Streak Artifacts Research Articles

Streak artifact suppressed back projection for sparse-view photoacoustic computed tomography.

The development of fast and accurate image reconstruction algorithms under constrained data acquisition conditions is important for photoacoustic computed tomography (PACT). Sparse-view measurements have been used to accelerate data acquisition and reduce system complexity; however, reconstructed images suffer from sparsity-induced streak artifacts. In this paper, a modified back-projection (BP) method termed anti-streak BP is proposed to suppress streak artifacts in sparse-view PACT reconstruction. During the reconstruction process, the anti-streak BP finds the back-projection terms contaminated by high-intensity sources with an outlier detection method. Then, the weights of the contaminated back-projection terms are adaptively adjusted to eliminate the effects of high-intensity sources. The proposed anti-streak BP method is compared with the conventional BP method on both simulation and in vivo data. The anti-streak BP method shows substantially fewer artifacts in the reconstructed images, and the streak index is 54% and 20% lower than that of the conventional BP method on simulation and in vivo data, when the transducer number N=128. The anti-streak BP method is a powerful improvement of the BP method with the ability of artifact suppression.

Association between Intracanal Medicament Radiopacity and Streak Artifact Production Using Cone-beam Computed Tomography: A Laboratory Study

IntroductionThis study aimed to calculate the correlation between the radiopacity levels of various intracanal medicaments and radiolucent streak formation using cone-beam computed tomography (CBCT). MethodsSeven commercially-available intracanal medicaments were tested, which contained different amounts of radiopacifier [Consepsis, Ca(OH)2, UltraCal XS, Calmix, Odontopaste, Odontocide, and Diapex Plus]. Their radiopacity levels were measured according to the International Organization for Standardization 13116 testing standards (mmAl). Subsequently, the medicaments were placed in 3 canals of radiopaque artificial printed maxillary molars (n = 15 roots per medicament), leaving the second mesiobuccal canal empty. CBCT imaging was carried out using an Orthophos SL 3-dimensional scanner under recommended manufacturer exposure settings. Radiopaque streak formation was assessed by a calibrated examiner using a previously published grading system (0–3). The Kruskal-Wallis and Mann-Whitney U tests with and without Bonferroni correction were used to compare radiopacity levels and radiopaque streak scores for the medicaments. Their relationship was assessed using the Pearson correlation coefficient. The level of significance was set as (α = 0.05). ResultsDiapex plus presented with the highest radiopacity levels (4.98 ± 0.01) and radiopaque streak scores [middle third (2.8 ± 0.18); apical third (2.73 ± 0.43)]; which was similar to UltraCal XS's radiopaque streak scores [middle third (2.8 ± 0.92); apical third (2.73 ± 0.77)]. Consepsis had the lowest radiopacity levels (0.12 ± 0.05), followed by Odontocide (0.60 ± 0.05). Consepsis and Ca(OH)2 were scored 0 for artifacts in all roots at all levels. A high positive correlation (R = 0.95) was found between radiopacity and streak formation. ConclusionsThe radiopacity of intracanal medicaments varies and strongly correlates with the formation of radiolucent streak artifacts during CBCT.

DIDR-Net: a sparse-view CT deep iterative reconstruction network with an independent detail recovery network

Sparse-view CT is an effective method to reduce X-ray radiation dose in clinical CT imaging. However, sparse view image reconstruction is still challenging due to highly undersampled data. To this end, we propose a new deep learning-based reconstruction model of CT images, called DIDR-Net. Unlike existing methods, DIDR-Net employs a dual network structure, including an iterative reconstruction sub-network and a detail recovery sub-network. The iterative reconstruction sub-network expands the FISTA (Fast Iterative Soft Thresholding Algorithm) into a deep network, utilizing learnable nonlinear sparse transform and shrinkage thresholding to improve reconstruction performance. To avoid loss of image details while removing artifacts, we design a detail recovery sub-network. Specifically, this sub-network captures the local details and global information of the initial image through local and global branches, and adaptively fuses the results of these two branches through a fusion module. DIDR-Net generates both the initial reconstruction map and the detail feature map in a parallel manner, and finally fuses the two sides to reconstruct a high-quality CT image. Experimental results on the AAPM public dataset show that DIDR-Net exhibits better performance in both streak artifact removal and detail structure preservation compared to other advanced reconstruction algorithms.

Four-Dimensional Cone Beam CT Imaging Using a Single Routine Scan via Deep Learning.

A novel method is proposed to obtain four-dimensional (4D) cone-beam computed tomography (CBCT) images from a routine scan in patients with upper abdominal cancer. The projections are sorted according to the location of the lung diaphragm before being reconstructed to phase-sorted data. A multiscale-discriminator generative adversarial network (MSD-GAN) is proposed to alleviate the severe streaking artifacts in the original images. The MSD-GAN is trained using simulated CBCT datasets from patient planning CT images. The enhanced images are further used to estimate the deformable vector field (DVF) among breathing phases using a deformable image registration method. The estimated DVF is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction approach to generate 4D CBCT images. The proposed MSD-GAN is compared with U-Net on the performance of image enhancement. Results show that the proposed method significantly outperforms the total variation regularization-based iterative reconstruction approach and the method using only MSD-GAN to enhance original phase-sorted images in simulation and patient studies on 4D reconstruction quality. The MSD-GAN also shows higher accuracy than the U-Net. The proposed method enables a practical way for 4D-CBCT imaging from a single routine scan in upper abdominal cancer treatment including liver and pancreatic tumors.

Assessing the Effects of Deep Learning Reconstruction on Abdominal CT Without Arm Elevation.

Purpose: To evaluate the effects of deep learning reconstruction (DLR) on image quality of abdominal computed tomography (CT) in patients without arm elevation compared with hybrid-iterative reconstruction (Hybrid-IR) and filtered back projection (FBP). Methods: In this retrospective study, axial images of 26 patients who underwent CT without arm elevation were reconstructed using DLR, Hybrid-IR, and FBP. Streak artifact index (SAI) was calculated by dividing the standard deviation of CT attenuation in the liver or spleen by that in fat. Two other blinded radiologists evaluated streak artifacts on images (in the liver, spleen, and kidney), depiction of liver vessels, subjective image noise, and overall quality. They were also asked to detect space-occupying lesions other than cysts in the liver, spleen, and kidney. Results: The SAI (liver/spleen) in DLR images was significantly reduced compared with Hybrid-IR and FBP. Regarding qualitative image analysis, streak artifacts in the 3 organs, qualitative image noise, and overall quality in DLR images were rated by both readers as significantly improved compared with Hybrid-IR (P ≤ .012) and FBP (P < .001). Both blinded readers detected more lesions on DLR images than on Hybrid-IR and FBP ones. Conclusion: DLR resulted in significantly better-quality abdominal CT images in patients scanned without elevating their arms with reducing streak artifacts compared with Hybrid-IR and FBP.

Fat mitigation strategies to improve image quality of radial 4D flow MRI in obese subjects.

This study addresses the challenges in obtaining abdominal 4D flow MRI of obese patients. We aimed to evaluate spectral saturation and inner volume excitation as methods to mitigating artifacts originating from adipose signals, with the goal of enhancing image quality and improving quantification. Radial 4D flow MRI acquisitions with fat mitigation (inner volume excitation [IVE] and intermittent fat saturation [FS]) were compared to a standard slab selective excitation (SSE) in a test-retest study of 15 obese participants. IVE selectively excited a cylindrical region of interest, avoiding contamination from peripheral adipose tissue, while FS globally suppressed fat based on spectral selection. Acquisitions were evaluated qualitatively based on expert ratings and quantitatively based on conservation of mass, test-retest repeatability, and a divergence free quality metric. Errors were evaluated statistically using the absolute and relative errors, regression, and Bland-Altman analysis. IVE demonstrated superior performance quantitatively in the conservation of mass analysis in the portal vein, with higher correlation and lower bias in regression analysis. IVE also produced flow fields with the lowest divergence error and was rated best in overall image quality, delineating small vessels, and producing the least streaking artifacts. Evaluation results did not differ significantly between FS and SSE. Test-retest reproducibility was similarly high for all sequences, with data suggesting biological variations dominate the technical variability. IVE improved hemodynamic assessment of radial 4D flow MRI in the abdomen of obese participants while FS did not lead to significant improvements in image quality or flow metrics.

An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction

X-ray Computed Tomography (CT) techniques play a vitally important role in clinical diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse-view CT reduces the impact of radioactivity on the human body through sparsely sampled projections. However, images reconstructed from sparse-view sinograms often suffer from serious streaking artifacts. To overcome this issue, we propose an end-to-end attention-based mechanism deep network for image correction in this paper. Firstly, the process is to reconstruct the sparse projection by the filtered back-projection algorithm. Next, the reconstructed results are fed into the deep network for artifact correction. More specifically, we integrate the attention-gating module into U-Net pipelines, whose function is implicitly learning to emphasize relevant features beneficial for a given assignment while restraining background regions. Attention is used to combine the local feature vectors extracted at intermediate stages in the convolutional neural network and the global feature vector extracted from the coarse scale activation map. To improve the performance of our network, we fused a pre-trained ResNet50 model into our architecture. The model was trained and tested using the dataset from The Cancer Imaging Archive (TCIA), which consists of images of various human organs obtained from multiple views. This experience demonstrates that the developed functions are highly effective in removing streaking artifacts while preserving structural details. Additionally, quantitative evaluation of our proposed model shows significant improvement in peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean squared error (RMSE) metrics compared to other methods, with an average PSNR of 33.9538, SSIM of 0.9435, and RMSE of 45.1208 at 20 views. Finally, the transferability of the network was verified using the 2016 AAPM dataset. Therefore, this approach holds great promise in achieving high-quality sparse-view CT images.

Deep learning-based reconstruction can improve the image quality of low radiation dose head CT.

To evaluate the image quality of deep learning-based reconstruction (DLR), model-based (MBIR), and hybrid iterative reconstruction (HIR) algorithms for lower-dose (LD) unenhanced head CT and compare it with those of standard-dose (STD) HIR images. This retrospective study included 114 patients who underwent unenhanced head CT using the STD (n = 57) or LD (n = 57) protocol on a 320-row CT. STD images were reconstructed with HIR; LD images were reconstructed with HIR (LD-HIR), MBIR (LD-MBIR), and DLR (LD-DLR). The image noise, gray and white matter (GM-WM) contrast, and contrast-to-noise ratio (CNR) at the basal ganglia and posterior fossa levels were quantified. The noise magnitude, noise texture, GM-WM contrast, image sharpness, streak artifact, and subjective acceptability were independently scored by three radiologists (1 = worst, 5 = best). The lesion conspicuity of LD-HIR, LD-MBIR, and LD-DLR was ranked through side-by-side assessments (1 = worst, 3 = best). Reconstruction times of three algorithms were measured. The effective dose of LD was 25% lower than that of STD. Lower image noise, higher GM-WM contrast, and higher CNR were observed in LD-DLR and LD-MBIR than those in STD (all, p ≤ 0.035). Compared with STD, the noise texture, image sharpness, and subjective acceptability were inferior for LD-MBIR and superior for LD-DLR (all, p < 0.001). The lesion conspicuity of LD-DLR (2.9 ± 0.2) was higher than that of HIR (1.2 ± 0.3) and MBIR (1.8 ± 0.4) (all, p < 0.001). Reconstruction times of HIR, MBIR, and DLR were 11 ± 1, 319 ± 17, and 24 ± 1s, respectively. DLR can enhance the image quality of head CT while preserving low radiation dose level and short reconstruction time. •For unenhanced head CT, DLR reduced the image noise and improved the GM-WM contrast and lesion delineation without sacrificing the natural noise texture and image sharpness relative to HIR. •The subjective and objective image quality of DLR was better than that of HIR even at 25% reduced dose without considerably increasing the image reconstruction times (24s vs. 11s). •Despite the strong noise reduction and improved GM-WM contrast performance, MBIR degraded the noise texture, sharpness, and subjective acceptance with prolonged reconstruction times relative to HIR, potentially hampering its feasibility.

Structured Illumination Microscopy of Mitochondrial in Mouse Hepatocytes with an Improved Image Reconstruction Algorithm.

In this paper, a structured illumination microscopy (SIM) image reconstruction algorithm combined with notch function (N-SIM) is proposed. This method suppresses the defocus signal in the imaging process by processing the low-frequency signal of the image. The existing super-resolution image reconstruction algorithm produces streak artifacts caused by defocus signal. The experimental results show that the algorithm proposed in our study can well suppress the streak artifacts caused by defocused signals during the imaging process without losing the effective information of the image. The image reconstruction algorithm is used to analyze the mouse hepatocytes, and the image processing tool developed by MATLAB is applied to identify, detect and count the reconstructed images of mitochondria and lipid droplets, respectively. It is found that the mitochondrial activity in oxidative stress induced growth inhibitor 1 (OSGIN1) overexpressed mouse hepatocytes is higher than that in normal cells, and the interaction with lipid droplets is more obvious. This paper provides a reliable subcellular observation platform, which is very meaningful for biomedical work.

Attention-based dual-branch deep network for sparse-view computed tomography image reconstruction.

The widespread application of X-ray computed tomography (CT) imaging in medical screening makes radiation safety a major concern for public health. Sparse-view CT is a promising solution to reduce the radiation dose. However, the reconstructed CT images obtained using sparse-view CT may suffer severe streaking artifacts and structural information loss. In this study, a novel attention-based dual-branch network (ADB-Net) is proposed to solve the ill-posed problem of sparse-view CT image reconstruction. In this network, downsampled sinogram input is processed through 2 parallel branches (CT branch and signogram branch) of the ADB-Net to independently extract the distinct, high-level feature maps. These feature maps are fused in a specified attention module from 3 perspectives (channel, plane, and spatial) to allow complementary optimizations that can mitigate the streaking artifacts and the structure loss in sparse-view CT imaging. Numerical simulations, an anthropomorphic thorax phantom, and in vivo preclinical experiments were conducted to verify the sparse-view CT imaging performance of the ADB-Net. The proposed network achieved a root-mean-square error (RMSE) of 20.6160, a structural similarity (SSIM) of 0.9257, and a peak signal-to-noise ratio (PSNR) of 38.8246 on numerical data. The visualization results demonstrate that this newly developed network can consistently remove the streaking artifacts while maintaining the fine structures. The proposed attention-based dual-branch deep network, ADB-Net, provides a promising alternative to reconstruct high-quality sparse-view CT images for low-dose CT imaging.

A machine learning supported sinogram interpolation method for X-ray computed tomography

The time of an X-ray Computed Tomography (XCT) measurement is directly affected by the number of acquired X-ray projections. In general, a large number of X-ray projections, hence a large acquisition time is required to obtain a qualitative reconstruction and successive feature analysis. To expand the applicability of XCT towards low-end parts we propose a novel sinogram interpolation method that incorporates the object rotation. The method combines a forward projection model of the XCT system and a deep learning regression model to generate intermediate X-ray projections for XCT scans with a reduced number of projections. Thereby, the accompanying reconstruction artefacts can be reduced while preserving a lower acquisition time. The method is validated on simulated projections of the 3D Shepp-Logan phantom and simulated projections of real 3D printed components using the ASTRA-toolbox. Within the reconstructions, less streaking artefacts are observed and an increased contrast between different features is obtained without introducing rotational artefacts.

Influence of Scan Parameters of Single and Dual-Energy CT Protocols in Combination with Metal Artifact Suppression Algorithms for THA: An ex Vivo Study.

Metal artifacts caused by hip arthroplasty stems limit the diagnostic value of computed tomography (CT) in the evaluation of periprosthetic fractures or implant loosening. The aim of this ex vivo study was to evaluate the influence of different scan parameters and metal artifact algorithms on image quality in the presence of hip stems. Nine femoral stems, 6 uncemented and 3 cemented, that had been implanted in subjects during their lifetimes were exarticulated and investigated after death and anatomical body donation. Twelve CT protocols consisting of single-energy (SE) and single-source consecutive dual-energy (DE) scans with and without an iterative metal artifact reduction algorithm (iMAR; Siemens Healthineers) and/or monoenergetic reconstructions were compared. Streak and blooming artifacts as well as subjective image quality were evaluated for each protocol. Metal artifact reduction with iMAR significantly reduced the streak artifacts in all investigated protocols (p = 0.001 to 0.01). The best subjective image quality was observed for the SE protocol with a tin filter and iMAR. The least streak artifacts were observed for monoenergetic reconstructions of 110, 160, and 190 keV with iMAR (standard deviation of the Hounsfield units: 151.1, 143.7, 144.4) as well as the SE protocol with a tin filter and iMAR (163.5). The smallest virtual growth was seen for the SE with a tin filter and without iMAR (4.40 mm) and the monoenergetic reconstruction of 190 keV without iMAR (4.67 mm). This study strongly suggests that metal artifact reduction algorithms (e.g., iMAR) should be used in clinical practice for imaging of the bone-implant interface of prostheses with either an uncemented or cemented femoral stem. Among the iMAR protocols, the SE protocol with 140 kV and a tin filter produced the best subjective image quality. Furthermore, this protocol and DE monoenergetic reconstructions of 160 and 190 keV with iMAR achieved the lowest levels of streak and blooming artifacts. