iterations. The contrast-to-noise ratio of the soft-tissue images was improved. Visualization of low contrast in the ventricle and soft-tissue images was much improved compared to those from FBP using the same dose index of 5 mGy.

Conclusions

Our proposed method showed satisfactory performance to reduce the truncation effect, especially inside the FOV with better image quality for soft-tissue imaging. The convergence of fast iterative reconstruction tends to be stable for many iterations.

LRR-CED: low-resolution reconstruction-aware convolutional encoder–decoder network for direct sparse-view CT image reconstruction

Objective. Sparse-view computed tomography (CT) reconstruction has been at the forefront of research in medical imaging. Reducing the total x-ray radiation dose to the patient while preserving the reconstruction accuracy is a big challenge. The sparse-view approach is based on reducing the number of rotation angles, which leads to poor quality reconstructed images as it introduces several artifacts. These artifacts are more clearly visible in traditional reconstruction methods like the filtered-backprojection (FBP) algorithm. Approach. Over the years, several model-based iterative and more recently deep learning-based methods have been proposed to improve sparse-view CT reconstruction. Many deep learning-based methods improve FBP-reconstructed images as a post-processing step. In this work, we propose a direct deep learning-based reconstruction that exploits the information from low-dimensional scout images, to learn the projection-to-image mapping. This is done by concatenating FBP scout images at multiple resolutions in the decoder part of a convolutional encoder–decoder (CED). Main results. This approach is investigated on two different networks, based on Dense Blocks and U-Net to show that a direct mapping can be learned from a sinogram to an image. The results are compared to two post-processing deep learning methods (FBP-ConvNet and DD-Net) and an iterative method that uses a total variation (TV) regularization. Significance. This work presents a novel method that uses information from both sinogram and low-resolution scout images for sparse-view CT image reconstruction. We also generalize this idea by demonstrating results with two different neural networks. This work is in the direction of exploring deep learning across the various stages of the image reconstruction pipeline involving data correction, domain transfer and image improvement.

Noise-robust phase-space deconvolution for light-field microscopy

SIGNIFICANCE

Light-field microscopy has achieved success in various applications of life sciences that require high-speed volumetric imaging. However, existing light-field reconstruction algorithms degrade severely in low-light conditions, and the deconvolution process is time-consuming.

AIM

This study aims to develop a noise robustness phase-space deconvolution method with low computational costs.

APPROACH

We reformulate the light-field phase-space deconvolution model into the Fourier domain with random-subset ordering and total-variation (TV) regularization. Additionally, we build a time-division-based multicolor light-field microscopy and conduct the three-dimensional (3D) imaging of the heart beating in zebrafish larva at over 95 Hz with a low light dose.

RESULTS

We demonstrate that this approach reduces computational resources, brings a tenfold speedup, and achieves a tenfold improvement for the noise robustness in terms of SSIM over the state-of-the-art approach.

CONCLUSIONS

We proposed a phase-space deconvolution algorithm for 3D reconstructions in fluorescence imaging. Compared with the state-of-the-art method, we show significant improvement in both computational effectiveness and noise robustness; we further demonstrated practical application on zebrafish larva with low exposure and low light dose.

Convex optimization algorithms in medical image reconstruction - in the age of AI

The past decade has seen the flourish of model based image reconstruction (MBIR) algorithms, which are often applications or adaptations of convex optimization algorithms from the optimization community. We review some state-of-the-art algorithms that have enjoyed wide popularity in medical image reconstruction, emphasize known connections between different algorithms, and discuss practical issues such as computation and memory cost. More recently, deep learning (DL) has forayed into medical imaging, where the latest development tries to exploit the synergy between DL and MBIR to elevate the MBIR's performance. We present existing approaches and emerging trends in DL-enhanced MBIR methods, with particular attention to the underlying role of convexity and convex algorithms on network architecture. We also discuss how convexity can be employed to improve the generalizability and representation power of DL networks in general.

Reducing residual-motion artifacts in iterative 3D CBCT reconstruction in image-guided radiation therapy

PURPOSE

Recent evaluations of a 3D iterative cone-beam computed tomography (iCBCT) reconstruction method available on Varian radiation treatment devices demonstrated that iCBCT provides superior image quality when compared to analytical Feldkamp-Davis-Kress (FDK) method. However, iCBCT employs statistical penalized likelihood (PL) that is known to be highly sensitive to inconsistencies due to physiological motion occurring during the acquisition. We propose a computationally inexpensive extension of iCBCT addressing this deficiency.

METHODS

During the iterative process, the gradients of PL are modified to avoid the generation of motion-related artifacts. To assess the impact of this modification, we propose a motion simulation generating CBCT projections of a moving anatomy together with artifact-free images used as ground truth. Contrast-to-noise ratio and power spectra of difference images are computed to quantify the impact of the motion on reconstructed CBCT volumes as well as the effect of the proposed modification.

RESULTS

Using both simulated and clinical data, it is shown that the motion of patient's abdominal wall during the acquisition results in artifacts that can be quantified as low-frequency components in volumes reconstructed with iCBCT. Further, a quantitative evaluation demonstrates that the proposed modification of PL reduces these low-frequency components. While preserving the advantages of PL, it effectively suppresses the propagation of motion-related artifacts into clinically important regions, thus increasing the motion resiliency of iCBCT.

CONCLUSIONS

The proposed modified iterative reconstruction method significantly improves the quality of CBCT images of anatomies suffering from residual motion.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on quantitative paraspinal muscle assessment

Background

Wasting disease entities like cachexia or sarcopenia are associated with a decreasing muscle mass and changing muscle composition. For valid and reliable disease detection and monitoring diagnostic techniques offering quantitative musculature assessment are needed. Multi-detector computed tomography (MDCT) is a broadly available imaging modality allowing for muscle composition analysis. A major disadvantage of using MDCT for muscle composition assessment is the radiation exposure. In this study we evaluated the performance of different methods of radiation dose reduction for paravertebral muscle composition assessment.

Methods

MDCT scans of eighteen subjects (6 males, age: 71.5±15.9 years, and 12 females, age: 71.0±8.9 years) were retrospectively simulated as if they were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections (i.e., sparse sampling). Images were reconstructed with a statistical iterative reconstruction (SIR) algorithm. Paraspinal muscles (psoas and erector spinae muscles) at the level of L4 were segmented in the original-dose images. Segmentations were superimposed on all low-dose scans and muscle density (MD) extracted.

Results

Sparse sampling derived mean MD showed no significant changes (P=0.57 and P=0.22) down to 5% of the original projections in the erector spinae and psoas muscles, respectively. All virtually reduced tube current series showed significantly different (P>0.05) mean MD in the psoas and erector spinae muscles as compared to the original dose except for the images of 5% of the original tube current in the erector spinae muscle.

Conclusions

Our findings demonstrated the possibility of considerable radiation dose reduction using MDCT scans for assessing the composition of the paravertebral musculature. The sparse sampling approach seems to be promising and a potentially superior technique for dose reduction as compared to tube current reduction.

Regularization by denoising sub-sampled Newton method for spectral CT multi-material decomposition

Spectral Computed Tomography (CT) is an emerging technology that enables us to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra. In this work, we aim at efficiently solving a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT. In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function using a randomized second order method. By approximating the Newton step using a sketching of the Hessian of the likelihood function, it is possible to reduce the complexity while retaining the complex prior structure given by the data-driven regularizer. We exploit a non-uniform block sub-sampling of the Hessian with inexact but efficient conjugate gradient updates that require only Jacobian-vector products for denoising term. Finally, we show numerical and experimental results for spectral CT materials decomposition. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 1'.

