Conventional Filtered Back Projection Research Articles

Sparse-View Neutron Computed Tomography 3-D Reconstruction via the Fast Gradient Projection Algorithm

Neutron tomography is an efficient nondestructive testing technique. As a complement to X-ray computed tomography, it has been widely used in various fields. Due to the difficulty of obtaining complete neutron projection data in a high-radiation environment and the high noise characteristics of neutron images, it is difficult to reconstruct a high-quality image using the conventional filtered-back projection (FBP) algorithm. Therefore, research on sparse-view reconstruction algorithms in neutron tomography is needed. To improve the quality of neutron three-dimensional reconstructed images, this paper proposes an algorithm that combines the Simultaneous Algebraic Reconstruction Technique (SART) with Fast Gradient Projection (FGP), where the FGP is an algorithm for image denoising and deblurring based on the discrete total variation (TV) minimization model. The algorithm proposed in this paper is compared with other algorithms (FBP, SART, and SART-TV) by simulated experimental data and real neutron experimental data. The experimental results show that the novel algorithm outperforms the other three algorithms in terms of denoising and retaining detailed structural information.

Accurate reconstruction of 4D spectral–spatial images from sparse-view data in continuous-wave EPRI

In continuous-wave electron paramagnetic resonance imaging (CW EPRI), data are collected generally at densely sampled views sufficient for achieving accurate reconstruction of a four dimensional spectral–spatial (4DSS) image by use of the conventional filtered-backprojection (FBP) algorithm. It is desirable to minimize the scan time by collection of data only at sparsely sampled views, referred to as sparse-view data. Interest thus remains in investigation of algorithms for accurate reconstruction of 4DSS images from sparse-view data collected for potentially enabling fast data acquisition in CW EPRI. In this study, we investigate and demonstrate optimization-based algorithms for accurate reconstruction of 4DSS images from sparse-view data. Numerical studies using simulated and real sparse-view data acquired in CW EPRI are conducted that reveal, in terms of image visualization and physical-parameter estimation, the potential of the algorithms developed for yielding accurate 4DSS images from sparse-view data in CW EPRI. The algorithms developed may be exploited for enabling sparse-view scans with minimized scan time in CW EPRI for yielding 4DSS images of quality comparable to, or better than, that of the FBP reconstruction from data collected at densely sampled views.

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

BackgroundCone-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.MethodsIn 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.ResultsThe 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.ConclusionsOur 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.

A novel protocol for abdominal low-dose CT scans adapted with a model-based iterative reconstruction method.

This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.

X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module.

X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy. To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov-luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model. A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments. This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error ( ), peak signal-to-noise ratio ( ), and Pearson correlation ( ) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP. This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.

Optimization of a customized simultaneous algebraic reconstruction technique algorithm for phase-contrast breast computed tomography

Objective. To introduce the optimization of a customized GPU-based simultaneous algebraic reconstruction technique (cSART) in the field of phase-contrast breast computed tomography (bCT). The presented algorithm features a 3D bilateral regularization filter that can be tuned to yield optimal performance for clinical image visualization and tissues segmentation. Approach. Acquisitions of a dedicated test object and a breast specimen were performed at Elettra, the Italian synchrotron radiation (SR) facility (Trieste, Italy) using a large area CdTe single-photon counting detector. Tomographic images were obtained at 5 mGy of mean glandular dose, with a 32 keV monochromatic x-ray beam in the free-space propagation mode. Three independent algorithms parameters were optimized by using contrast-to-noise ratio (CNR), spatial resolution, and noise texture metrics. The results obtained with the cSART algorithm were compared with conventional SART and filtered back projection (FBP) reconstructions. Image segmentation was performed both with gray scale-based and supervised machine-learning approaches. Main results. Compared to conventional FBP reconstructions, results indicate that the proposed algorithm can yield images with a higher CNR (by 35% or more), retaining a high spatial resolution while preserving their textural properties. Alternatively, at the cost of an increased image ‘patchiness’, the cSART can be tuned to achieve a high-quality tissue segmentation, suggesting the possibility of performing an accurate glandularity estimation potentially of use in the realization of realistic 3D breast models starting from low radiation dose images. Significance. The study indicates that dedicated iterative reconstruction techniques could provide significant advantages in phase-contrast bCT imaging. The proposed algorithm offers great flexibility in terms of image reconstruction optimization, either toward diagnostic evaluation or image segmentation.

Evaluation of Ultra-low-dose (ULD) Lung Computed Tomography (CT) Using Deep-learning: A Phantom Study

As an electromagnetic wave, X-rays are used to acquire diagnostic CT images. The aim of this phantom study was to evaluate the image quality of ultra-low-dose (ULD) lung computed tomography (CT) achieved using a deep-learning based image reconstruction method. The chest phantom was scanned with a tube voltage of 100 kV for various CT dose index (CTDIvol) conditions: 0.4 mGy for ultra-low-dose (ULD), 0.6 mGy for low-dose (LD), 2.7 mGy for standard (SD), and 7.1 mGy for large size (LS). The signal-to-noise ratio (SNR) and noise values in reconstructions produced via filtered back projection (FBP), iterative reconstruction (IR), and deep convolutional neural network (DCNN) were computed for comparison. The quantitative results of both the SNR and noise indicate that the adoption of the DCNN makes the image reconstruction in the ULD setting more stable and robust, achieving a higher image quality when compared with the FBP algorithm in the SD condition. Compared with the conventional FBP and IR, the proposed deep learning-based image reconstruction approach can improve the ULD CT image quality while significantly reducing the patient dose.

Usefulness of an Image-Based Noise-Reduction Technique in Cone Beam Computed Tomography for Adaptive Radiotherapy

Cone-beam computed tomography (CBCT) plays an important role in adaptive radiation therapy and can be used to observe changes in body shape, tumor size, and organ position during the treatment period. This technique can also be used to generate an image for replanning. However, performing CBCT during every image guided radiotherapy (IGRT) process increases the radiation dose. Therefore, in this study, we investigated the reduction of the CBCT exposure dose using an image-based edge-preserving noise-reduction technique. A three-dimensional cross-directional bilateral filter (3D-CDBF) which was recently developed by one of the coauthors and reported to provide a noise reduction performance better than a mode-based iterative reconstruction.Catphan was used to evaluate the performance of 3D-CDBF in CBCT. The baseline scan parameters were 120 kV, 40 mA, 40 ms, and 660 frames/360° rotation. The tube current was changed to 32, 20, and 10 mA to reduce the exposure dose. The CBCT images were reconstructed with the filtered back projection (FBP). The 3D-CDBF images were obtained by processing the FBP images. Task-based spatial resolution (task-based transfer function: TTF) and noise power spectrum (NPS) were used to evaluate image quality. Visual evaluation of the image quality was also performed by two experienced radiation oncologists and five radiologists using clinical pelvic images of two patients undergoing radiotherapy for prostate cancer. Additionally, the accuracy of the dose calculation when replanning with 3D-CDBF images was evaluated.The 50%/10% TTFs were 0.364/0.646 mm-1 using FBP and 0.362/0.645 mm-1 using 3D-CDBF, suggesting the excellent edge preservation ability of 3D-CDBF. The noise power of 3D-CDBF compared to FBP were 25.4% at 0.2 mm-1 and 17.0% at 0.5 mm-1. 3D-CDBF notably reduced noise power over the entire frequency range. Therefore, the NPS level of 10 mA became similar to that of 40 mA. The visual evaluation allowed by the 3D-CDBF images was considerably higher than that realized with other images. Additionally, dose evaluation by gamma analysis showed that 3D-CDBF had no effect on the accuracy of the dose calculation.Compared with conventional FBP, the 3D-CDBF yielded significant improvements in quantitative and qualitative image qualities. The results indicated a possible 75% dose reduction of this method, while preserving the accuracy of the dose calculation.

Optimisation of the event-based TOF filtered back-projection for online imaging in total-body J-PET

We perform a parametric study of the newly developed time-of-flight (TOF) image reconstruction algorithm, proposed for the real-time imaging in total-body Jagiellonian PET (J-PET) scanners. The asymmetric 3D filtering kernel is applied at each most likely position of electron-positron annihilation, estimated from the emissions of back-to-back γ-photons. The optimisation of its parameters is studied using Monte Carlo simulations of a 1-mm spherical source, NEMA IEC and XCAT phantoms inside the ideal J-PET scanner. The combination of high-pass filters which included the TOF filtered back-projection (FBP), resulted in spatial resolution, 1.5 times higher in the axial direction than for the conventional 3D FBP. For realistic 10-minute scans of NEMA IEC and XCAT, which require a trade-off between the noise and spatial resolution, the need for Gaussian TOF kernel components, coupled with median post-filtering, is demonstrated. The best sets of 3D filter parameters were obtained by the Nelder-Mead minimisation of the mean squared error between the resulting and reference images. The approach allows training the reconstruction algorithm for custom scans, using the IEC phantom, when the temporal resolution is below 50 ps. The image quality parameters, estimated for the best outcomes, were systematically better than for the non-TOF FBP.

A Voxel-Based Assessment of Noise Properties in Computed Tomography Imaging with the ASiR-V and ASiR Iterative Reconstruction Algorithms

Given the inherent characteristics of nonlinearity and nonstationarity of iterative reconstruction algorithms in computed tomography (CT) imaging, this study aimed to perform, for the first time, a voxel-based characterization of noise properties in CT imaging with the ASiR-V and ASiR algorithms as compared with conventional filtered back projection (FBP). Multiple repeated scans of the Catphan-504 phantom were carried out. CT images were reconstructed using FBP and ASiR/ASiR-V with different blending levels of reconstruction (20%, 40%, 60%, 80%, 100%). Noise maps and their nonuniformity index (NUI) were obtained according to the approach proposed by the report of AAPM TG-233. For the homogeneous CTP486 module, ASiR-V/ASiR allowed a noise reduction of up to 63.7%/52.9% relative to FBP. While the noise reduction values of ASiR-V-/ASiR-reconstructed images ranged up to 33.8%/39.9% and 31.2%/35.5% for air and Teflon contrast objects, respectively, these values were approximately 60%/50% for other contrast objects (PMP, LDPE, polystyrene, acrylic, Delrin). Moreover, for all contrast objects but air and Teflon, ASiR-V showed a greater noise reduction potential than ASiR when the blending level was ≥40%. While noise maps of the homogenous CTP486 module showed only a slight spatial variation of noise (NUI &lt; 5.2%) for all reconstruction algorithms, the NUI values of iterative-reconstructed images of the nonhomogeneous CTP404 module increased nonlinearly with blending level and were 19%/15% and 6.7% for pure ASiR-V/ASiR and FBP, respectively. Overall, these results confirm the potential of ASiR-V and ASiR in reducing noise as compared with conventional FBP, suggesting, however, that the use of pure ASiR-V or ASiR might be suboptimal for specific clinical applications.

Optimal quadrature formulas for non-periodic functions in Sobolev space and its application to CT image reconstruction

In the present paper, optimal quadrature formulas in the sense of Sard are constructed for numerical integration of the integral ?ba e2?i?x?(x)dx with ? ? R in the Sobolev space L(m)2 [a,b] of complexvalued functions which are square integrable with m-th order derivative. Here, using the discrete analogue of the differential operator d2m/dx2m, the explicit formulas for optimal coefficients are obtained. The order of convergence of the obtained optimal quadrature formula is O(hm). As an application, we implement the filtered back-projection (FBP) algorithm, which is a well-known image reconstruction algorithm for computed tomography (CT). By approximating Fourier transforms and its inversion using the proposed optimal quadrature formula of the second and third orders , we observe that the accuracy of the reconstruction algorithm is improved. In numerical experiments, we compare the quality of the reconstructed image obtained by using the proposed optimal quadrature formulas with the conventional FBP, in which fast Fourier transform is used for the calculation of Fourier transform and its inversion. In the noise test, the proposed algorithm provides more reliable results against the noise than the conventional FBP.

Image improvement in digital tomosynthesis (DTS) using a deep convolutional neural network

Digital tomosynthesis (DTS) is a geometric tomography technique using a limited-angle scan. It has been popularly used in both medical and industrial x-ray imaging applications. DTS provides the tomographic benefits of computed tomography with reduced dose and time. However, conventional DTS reconstruction based on the computationally cheap filtered back-projection (FBP) method typically produces poor image quality due to limited angular samplings. To overcome these difficulties, iterative reconstruction methods are often used in DTS reconstruction as they have the potential to provide multiplanar images of higher quality than conventional FBP-based methods. However, they require enormous computational cost in the iterative process, which remains an obstacle to practical applications. In this study, we propose a method for effectively reducing limited-angle artifacts in conventional FBP reconstruction, using a state-of-the-art deep learning scheme with a convolutional neural network. Our results indicate that the proposed DTS reconstruction method effectively minimized limited-angle artifacts, thus improving image performance in DTS, and that further it provided good image quality in both sagittal and coronal views (as in computed tomography) as well as in axial view.

Fast Enhanced CT Metal Artifact Reduction Using Data Domain Deep Learning

Filtered back projection (FBP) is the most widely used method for image reconstruction in X-ray computed tomography (CT) scanners, and can produce excellent images in many cases. However, the presence of dense materials, such as metals, can strongly attenuate or even completely block X-rays, producing severe streaking artifacts in the FBP reconstruction. These metal artifacts can greatly limit subsequent object delineation and information extraction from the images, restricting their diagnostic value. This problem is particularly acute in the security domain, where there is great heterogeneity in the objects that can appear in a scene, highly accurate decisions must be made quickly, and processing time is highly constrained. The standard practical approaches to reducing metal artifacts in CT imagery are either simplistic nonadaptive interpolation-based projection data completion methods or direct image post-processing methods. These standard approaches have had limited success. Motivated primarily by security applications, we present a new deep-learning-based metal artifact reduction approach that tackles the problem in the projection data domain. We treat the projection data corresponding to dense, metal objects as missing data and train an adversarial deep network to complete the missing data directly in the projection domain. The subsequent complete projection data is then used with conventional FBP to reconstruct an image intended to be free of artifacts. This new approach results in an end-to-end metal artifact reduction algorithm that is computationally efficient textcolorredand therefore practical and fits well into existing CT workflows allowing easy adoption in existing scanners. Training deep networks can be challenging, and another contribution of our work is to demonstrate that training data generated using an accurate X-ray simulation can be used to successfully train the deep network, when combined with transfer learning using limited real data sets. We demonstrate the effectiveness and potential of our algorithm on simulated and real examples.

Statistical image reconstruction for high-throughput thermal Neutron Computed Tomography

Neutron Computed Tomography (CT) is an increasingly utilised non-destructive analysis tool in material science, palaeontology, and cultural heritage. With the development of new neutron imaging facilities (such as DINGO, ANSTO, Australia) new opportunities arise to maximise their performance through the implementation of statistically driven image reconstruction methods which have yet to see wide scale application in neutron transmission tomography. This work outlines the implementation of a convex algorithm statistical image reconstruction framework applicable to the geometry of most neutron tomography instruments with the aim of obtaining similar imaging quality to conventional ramp filtered back-projection via the inverse Radon transform, but using a lower number of measured projections to increase object throughput. Through comparison of the output of these two frameworks using a tomographic scan of a known 3 material cylindrical phantom obtain with the DINGO neutron radiography instrument (ANSTO, Australia), this work illustrates the advantages of statistical image reconstruction techniques over conventional filter back-projection. It was found that the statistical image reconstruction framework was capable of obtaining image estimates of similar quality with respect to filtered back-projection using only 12.5% the number of projections, potentially increasing object throughput at neutron imaging facilities such as DINGO eight-fold.

4D digital tomosynthesis image reconstruction using brute force-based adaptive total variation (BF-ATV) in a prototype LINAC system

Respiratory-correlated cone-beam CT (CBCT) not only inhibits rapid scanning due to the slow speed of the LINAC head gantry rotation, but its implementation for routine patient imaging is impractical because of the high radiation dose delivered during the process. Digital tomosynthesis (DTS) is a potentially faster technique that delivers a much lower radiation dose by reducing the number of projections in a limited angular range. Unfortunately, 4D-DTS introduces strong aliasing artifacts in the reconstructed images due to the sparsely sampled projections in each respiratory phase bin. The authors hereby suggest a novel low-dose 4D-DTS image reconstruction method that achieves a compromise between the occurrence of aliasing artifacts and image smoothing using a brute force-based adaptive weighting parameter searching technique. We used a prototype LINAC system mounted with a flat-panel detector to acquire tomosynthesis projections of respiratory motion in a phantom in the anterior–posterior (AP) and lateral views. Three different 4D-DTS image reconstruction schemes that included conventional filtered back-projection (FBP), adaptive steepest descent projection onto convex sets (ASD-POCS), and the proposed brute force-based adaptive total variation (BF-ATV) were implemented in four different respiratory phase bins for both AP and lateral views. All reconstructions were accelerated using a single GPU card to reduce the computation time. To study the performance of the algorithm under various sparse conditions, we operated the prototype system in three different gantry sweep modes. The results indicate that the proposed BF-ATV method yields the largest structural similarities in the differenced image between the ground-truth dataset acquired using the slow gantry sweep mode and the sparse dataset from both moderate and fast sweep modes. In addition, the proposed method maintained the object sharpness with less streaking lines and small loss of sharpness compared to the conventional FBP and ASD-POCS methods. In conclusion, the proposed low-dose 4D-DTS reconstruction scheme may provide better performance due in part to its rapid scanning. Therefore, it is potentially applicable to practical 4D imaging for radiotherapy.

Dual-Energy and Iterative Metal Artifact Reduction for Reducing Artifacts Due to Metallic Hardware: A Loosening Hip Phantom Study.

OBJECTIVE. The purpose of this study was to compare the value of iterative metal artifact reduction (IMAR) with that of dual-energy CT (DECT) and filtered back projection (FBP) CT protocols for reducing metal artifacts and for facilitating visualization of the acetabular cortex in a loosening hip phantom model. MATERIALS AND METHODS. CT scans were obtained with conventional FBP and dual-source CT for two types of hip phantom. For the quantitative study, attenuation was measured by placement of ROIs in the phantoms around the metallic hardware. The differences between mean attenuation in each ROI and the actual attenuation were compared among the three CT protocols. For the qualitative study, the visibility of the acetabular cortex in the artificial loosening area of the total hip arthroplasty model and in the joint space of the bipolar hemiarthroplasty model was evaluated by measurement of the obscured cortical angle. RESULTS. In the quantitative study, attenuation differences in the bipolar hemiarthroplasty model were markedly decreased with IMAR and DECT compared with FBP (p = 0.006-0.007). In the total hip replacement model, attenuation differences were significantly lower with IMAR than with FBP (p < 0.001). In the qualitative study, visibility of the acetabular cortex was markedly improved with IMAR compared with DECT and FBP (p < 0.001) for both hip models. CONCLUSION. CT with IMAR can reduce the distortion caused by metal artifacts more effectively than FBP and DECT can while preserving visibility of the acetabular cortex in both bipolar hemiarthroplasty and total hip arthroplasty phantoms.

Task-Based Evaluation of Image Reconstruction Methods for Defect Detection and Radiation Dose Reduction in Myocardial Perfusion SPECT

We evaluated the ability of conventional and corrective image reconstruction methods to detect a myocardial perfusion defect in single-photon emission computed tomography (SPECT) images using the channelized hotelling observer (CHO). Using our previously developed 4-D extended cardiac-torso, we simulated realistic transmural and endocardial perfusion defects in various locations and sizes in the myocardium. Almost noise-free projection datasets were generated separately from the heart, blood pool, lungs, liver, kidneys, stomach, gall bladder, and the remaining body using Monte Carlo simulation techniques that included the effects of collimator detector response, photon attenuation, and scatter. The datasets were then scaled and combined to model 99mTc Sestamibi myocardial perfusion SPECT projection datasets from a typical patient with various reduced count levels, simulating reduced radiation doses. The final projection data were reconstructed using the 3-D filtered back-projection (FBP) without correction and a 3-D ordered-subset expectation-maximization (OS-EM) method with corrections of attenuation, collimator-detector response, and scatter (ADS), followed by smoothing filtering with several different cut-off frequencies. Task-based evaluations on the reconstructed images were performed by using the CHO followed by the receiver operating characteristics (ROC) methodology to evaluate the detectability of a myocardial perfusion defect by the two image reconstruction methods for various defect anatomies. Areas under the ROC curve (AUC) were computed to assess the changes in the detection of myocardial perfusion defects. The results showed that the 3-D OS-EM with ADS correction gave overall higher AUC values than FBP at several post smoothing levels, count levels, and myocardial perfusion defect sizes. The difference in AUC increased as image smoothness decreased, where the 3-D OS-EM with correction was able to provide similar AUC values with 20%–40% reduction in count levels compared to those of FBP. The AUC values for smaller myocardial perfusion defects were lower for both reconstruction methods with smaller differences. We concluded that the 3-D OS-EM with ADS correction provides higher performance in detecting myocardial perfusion defects. At the conventional count level, it allows for less counts or lower radiation doses without loss of defect detection in myocardial perfusion SPECT images compared to the conventional FBP method, especially regarding less smoothed images.

Ultra-High Resolution Photon-Counting Detector CT Reconstruction using Spectral Prior Image Constrained Compressed-Sensing (UHR-SPICCS).

Photon-counting detector based CT (PCD-CT) enables dose efficient high resolution imaging, in addition to providing multi-energy information. This allows better delineation of anatomical structures crucial for several clinical applications ranging from temporal bone imaging to pulmonary nodule visualization. Due to the smaller detector pixel sizes required for high resolution imaging, the PCD-CT images suffer from higher noise levels. The image quality is further degraded in narrow energy bins as a consequence of low photon counts. This limits the potential benefits that high-resolution PCD-CT could offer. Conventional reconstruction techniques such as the filtered back projection (FBP) have poor performance when reconstructing noisy CT projection data. To enable low noise multi-energy reconstructions, we employed a spectral prior image constrained compressed sensing (SPICCS) framework that exploits the spatio-spectral redundancy in the multi-energy acquisitions. We demonstrated noise reduction in narrow energy bins without losing energy-specific attenuation information and spatial resolution. We scanned an anthropomorphic head phantom, and a euthanized pig using our whole-body prototype PCD-CT system in the ultra-high resolution mode at 120kV. Image reconstructions were performed using SPICCS and compared with conventional FBP. Noise reduction of 18 to 46% was noticed in narrow energy bins corresponding to 25 - 65 keV and 65 - 12 keV, while the mean CT number was preserved. Spatial resolution measurement showed similar modulation transfer function (MTF) values between FBP and SPICCS, demonstrating preservation of spatial resolution.

Low-Dose CT Perfusion of the Liver using Reconstruction of Difference.

Liver CT perfusion (CTP) is used in the detection, staging, and treatment response analysis of hepatic diseases. Unfortunately, CTP radiation exposures is significant, limiting more widespread use. Traditional CTP data processing reconstructs individual temporal samples, ignoring a large amount of shared anatomical information between temporal samples, suggesting opportunities for improved data processing. We adopt a prior-image-based reconstruction approach called Reconstruction of Difference (RoD) to enable low-exposure CTP acquisition. RoD differs from many algorithms by directly estimating the attenuation changes between the current patient state and a prior CT volume. We propose to use a high-fidelity unenhanced baseline CT image to integrate prior anatomical knowledge into subsequent data reconstructions. Using simulation studies based on a 4D digital anthropomorphic phantom with realistic time-attenuation curves, we compare RoD with conventional filtered-backprojection, penalized-likelihood estimation, and prior image penalized-likelihood estimation. We evaluate each method in comparisons of reconstructions at individual time points, accuracy of estimated time-attenuation curves, and in an analysis of common perfusion metric maps including hepatic arterial perfusion, hepatic portal perfusion, perfusion index, and time-to-peak. Results suggest that RoD enables significant exposure reductions, outperforming standard and more sophisticated model-based reconstruction, making RoD a potentially important tool to enable low-dose liver CTP.

Maximum-Likelihood Expectation-Maximization Algorithm Versus Windowed Filtered Backprojection Algorithm: A Case Study

Filtered backprojection (FBP) algorithms reduce image noise by smoothing the image. Iterative algorithms reduce image noise by noise weighting and regularization. It is believed that iterative algorithms are able to reduce noise without sacrificing image resolution, and thus iterative algorithms, especially maximum-likelihood expectation maximization (MLEM), are used in nuclear medicine to replace FBP algorithms. Methods: This short paper uses counter examples to show that this belief is not true. We compare image noise variance for FBP and MLEM reconstructions having the same spatial resolution. Results: The truth is that although MLEM suppresses image noise, it does so by sacrificing image resolution as well; the performance of windowed FBP may be better than that of MLEM in our case study. Conclusion: The myth of the superiority of iterative algorithms is caused by comparing them with conventional FBP instead of with windowed FBP. However, we do not intend to generalize the comparison results to imply which algorithm is more favorable.