A prototype spatial-spectral CT system for material decomposition with energy-integrating detectors

PURPOSE

Spectral CT has great potential for a variety of clinical applications due to improved tissue and material discrimination over conventional single-energy CT. Many clinical and preclinical spectral CT systems have two spectral channels enabling dual-energy CT. Strategies include split filtration, dual-layer detectors, photon-counting detectors, and kVp switching. The motivation for this work is the development of an x-ray source spectral modulation device with three or more spectral channels to enable high-sensitivity multi-material decomposition with energy-integrating detectors.

MATERIALS AND METHODS

We present spatial-spectral filters which are a new x-ray source modulation technology with the potential for additional channel diversity. The filtering device consists of an array of K-edge materials which divide the x-ray beam into spectrally varied beamlets. This design allows for an arbitrary number of spectral channels-trading off spatial and spectral information. We use a one-step model-based material decomposition (MBMD) algorithm to iteratively estimate material density images directly from the spatial-spectral CT data. In this work, we present a prototype spatial-spectral filter integrated with an x-ray CT test bench. The filter is composed of an array of tin, erbium, tantalum, and lead filter tiles which spatially modulate the system spectral sensitivity pattern. In a simulation study, we investigate the particular problem of mis-calibration between the data acquisition and the reconstruction model. With an understanding of the required model accuracy, we present a spectral calibration method to estimate the critical model parameters. To demonstrate feasibility of the spatial-spectral filter with a calibrated beamlet model, we collected a spatial-spectral CT scan of a multicontrast-enhanced phantom containing water, iodine, and gadolinium solutions.

RESULTS

With simulations, we show that material decomposition is possible with spatial-spectral-filtered CT data, and we demonstrate the importance of a well-calibrated physical model. We find a 50% increase in error for focal spot model mismatch of 0.27mm and gap width model mismatch of 16 mμ. With physical results, we demonstrate that the calibrated system model is in close agreement with the measured data, and that the reconstructed material density images match the ground truth concentrations for the multicontrast phantom. Empirical results indicate gadolinium density estimation had an error of 17-58% mostly due to a systematic constant bias of 0.30-0.60 mg/ml. Water density estimates are within 1% and iodine estimates are within 10% of ground truth.

CONCLUSION

These preliminary results demonstrate the potential of spatial-spectral filters to enable multicontrast imaging. Moreover, this device is compatible with energy-integrating detectors and so provides a feasible modification to enable spectral CT imaging with existing single-energy systems.

Computed tomography image reconstruction using stacked U-Net

Since the development of deep learning methods, many researchers have focused on image quality improvement using convolutional neural networks. They proved its effectivity in noise reduction, single-image super-resolution, and segmentation. In this study, we apply stacked U-Net, a deep learning method, for X-ray computed tomography image reconstruction to generate high-quality images in a short time with a small number of projections. It is not easy to create highly accurate models because medical images have few training images due to patients' privacy issues. Thus, we utilize various images from the ImageNet, a widely known visual database. Results show that a cross-sectional image with a peak signal-to-noise ratio of 27.93 db and a structural similarity of 0.886 is recovered for a 512 × 512 image using 360-degree rotation, 512 detectors, and 64 projections, with a processing time of 0.11 s on the GPU. Therefore, the proposed method has a shorter reconstruction time and better image quality than the existing methods.

Low-Dose MDCT of Patients With Spinal Instrumentation Using Sparse Sampling: Impact on Metal Artifacts

OBJECTIVE. The purpose of our study was to evaluate simulated sparse-sampled MDCT combined with statistical iterative reconstruction (SIR) for low-dose imaging of patients with spinal instrumentation. MATERIALS AND METHODS. Thirty-eight patients with implanted hardware after spinal instrumentation (24 patients with short- or long-term instrumentation-related complications [i.e., adjacent segment disease, screw loosening or implant failure, or postoperative hematoma or seroma] and 14 control subjects with no complications) underwent MDCT. Scans were simulated as if they were performed with 50% (P50), 25% (P25), 10% (P10), and 5% (P5) of the projections of the original acquisition using an in-house-developed SIR algorithm for advanced image reconstructions. Two readers performed qualitative image evaluations of overall image quality and artifacts, image contrast, inspection of the spinal canal, and diagnostic confidence (1 = high, 2 = medium, and 3 = low confidence). RESULTS. Although overall image quality decreased and artifacts increased with reductions in the number of projections, all complications were detected by both readers when 100% of the projections of the original acquisition (P100), P50, and P25 imaging data were used. For P25 data, diagnostic confidence was still high (mean score ± SD: reader 1, 1.2 ± 0.4; reader 2, 1.3 ± 0.5), and interreader agreement was substantial to almost perfect (weighted Cohen κ = 0.787-0.855). The mean volumetric CT dose index was 3.2 mGy for P25 data in comparison with 12.6 mGy for the original acquisition (P100 data). CONCLUSION. The use of sparse sampling and SIR for low-dose MDCT in patients with spinal instrumentation facilitated considerable reductions in radiation exposure. The use of P25 data with SIR resulted in no missed complications related to spinal instrumentation and allowed high diagnostic confidence, so using only 25% of the projections is probably enough for accurate and confident diagnostic detection of major instrumentation-related complications.

Accelerated 3D image reconstruction with a morphological pyramid and noise-power convergence criterion

Model-based iterative reconstruction (MBIR) for cone-beam CT (CBCT) offers better noise-resolution tradeoff and image quality than analytical methods for acquisition protocols with low x-ray dose or limited data, but with increased computational burden that poses a drawback to routine application in clinical scenarios. This work develops a comprehensive framework for acceleration of MBIR in the form of penalized weighted least squares optimized with ordered subsets separable quadratic surrogates. The optimization was scheduled on a set of stages forming a morphological pyramid varying in voxel size. Transition between stages was controlled with a convergence criterion based on the deviation between the mid-band noise power spectrum (NPS) measured on a homogeneous region of the evolving reconstruction and that expected for the converged image, computed with an analytical model that used projection data and the reconstruction parameters. A stochastic backprojector was developed by introducing a random perturbation to the sampling position of each voxel for each ray in the reconstruction within a voxel-based backprojector, breaking the deterministic pattern of sampling artifacts when combined with an unmatched Siddon forward projector. This fast, forward and backprojector pair were included into a multi-resolution reconstruction strategy to provide support for objects partially outside of the field of view. Acceleration from ordered subsets was combined with momentum accumulation stabilized with an adaptive technique that automatically resets the accumulated momentum when it diverges noticeably from the current iteration update. The framework was evaluated with CBCT data of a realistic abdomen phantom acquired on an imaging x-ray bench and with clinical CBCT data from an angiography robotic C-arm (Artis Zeego, Siemens Healthineers, Forchheim, Germany) acquired during a liver embolization procedure. Image fidelity was assessed with the structural similarity index (SSIM) computed with a converged reconstruction. The accelerated framework provided accurate reconstructions in 60 s (SSIM = 0.97) and as little as 27 s (SSIM = 0.94) for soft-tissue evaluation. The use of simple forward and backprojectors resulted in 9.3× acceleration. Accumulation of momentum provided extra ∼3.5× acceleration with stable convergence for 6-30 subsets. The NPS-driven morphological pyramid resulted in initial faster convergence, achieving similar SSIM with 1.5× lower runtime than the single-stage optimization. Acceleration of MBIR to provide reconstruction time compatible with clinical applications is feasible via architectures that integrate algorithmic acceleration with approaches to provide stable convergence, and optimization schedules that maximize convergence speed.

Low-dose MDCT: evaluation of the impact of systematic tube current reduction and sparse sampling on the detection of degenerative spine diseases

OBJECTIVES

To investigate potential radiation dose reduction for multi-detector computed tomography (MDCT) exams of the spine by using sparse sampling and virtually lowered tube currents combined with statistical iterative reconstruction (SIR).

METHODS

MDCT data of 26 patients (68.9 ± 11.7 years, 42.3% males) were retrospectively simulated as if the scans were acquired at 50%, 10%, 5%, and 3% of the original X-ray tube current or number of projections, using SIR for image reconstructions. Two readers performed qualitative image evaluation considering overall image quality, artifacts, and contrast and determined the number and type of degenerative changes. Scoring was compared between readers and virtual low-dose and sparse-sampled MDCT, respectively.

RESULTS

Image quality and contrast decreased with virtual lowering of tube current and sparse sampling, but all degenerative changes were correctly detected in MDCT with 50% of tube current as well as MDCT with 50% of projections. Sparse-sampled MDCT with only 10% of initial projections still enabled correct identification of all degenerative changes, in contrast to MDCT with virtual tube current reduction by 90% where non-calcified disc herniations were frequently missed (R1: 23.1%, R2: 21.2% non-diagnosed herniations). The average volumetric CT dose index (CTDI_vol) was 1.4 mGy for MDCT with 10% of initial projections, compared with 13.8 mGy for standard-dose imaging.

CONCLUSIONS

MDCT with 50% of original tube current or projections using SIR still allowed for accurate diagnosis of degenerative changes. Sparse sampling may be more promising for further radiation dose reductions since no degenerative changes were missed with 10% of initial projections.

KEY POINTS

• Most common degenerative changes of the spine can be diagnosed in multi-detector CT with 50% of tube current or number of projections. • Sparse-sampled multi-detector CT with only 10% of initial projections still enables correct identification of degenerative changes, in contrast to imaging with 10% of original tube current. • Sparse sampling may be a promising option for distinct lowering of radiation dose, reducing the CTDI_vol from 13.8 to 1.4 mGy in the study cohort.

Impact of the non-negativity constraint in model-based iterative reconstruction from CT data

PURPOSE

Model-based iterative reconstruction is a promising approach to achieve dose reduction without affecting image quality in diagnostic x-ray computed tomography (CT). In the problem formulation, it is common to enforce non-negative values to accommodate the physical non-negativity of x-ray attenuation. Using this a priori information is believed to be beneficial in terms of image quality and convergence speed. However, enforcing non-negativity imposes limitations on the problem formulation and the choice of optimization algorithm. For these reasons, it is critical to understand the value of the non-negativity constraint. In this work, we present an investigation that sheds light on the impact of this constraint.

METHODS

We primarily focus our investigation on the examination of properties of the converged solution. To avoid any possibly confounding bias, the reconstructions are all performed using a provably converging algorithm started from a zero volume. To keep the computational cost manageable, an axial CT scanning geometry with narrow collimation is employed. The investigation is divided into five experimental studies that challenge the non-negativity constraint in various ways, including noise, beam hardening, parametric choices, truncation, and photon starvation. These studies are complemented by a sixth one that examines the effect of using ordered subsets to obtain a satisfactory approximate result within 50 iterations. All studies are based on real data, which come from three phantom scans and one clinical patient scan. The reconstructions with and without the non-negativity constraint are compared in terms of image similarity and convergence speed. In select cases, the image similarity evaluation is augmented with quantitative image quality metrics such as the noise power spectrum and closeness to a known ground truth.

RESULTS

For cases with moderate inconsistencies in the data, associated with noise and bone-induced beam hardening, our results show that the non-negativity constraint offers little benefit. By varying the regularization parameters in one of the studies, we observed that sufficient edge-preserving regularization tends to dilute the value of the constraint. For cases with strong data inconsistencies, the results are mixed: the constraint can be both beneficial and deleterious; in either case, however, the difference between using the constraint or not is small relative to the overall level of error in the image. The results with ordered subsets are encouraging in that they show similar observations. In terms of convergence speed, we only observed one major effect, in the study with data truncation; this effect favored the use of the constraint, but had no impact on our ability to obtain the converged solution without constraint.

CONCLUSIONS

Our results did not highlight the non-negativity constraint as being strongly beneficial for diagnostic CT imaging. Altogether, we thus conclude that in some imaging scenarios, the non-negativity constraint could be disregarded to simplify the optimization problem or to adopt other forward projection models that require complex optimization machinery to be used together with non-negativity.

Accelerating iterative coordinate descent using a stored system matrix

PURPOSE

The computational burden associated with model-based iterative reconstruction (MBIR) is still a practical limitation. Iterative coordinate descent (ICD) is an optimization approach for MBIR that has sometimes been thought to be incompatible with modern computing architectures, especially graphics processing units (GPUs). The purpose of this work is to accelerate the previously released open-source FreeCT_ICD to include GPU acceleration and to demonstrate computational performance with ICD that is comparable with simultaneous update approaches.

METHODS

FreeCT_ICD uses a stored system matrix (SSM), which precalculates the forward projector in the form of a sparse matrix and then reconstructs on a rotating coordinate grid to exploit helical symmetry. In our GPU ICD implementation, we shuffle the sinogram memory ordering such that data access in the sinogram coalesce into fewer transactions. We also update N_S voxels in the xy-plane simultaneously to improve occupancy. Conventional ICD updates voxels sequentially (N_S  = 1). Using N_S  > 1 eliminates existing convergence guarantees. Convergence behavior in a clinical dataset was therefore studied empirically.

RESULTS

On a pediatric dataset with sinogram size of 736 × 16 × 13860 reconstructed to a matrix size of 512 × 512 × 128, our code requires about 20 s per iteration on a single GPU compared to 2300 s per iteration for a 6-core CPU using FreeCT_ICD. After 400 iterations, the proposed and reference codes converge within 2 HU RMS difference (RMSD). Using a wFBP initialization, convergence within 10 HU RMSD is achieved within 4 min. Convergence is similar with N_S values between 1 and 256, and N_S  = 16 was sufficient to achieve maximum performance. Divergence was not observed until N_S  > 1024.

CONCLUSIONS

With appropriate modifications, ICD may be able to achieve computational performance competitive with simultaneous update algorithms currently used for MBIR.

Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm

Low dose computed tomography (CT) has drawn much attention in the medical imaging field because of its ability to reduce the radiation dose. Recently, statistical iterative reconstruction (SIR) with total variation (TV) penalty has been developed to low dose CT image reconstruction. Nevertheless, the TV penalty has the drawback of creating blocky effects in the reconstructed images. To overcome the limitations of TV, in this paper we firstly introduce the structure tensor total variation (STV₁) penalty into SIR framework for low dose CT image reconstruction. Then, an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) is developed to minimize the objective function. The proposed AFISTA reconstruction algorithm was evaluated using numerical simulated low dose projection based on two CT images and realistic low dose projection data of a sheep lung CT perfusion. The experimental results demonstrated that our proposed STV₁-based algorithm outperform FBP and TV-based algorithm in terms of removing noise and restraining blocky effects.

SPULTRA: Low-Dose CT Image Reconstruction With Joint Statistical and Learned Image Models

Low-dose CT image reconstruction has been a popular research topic in recent years. A typical reconstruction method based on post-log measurements is called penalized weighted-least squares (PWLS). Due to the underlying limitations of the post-log statistical model, the PWLS reconstruction quality is often degraded in low-dose scans. This paper investigates a shifted-Poisson (SP) model based likelihood function that uses the pre-log raw measurements that better represents the measurement statistics, together with a data-driven regularizer exploiting a Union of Learned TRAnsforms (SPULTRA). Both the SP induced data-fidelity term and the regularizer in the proposed framework are nonconvex. The proposed SPULTRA algorithm uses quadratic surrogate functions for the SP induced data-fidelity term. Each iteration involves a quadratic subproblem for updating the image, and a sparse coding and clustering subproblem that has a closed-form solution. The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.

Low-dose and sparse sampling MDCT-based femoral bone strength prediction using finite element analysis

This study aims to evaluate the impact of dose reduction through tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis. FE-predicted femoral failure load obtained from MDCT scan data was not significantly affected by 50% dose reductions through sparse sampling. Further decrease in dose through sparse sampling (25% of original projections) and virtually reduced tube current (50% and 25% of the original dose) showed significant effects on the FE-predicted failure load results.

PURPOSE

To investigate the effect of virtually reduced tube current and sparse sampling on multi-detector computed tomography (MDCT)-based femoral bone strength prediction using finite element (FE) analysis.

METHODS

Routine MDCT data covering the proximal femur of 21 subjects (17 males; 4 females; mean age, 71.0 ± 8.8 years) without any bone diseases aside from osteoporosis were included in this study. Fifty percent and 75% dose reductions were achieved by virtually reducing tube current and by applying a sparse sampling strategy from the raw image data. Images were then reconstructed with a statistically iterative reconstruction algorithm. FE analysis was performed on all reconstructed images and the failure load was calculated. The root mean square coefficient of variation (RMSCV) and coefficient of correlation (R²) were calculated to determine the variation in the FE-predicted failure load data for dose reductions, using original-dose MDCT scan as the standard of reference.

RESULTS

Fifty percent dose reduction through sparse sampling showed lower RMSCV and higher correlations when compared with virtually reduced tube current method (RMSCV = 5.70%, R² = 0.96 vs. RMSCV = 20.78%, R² = 0.79). Seventy-five percent dose reduction achieved through both methods (RMSCV = 22.38%, R² = 0.80 for sparse sampling; RMSCV = 24.58%, R² = 0.73 for reduced tube current) could not predict the failure load accurately.

CONCLUSION

Our simulations indicate that up to 50% reduction in radiation dose through sparse sampling can be used for FE-based prediction of femoral failure load. Sparse-sampled MDCT may allow fracture risk prediction and treatment monitoring in osteoporosis with less radiation exposure in the future.

Finite Element Analysis-Based Vertebral Bone Strength Prediction Using MDCT Data: How Low Can We Go?

Objective: To study the impact of dose reduction in MDCT images through tube current reduction or sparse sampling on the vertebral bone strength prediction using finite element (FE) analysis for fracture risk assessment. Methods: Routine MDCT data covering lumbar vertebrae of 12 subjects (six male; six female; 74.70 ± 9.13 years old) were included in this study. Sparsely sampled and virtually reduced tube current-based MDCT images were computed using statistical iterative reconstruction (SIR) with reduced dose levels at 50, 25, and 10% of the tube current and original projections, respectively. Subject-specific static non-linear FE analyses were performed on vertebra models (L1, L2, and L3) 3-D-reconstructed from those dose-reduced MDCT images to predict bone strength. Coefficient of correlation (R²), Bland-Altman plots, and root mean square coefficient of variation (RMSCV) were calculated to find the variation in the FE-predicted strength at different dose levels, using high-intensity dose-based strength as the reference. Results: FE-predicted failure loads were not significantly affected by up to 90% dose reduction through sparse sampling (R² = 0.93, RMSCV = 8.6% for 50%; R² = 0.89, RMSCV = 11.90% for 75%; R² = 0.86, RMSCV = 11.30% for 90%) and up to 50% dose reduction through tube current reduction method (R² = 0.96, RMSCV = 12.06%). However, further reduction in dose with the tube current reduction method affected the ability to predict the failure load accurately (R² = 0.88, RMSCV = 22.04% for 75%; R² = 0.43, RMSCV = 54.18% for 90%). Conclusion: Results from this study suggest that a 50% radiation dose reduction through reduced tube current and a 90% radiation dose reduction through sparse sampling can be used to predict vertebral bone strength. Our findings suggest that the sparse sampling-based method performs better than the tube current-reduction method in generating images required for FE-based bone strength prediction models.

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.

Ordered subsets Non-Local means constrained reconstruction for sparse view cone beam CT system

Sparse-view sampling scans reduce the patient's radiation dose by reducing the total exposure duration. CT reconstructions under such scan mode are often accompanied by severe artifacts due to the high ill-posedness of the problem. In this paper, we use a Non-Local means kernel as a regularization constraint to reconstruct image volumes from sparse-angle sampled cone-beam CT scans. To overcome the huge computational cost of the 3D reconstruction, we propose a sequential update scheme relying on ordered subsets in the image domain. It is shown through experiments on simulated and real data and comparisons with other methods that the proposed approach is robust enough to deal with the number of views reduced up to 1/10. When coupled with a CUDA parallel computing technique, the computation speed of the iterative reconstruction is greatly improved.

A Krasnoselskii-Mann Algorithm With an Improved EM Preconditioner for PET Image Reconstruction

This paper presents a preconditioned Krasnoselskii-Mann (KM) algorithm with an improved EM preconditioner (IEM-PKMA) for higher-order total variation (HOTV) regularized positron emission tomography (PET) image reconstruction. The PET reconstruction problem can be formulated as a three-term convex optimization model consisting of the Kullback-Leibler (KL) fidelity term, a nonsmooth penalty term, and a nonnegative constraint term which is also nonsmooth. We develop an efficient KM algorithm for solving this optimization problem based on a fixed-point characterization of its solution, with a preconditioner and a momentum technique for accelerating convergence. By combining the EM precondtioner, a thresholding, and a good inexpensive estimate of the solution, we propose an improved EM preconditioner that can not only accelerate convergence but also avoid the reconstructed image being "stuck at zero." Numerical results in this paper show that the proposed IEM-PKMA outperforms existing state-of-the-art algorithms including, the optimization transfer descent algorithm and the preconditioned L-BFGS-B algorithm for the differentiable smoothed anisotropic total variation regularized model, the preconditioned alternating projection algorithm, and the alternating direction method of multipliers for the nondifferentiable HOTV regularized model. Encouraging initial experiments using clinical data are presented.

Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners

Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.

A fast regularized iterative algorithm for fan-beam CT reconstruction

We propose a fast iterative image reconstruction algorithm for normal, short-scan, and super-short-scan fan-beam computed tomography (CT), which aims at iterative reconstruction for low-dose and few-view CT by minimizing a data-fidelity term regularized with a total variation (TV) penalty. The derivation of the algorithm can be outlined as follows. First, the original minimization problem is formulated into a saddle-point (primal-dual) problem by using the Lagrangian duality, to which we apply the alternating projection proximal (APP) algorithm, which belongs to a class of first-order primal-dual methods. Second, we precondition the iterative formula using the modified ramp filter of the filtered back-projection (FBP) reconstruction algorithm in such a way that the solution to this preconditioned iteration perfectly coincides with the solution to the original problem. The resulting algorithm converges quickly to the minimizer of the cost function. To demonstrate the advantages of our method, we perform reconstruction experiments using projection data from both numerical phantoms and real CT data. Both qualitative and quantitative results are presented.

Convergence criterion for MBIR based on the local noise-power spectrum: Theory and implementation in a framework for accelerated 3D image reconstruction with a morphological pyramid

Model-based iterative reconstruction (MBIR) offers improved noise-resolution tradeoffs and artifact reduction in cone-beam CT compared to analytical reconstruction, but carries increased computational burden. An important consideration in minimizing computation time is reliable selection of the stopping criterion to perform the minimum number of iterations required to obtain the desired image quality. Most MBIR methods rely on a fixed number of iterations or relative metrics on image or cost-function evolution, and it would be desirable to use metrics that are more representative of the underlying image properties. A second front for reduction of computation time is the use of acceleration techniques (e.g. subsets or momentum). However, most of these techniques do not strictly guarantee convergence of the resulting MBIR method. A data-dependent analytical model of noise-power spectrum (NPS) for penalized weighted least squares (PWLS) reconstruction is proposed as an absolute metric of image properties for the fully converged volume. Distance to convergence is estimated as the root mean squared error (RMSE) between the estimated NPS and an NPS measured on a uniform region of interest (ROI) in the evolving volume. Iterations are stopped when the RMSE falls below a threshold directly related with the properties of the target image. Further acceleration was achieved by combining the spectral stopping criterion with a morphological pyramid (mPyr) in which the minimization of the PWLS cost-function is divided in a cascade of stages. The algorithm parameters (voxel size in this work) change between stages to achieve faster evolution in early stages, and a final stage with the target parameters to guarantee convergence. Transition between stages is governed by the spectral stopping criterion. The approach was evaluated on simulated CBCT data of a realistic digital abdomen phantom. Accuracy of the NPS model and evolution with time of the distance from the measured NPS was assessed in two ROIs. Performance of the spectrally-driven mPyr architecture was compared to a conventional, single stage, PWLS, and to two mPyr designs running a fixed number of iterations. The spectrally-driven mPyr achieved faster convergence, with 40% lower RMSE than the single stage PWLS, and between 10% and 20% RMSE reduction compared to other mPyr designs. The proposed spectral stopping criterion proved to be a suitable choice for a stopping rule, and, in particular, to govern mPyr stage transition.

SparseCT: System concept and design of multislit collimators

PURPOSE

SparseCT, an undersampling scheme for compressed sensing (CS) computed tomography (CT), has been proposed to reduce radiation dose by acquiring undersampled projection data from clinical CT scanners (Koesters et al. in, SparseCT: Interrupted-Beam Acquisition and Sparse Reconstruction for Radiation Dose Reduction; 2017). SparseCT partially blocks the x-ray beam with a multislit collimator (MSC) to perform a multidimensional undersampling along the view and detector row dimensions. SparseCT undersamples the projection data within each view and moves the MSC along the z-direction during gantry rotation to change the undersampling pattern. It enables reconstruction of images from undersampled data using CS algorithms. The purpose of this work is to design the spacing and width of the MSC slits and the MSC motion patterns based on beam separation, undersampling efficiency, and image quality. The development and testing of a SparseCT prototype with the designed MSC will be described in a following paper.

METHODS

We chose a few initial MSC designs based on the guidance from two metrics: beam separation and undersampling efficiency. Both beam separation and undersampling efficiency were measured from numerically simulated photon distribution with MSC taken into consideration. Beam separation measures the separation between x-ray beams from consecutive slits, taking into account penumbra effects on both sides of each slit. Undersampling efficiency measures the dose-weighted similarity between penumbra undersampling and binary undersampling, in other words, the effective contribution of the incident dose to the signal to noise ratio of the projection data. We then compared the initially chosen MSC designs in terms of their reconstruction image quality. SparseCT projections were simulated from fully sampled patient projection data according to the MSC design and motion pattern, reconstructed iteratively using a sparsity-enforcing penalized weighted least squares cost function with ordered subsets/momentum algorithm, and compared visually and quantitatively.

RESULTS

Simulated photon distributions indicate that the size of the penumbra is dominated by the size of the focal spot. Therefore, a wider MSC slit and a smaller focal spot lead to increased beam separation and undersampling efficiency. For fourfold undersampling with a 1.2 mm focal spot, a minimum MSC slit width of three detector rows (projected to the detector surface) is needed for beam separation; for threefold undersampling, a minimum slit width of four detector rows is needed. Simulations of SparseCT projection and reconstruction indicate that the motion pattern of the MSC does not have a visible impact on image quality. An MSC slit width of three or four detector rows yields similar image quality.

CONCLUSION

The MSC is the key component of the SparseCT method. Simulations of MSC designs incorporating x-ray beam penumbra effects showed that for threefold and fourfold dose reductions, an MSC slit width of four detector rows provided reasonable beam separation, undersampling efficiency, and image quality.

Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

Purpose:

We have quantitatively evaluated the image quality of a new commercially available iterative cone-beam computed tomography reconstruction algorithm over standard cone-beam computed tomography image reconstruction results.

Methods:

This iterative cone-beam computed tomography reconstruction pipeline uses a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction in addition to a standard kernel-based correction followed by filtered back-projection-based Feldkamp-Davis-Kress cone-beam computed tomography reconstruction. Standard full-fan half-rotation Head, half-fan full-rotation Head, and standard Pelvis cone-beam computed tomography protocols have been investigated to scan a quality assurance phantom via the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio. An anthropomorphic head phantom was scanned for verification of noise reduction. Clinical patient image data sets for 5 head/neck patients and 5 prostate patients were qualitatively evaluated.

Results:

Quality assurance phantom study results showed that relative to filtered back-projection-based cone-beam computed tomography, noise was reduced from 28.8 ± 0.3 HU to a range between 18.3 ± 0.2 and 5.9 ± 0.2 HU for Full-Fan Head scans, from 14.4 ± 0.2 HU to a range between 12.8 ± 0.3 and 5.2 ± 0.3 HU for Half-Fan Head scans, and from 6.2 ± 0.1 HU to a range between 3.8 ± 0.1 and 2.0 ± 0.2 HU for Pelvis scans, with the iterative cone-beam computed tomography algorithm. Spatial resolution was marginally improved while results for uniformity and HU constancy were similar. For the head phantom study, noise was reduced from 43.6 HU to a range between 24.8 and 13.0 HU for a Full-Fan Head and from 35.1 HU to a range between 22.9 and 14.0 HU for a Half-Fan Head scan. The patient data study showed that artifacts due to photon starvation and streak artifacts were all reduced, and image noise in specified target regions were reduced to 62% ± 15% for 10 patients.

Conclusion:

Noise and contrast-to-noise ratio image quality characteristics were significantly improved using the iterative cone-beam computed tomography reconstruction algorithm relative to the filtered back-projection-based cone-beam computed tomography method. These improvements will enhance the accuracy of cone-beam computed tomography-based image-guided applications.

Model-based material decomposition with a penalized nonlinear least-squares CT reconstruction algorithm

Spectral information in CT may be used for material decomposition to produce accurate reconstructions of material density and to separate materials with similar overall attenuation. Traditional methods separate the reconstruction and decomposition steps, often resulting in undesirable trade-offs (e.g. sampling constraints, a simplified spectral model). In this work, we present a model-based material decomposition algorithm which performs the reconstruction and decomposition simultaneously using a multienergy forward model. In a kV-switching simulation study, the presented method is capable of reconstructing iodine at 0.5 mg ml^-1 with a contrast-to-noise ratio greater than two, as compared to 3.0 mg ml^-1 for image domain decomposition. The presented method also enables novel acquisition methods, which was demonstrated in this work with a combined kV-switching/split-filter acquisition explored in simulation and physical test bench studies. This novel design used four spectral channels to decompose three materials: water, iodine, and gadolinium. In simulation, the presented method accurately reconstructed concentration value estimates with RMSE values of 4.86 mg ml^-1 for water, 0.108 mg ml^-1 for iodine and 0.170 mg ml^-1 for gadolinium. In test-bench data, the RMSE values were 134 mg ml^-1, 5.26 mg ml^-1 and 1.85 mg ml^-1, respectively. These studies demonstrate the ability of model-based material decomposition to produce accurate concentration estimates in challenging spatial/spectral sampling acquisitions.

Improvements in CBCT Image Quality Using a Novel Iterative Reconstruction Algorithm: A Clinical Evaluation

Purpose

This study aimed to evaluate the clinical utility of a novel iterative cone beam computed tomography (CBCT) reconstruction algorithm for prostate and head and neck (HN) cancer.

Methods and Materials

A total of 10 patients with HN and 10 patients with prostate cancer were analyzed. For each patient, raw CBCT acquisition data were used to reconstruct images with a currently available algorithm (FDK_CBCT) and novel iterative algorithm (Iterative_CBCT). Quantitative contouring variation analysis was performed using structures delineated by several radiation oncologists. For prostate, observers contoured the prostate, proximal 2 cm seminal vesicles, bladder, and rectum. For HN, observers contoured the brain stem, spinal canal, right-left parotid glands, and right-left submandibular glands. Observer contours were combined to form a reference consensus contour using the simultaneous truth and performance level estimation method. All observer contours then were compared with the reference contour to calculate the Dice coefficient, Hausdorff distance, and mean contour distance (prostate contour only). Qualitative image quality analysis was performed using a 5-point scale ranging from 1 (much superior image quality for Iterative_CBCT) to 5 (much inferior image quality for Iterative_CBCT).

Results

The Iterative_CBCT data sets resulted in a prostate contour Dice coefficient improvement of approximately 2.4% (P = .029). The average prostate contour Dice coefficient for the Iterative_CBCT data sets was improved for all patients, with improvements up to approximately 10% for 1 patient. The mean contour distance results indicate an approximate 15% reduction in mean contouring error for all prostate regions. For the parotid contours, Iterative_CBCT data sets resulted in a Hausdorff distance improvement of approximately 2 mm (P < .01) and an approximate 2% improvement in Dice coefficient (P = .03). The Iterative_CBCT data sets were scored as equivalent or of better image quality for 97.3% (prostate) and 90.0% (HN) of the patient data sets.

Conclusions

Observers noted an improvement in image uniformity, noise level, and overall image quality for Iterative_CBCT data sets. In addition, expert observers displayed an improved ability to consistently delineate soft tissue structures, such as the prostate and parotid glands. Thus, the novel iterative reconstruction algorithm analyzed in this study is capable of improving the visualization for prostate and HN cancer image guided radiation therapy.

Time of flight PET reconstruction using nonuniform update for regional recovery uniformity

PURPOSE

Time of flight (TOF) PET reconstruction is well known to statistically improve the image quality compared to non-TOF PET. Although TOF PET can improve the overall signal to noise ratio (SNR) of the image compared to non-TOF PET, the SNR disparity between separate regions in the reconstructed image using TOF data becomes higher than that using non-TOF data. Using the conventional ordered subset expectation maximization (OS-EM) method, the SNR in the low activity regions becomes significantly lower than in the high activity regions due to the different photon statistics of TOF bins. A uniform recovery across different SNR regions is preferred if it can yield an overall good image quality within small number of iterations in practice. To allow more uniform recovery of regions, a spatially variant update is necessary for different SNR regions.

METHODS

This paper focuses on designing a spatially variant step size and proposes a TOF-PET reconstruction method that uses a nonuniform separable quadratic surrogates (NUSQS) algorithm, providing a straightforward control of spatially variant step size. To control the noise, a spatially invariant quadratic regularization is incorporated, which by itself does not theoretically affect the recovery uniformity. The Nesterov's momentum method with ordered subsets (OS) is also used to accelerate the reconstruction speed. To evaluate the proposed method, an XCAT simulation phantom and clinical data from a pancreas cancer patient with full (ground truth) and 6× downsampled counts were used, where a Poisson thinning process was employed for downsampling. We selected tumor and cold regions of interest (ROIs) and compared the proposed method with the TOF-based conventional OS-EM and OS-SQS algorithms with an early stopping criterion.

RESULTS

In computer simulation, without regularization, hot regions of OS-EM and OS-NUSQS converged similarly, but cold region of OS-EM was noisier than OS-NUSQS after 24 iterations. With regularization, although the overall speeds of OS-EM and OS-NUSQS were similar, recovery ratios of hot and cold regions reconstructed by the OS-NUSQS were more uniform compared to those of the conventional OS-SQS and OS-EM. The OS-NUSQS with Nesterov's momentum converged faster than others while preserving the uniform recovery. In the clinical example, we demonstrated that the OS-NUSQS with Nesterov's momentum provides more uniform recovery ratios of hot and cold ROIs compared to the OS-SQS and OS-EM. Although the cost function of all methods is equivalent, the proposed method has higher structural similarity (SSIM) values of hot and cold regions compared to other methods after 24 iterations. Furthermore, our computing time using graphics processing unit was 80× shorter than the time using quad-core CPUs.

CONCLUSION

This paper proposes a TOF PET reconstruction method using the OS-NUSQS with Nesterov's momentum for uniform recovery of different SNR regions. In particular, the spatially nonuniform step size in the proposed method provides uniform recovery ratios of different SNR regions, and the Nesterov's momentum further accelerates overall convergence while preserving uniform recovery. The computer simulation and clinical example demonstrate that the proposed method converges uniformly across ROIs. In addition, tumor contrast and SSIM of the proposed method were higher than those of the conventional OS-EM and OS-SQS in early iterations.

Effects of virtual tube current reduction and sparse sampling on MDCT-based femoral BMD measurements

This study investigates the impact of tube current reduction and sparse sampling on femoral bone mineral density (BMD) measurements derived from multi-detector computed tomography (MDCT). The application of sparse sampling led to robust and clinically acceptable BMD measurements. In contrast, BMD measurements derived from MDCT with virtually reduced tube currents showed a considerable increase when compared to original data.

INTRODUCTION

The study aims to evaluate the effects of radiation dose reduction by using virtual reduction of tube current or sparse sampling combined with standard filtered back projection (FBP) and statistical iterative reconstruction (SIR) on femoral bone mineral density (BMD) measurements derived from multi-detector computed tomography (MDCT).

METHODS

In routine MDCT scans of 41 subjects (65.9% men; age 69.3 ± 10.1 years), reduced radiation doses were simulated by lowering tube currents and applying sparse sampling (50, 25, and 10% of the original tube current and projections, respectively). Images were reconstructed using FBP and SIR. BMD values were assessed in the femoral neck and compared between the different dose levels, numbers of projections, and image reconstruction approaches.

RESULTS

Compared to full-dose MDCT, virtual lowering of the tube current by applying our simulation algorithm resulted in increases in BMD values for both FBP (up to a relative change of 32.5%) and SIR (up to a relative change of 32.3%). In contrast, the application of sparse sampling with a reduction down to 10% of projections showed robust BMD values, with clinically acceptable relative changes of up to 0.5% (FBP) and 0.7% (SIR).

CONCLUSIONS

Our simulations, which still require clinical validation, indicate that reductions down to ultra-low tube currents have a significant impact on MDCT-based femoral BMD measurements. In contrast, the application of sparse-sampled MDCT seems a promising future clinical option that may enable a significant reduction of the radiation dose without considerable changes of BMD values.

Comparison of five one-step reconstruction algorithms for spectral CT

Over the last decade, dual-energy CT scanners have gone from prototypes to clinically available machines, and spectral photon counting CT scanners are following. They require a specific reconstruction process, consisting of two steps: material decomposition and tomographic reconstruction. Image-based methods perform reconstruction, then decomposition, while projection-based methods perform decomposition first, and then reconstruction. As an alternative, 'one-step inversion' methods have been proposed, which perform decomposition and reconstruction simultaneously. Unfortunately, one-step methods are typically slower than their two-step counterparts, and in most CT applications, reconstruction time is critical. This paper therefore proposes to compare the convergence speeds of five one-step algorithms. We adapted all these algorithms to solve the same problem: spectral photon-counting CT reconstruction from five energy bins, using a three materials decomposition basis and spatial regularization. The paper compares a Bayesian method which uses non-linear conjugate gradient for minimization (Cai et al 2013 Med. Phys. 40 111916-31), three methods based on quadratic surrogates (Long and Fessler 2014 IEEE Trans. Med. Imaging 33 1614-26, Weidinger et al 2016 Int. J. Biomed. Imaging 2016 1-15, Mechlem et al 2018 IEEE Trans. Med. Imaging 37 68-80), and a primal-dual method based on MOCCA, a modified Chambolle-Pock algorithm (Barber et al 2016 Phys. Med. Biol. 61 3784). Some of these methods have been accelerated by using μ-preconditioning, i.e. by performing all internal computations not with the actual materials the object is made of, but with carefully chosen linear combinations of those. In this paper, we also evaluated the impact of three different μ-preconditioners on convergence speed. Our experiments on simulated data revealed vast differences in the number of iterations required to reach a common image quality objective: Mechlem et al (2018 IEEE Trans. Med. Imaging 37 68-80) needed ten iterations, Cai et al (2013 Med. Phys. 40 111916-31), Long and Fessler (2014 IEEE Trans. Med. Imaging 33 1614-26) and Weidinger et al (2016 Int. J. Biomed. Imaging 2016 1-15) several hundreds, and Barber et al (2016 Phys. Med. Biol. 61 3784) several thousands. We also sum up other practical aspects, like memory footprint and the need to tune extra parameters.

Quantitative cone-beam CT reconstruction with polyenergetic scatter model fusion

Scatter can account for large errors in cone-beam CT (CBCT) due to its wide field of view, and its complicated nature makes its compensation difficult. Iterative polyenergetic reconstruction algorithms offer the potential to provide quantitative imaging in CT, but they are usually incompatible with scatter contaminated measurements. In this work, we introduce a polyenergetic convolutional scatter model that is directly fused into the reconstruction process, and exploits information readily available at each iteration for a fraction of additional computational cost. We evaluate this method with numerical and real CBCT measurements, and show significantly enhanced electron density estimation and artifact mitigation over pre-calculated fast adaptive scatter kernel superposition (fASKS). We demonstrate our approach has two levels of benefit: reducing the bias introduced by estimating scatter prior to reconstruction; and adapting to the spectral and spatial properties of the specimen.

Image quality and dose characteristics for an O-arm intraoperative imaging system with model-based image reconstruction

PURPOSE

To assess the imaging performance and radiation dose characteristics of the O-arm CBCT imaging system (Medtronic Inc., Littleton MA) and demonstrate the potential for improved image quality and reduced dose via model-based image reconstruction (MBIR).

METHODS

Two main studies were performed to investigate previously unreported characteristics of the O-arm system. First is an investigation of dose and 3D image quality achieved with filtered back-projection (FBP) - including enhancements in geometric calibration, handling of lateral truncation and detector saturation, and incorporation of an isotropic apodization filter. Second is implementation of an MBIR algorithm based on Huber-penalized likelihood estimation (PLH) and investigation of image quality improvement at reduced dose. Each study involved measurements in quantitative phantoms as a basis for analysis of contrast-to-noise ratio and spatial resolution as well as imaging of a human cadaver to test the findings under realistic imaging conditions.

RESULTS

View-dependent calibration of system geometry improved the accuracy of reconstruction as quantified by the full-width at half maximum of the point-spread function - from 0.80 to 0.65 mm - and yielded subtle but perceptible improvement in high-contrast detail of bone (e.g., temporal bone). Standard technique protocols for the head and body imparted absorbed dose of 16 and 18 mGy, respectively. For low-to-medium contrast (<100 HU) imaging at fixed spatial resolution (1.3 mm edge-spread function) and fixed dose (6.7 mGy), PLH improved CNR over FBP by +48% in the head and +35% in the body. Evaluation at different dose levels demonstrated 30% increase in CNR at 62% of the dose in the head and 90% increase in CNR at 50% dose in the body.

CONCLUSIONS

A variety of improvements in FBP implementation (geometric calibration, truncation and saturation effects, and isotropic apodization) offer the potential for improved image quality and reduced radiation dose on the O-arm system. Further gains are possible with MBIR, including improved soft-tissue visualization, low-dose imaging protocols, and extension to methods that naturally incorporate prior information of patient anatomy and/or surgical instrumentation.

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

Regularization strategies in statistical image reconstruction of low-dose x-ray CT: A review

Statistical image reconstruction (SIR) methods have shown potential to substantially improve the image quality of low-dose x-ray computed tomography (CT) as compared to the conventional filtered back-projection (FBP) method. According to the maximum a posteriori (MAP) estimation, the SIR methods are typically formulated by an objective function consisting of two terms: (a) a data-fidelity term that models imaging geometry and physical detection processes in projection data acquisition, and (b) a regularization term that reflects prior knowledge or expectations of the characteristics of the to-be-reconstructed image. SIR desires accurate system modeling of data acquisition, while the regularization term also has a strong influence on the quality of reconstructed images. A variety of regularization strategies have been proposed for SIR in the past decades, based on different assumptions, models, and prior knowledge. In this paper, we review the conceptual and mathematical bases of these regularization strategies and briefly illustrate their efficacies in SIR of low-dose CT.

A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution

Sparse-view computed tomography (CT) holds great promise for speeding up data acquisition and reducing radiation dose in CT scans. Recent advances in reconstruction algorithms for sparse-view CT, such as iterative reconstruction algorithms, obtained high-quality image while requiring advanced computing power. Lately, deep learning (DL) has been widely used in various applications and has obtained many remarkable outcomes. In this paper, we propose a new method for sparse-view CT reconstruction based on the DL approach. The method can be divided into two steps. First, filter backprojection (FBP) was used to reconstruct the CT image from sparsely sampled sinogram. Then, the FBP results were fed to a DL neural network, which is a DenseNet and deconvolution-based network (DD-Net). The DD-Net combines the advantages of DenseNet and deconvolution and applies shortcut connections to concatenate DenseNet and deconvolution to accelerate the training speed of the network; all of those operations can greatly increase the depth of network while enhancing the expression ability of the network. After the training, the proposed DD-Net achieved a competitive performance relative to the state-of-the-art methods in terms of streaking artifacts removal and structure preservation. Compared with the other state-of-the-art reconstruction methods, the DD-Net method can increase the structure similarity by up to 18% and reduce the root mean square error by up to 42%. These results indicate that DD-Net has great potential for sparse-view CT image reconstruction.

PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

The development of computed tomography (CT) image reconstruction methods that significantly reduce patient radiation exposure, while maintaining high image quality is an important area of research in low-dose CT imaging. We propose a new penalized weighted least squares (PWLS) reconstruction method that exploits regularization based on an efficient Union of Learned TRAnsforms (PWLS-ULTRA). The union of square transforms is pre-learned from numerous image patches extracted from a dataset of CT images or volumes. The proposed PWLS-based cost function is optimized by alternating between a CT image reconstruction step, and a sparse coding and clustering step. The CT image reconstruction step is accelerated by a relaxed linearized augmented Lagrangian method with ordered-subsets that reduces the number of forward and back projections. Simulations with 2-D and 3-D axial CT scans of the extended cardiac-torso phantom and 3-D helical chest and abdomen scans show that for both normal-dose and low-dose levels, the proposed method significantly improves the quality of reconstructed images compared to PWLS reconstruction with a nonadaptive edge-preserving regularizer. PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform. We also incorporate patch-based weights in PWLS-ULTRA that enhance image quality and help improve image resolution uniformity. The proposed approach achieves comparable or better image quality compared to learned overcomplete synthesis dictionaries, but importantly, is much faster (computationally more efficient).

Multidetector Computed Tomography Imaging: Effect of Sparse Sampling and Iterative Reconstruction on Trabecular Bone Microstructure

Multidetector computed tomography-based trabecular bone microstructure analysis ensures promising results in fracture risk prediction caused by osteoporosis. Because multidetector computed tomography is associated with high radiation exposure, its clinical routine use is limited. Hence, in this study, we investigated in 11 thoracic midvertebral specimens whether trabecular texture parameters are comparable derived from (1) images reconstructed using statistical iterative reconstruction (SIR) and filtered back projection as criterion standard at different exposures (80, 150, 220, and 500 mAs) and (2) from SIR-based sparse sampling projections (12.5%, 25%, 50%, and 100%) and equivalent exposures as criterion standard. Twenty-four texture features were computed, and those that showed similar values between (1) filtered back projection and SIR at the different exposure levels and (2) sparse sampling and equivalent exposures and reconstructed with SIR were identified. These parameters can be of equal value in determining trabecular bone microstructure with lower radiation exposure using sparse sampling and SIR.

Low-dose CT reconstruction using spatially encoded nonlocal penalty

PURPOSE

Computed tomography (CT) is one of the most used imaging modalities for imaging both symptomatic and asymptomatic patients. However, because of the high demand for lower radiation dose during CT scans, the reconstructed image can suffer from noise and artifacts due to the trade-off between the image quality and the radiation dose. The purpose of this paper is to improve the image quality of quarter dose images and to select the best hyperparameters using the regular dose image as ground truth.

METHODS

We first generated the axially stacked two-dimensional sinograms from the multislice raw projections with flying focal spots using a single slice rebinning method, which is an axially approximate method to provide simple implementation and efficient memory usage. To improve the image quality, a cost function containing the Poisson log-likelihood and spatially encoded nonlocal penalty is proposed. Specifically, an ordered subsets separable quadratic surrogates (OS-SQS) method for the log-likelihood is exploited and the patch-based similarity constraint with a spatially variant factor is developed to reduce the noise significantly while preserving features. Furthermore, we applied the Nesterov's momentum method for acceleration and the diminishing number of subsets strategy for noise consistency. Fast nonlocal weight calculation is also utilized to reduce the computational cost.

RESULTS

Datasets given by the Low Dose CT Grand Challenge were used for the validation, exploiting the training datasets with the regular and quarter dose data. The most important step in this paper was to fine-tune the hyperparameters to provide the best image for diagnosis. Using the regular dose filtered back-projection (FBP) image as ground truth, we could carefully select the hyperparameters by conducting a bias and standard deviation study, and we obtained the best images in a fixed number of iterations. We demonstrated that the proposed method with well selected hyperparameters improved the image quality using quarter dose data. The quarter dose proposed method was compared with the regular dose FBP, quarter dose FBP, and quarter dose l₁ -based 3-D TV method. We confirmed that the quarter dose proposed image was comparable to the regular dose FBP image and was better than images using other quarter dose methods. The reconstructed test images of the accreditation (ACR) CT phantom and 20 patients data were evaluated by radiologists at the Mayo clinic, and this method was awarded first place in the Low Dose CT Grand Challenge.

CONCLUSION

We proposed the iterative CT reconstruction method using a spatially encoded nonlocal penalty and ordered subsets separable quadratic surrogates with the Nesterov's momentum and diminishing number of subsets. The results demonstrated that the proposed method with fine-tuned hyperparameters can significantly improve the image quality and provide accurate diagnostic features at quarter dose. The performance of the proposed method should be further improved for small lesions, and a more thorough evaluation using additional clinical data is required in the future.

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction

PURPOSE

Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach.

METHOD

We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance.

RESULTS

Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge."

CONCLUSIONS

To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research.

High-Fidelity Modeling of Detector Lag and Gantry Motion in CT Reconstruction

Detector lag and gantry motion during x-ray exposure and integration both result in azimuthal blurring in CT reconstructions. These effects can degrade image quality both for high-resolution features as well as low-contrast details. In this work we consider a forward model for model-based iterative reconstruction (MBIR) that is sufficiently general to accommodate both of these physical effects. We integrate this forward model in a penalized, weighted, nonlinear least-square style objective function for joint reconstruction and correction of these blur effects. We show that modeling detector lag can reduce/remove the characteristic lag artifacts in head imaging in both a simulation study and physical experiments. Similarly, we show that azimuthal blur ordinarily introduced by gantry motion can be mitigated with proper reconstruction models. In particular, we find the largest image quality improvement at the periphery of the field-of-view where gantry motion artifacts are most pronounced. These experiments illustrate the generality of the underlying forward model, suggesting the potential application in modeling a number of physical effects that are traditionally ignored or mitigated through pre-corrections to measurement data.

Penalized-Likelihood Reconstruction With High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging

We present a novel reconstruction algorithm based on a general cone-beam CT forward model, which is capable of incorporating the blur and noise correlations that are exhibited in flat-panel CBCT measurement data. Specifically, the proposed model may include scintillator blur, focal-spot blur, and noise correlations due to light spread in the scintillator. The proposed algorithm (GPL-BC) uses a Gaussian Penalized-Likelihood objective function, which incorporates models of blur and correlated noise. In a simulation study, GPL-BC was able to achieve lower bias as compared with deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur. In the same study, GPL-BC was able to achieve better line-pair reconstructions (in terms of segmented-image accuracy) as compared with deblurring followed by FDK, a model-based method without blur, and a model-based method with blur but not noise correlations. A prototype extremities quantitative cone-beam CT test-bench was used to image a physical sample of human trabecular bone. These data were used to compare reconstructions using the proposed method and model-based methods without blur and/or correlation to a registered CT image of the same bone sample. The GPL-BC reconstructions resulted in more accurate trabecular bone segmentation. Multiple trabecular bone metrics, including trabecular thickness (Tb.Th.) were computed for each reconstruction approach as well as the CT volume. The GPL-BC reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as compared with the CT derived value of 0.193 mm, followed by the GPL-B reconstruction, the GPL-I reconstruction, and then the FDK reconstruction (0.271 mm, 0.309 mm, and 0.335 mm, respectively).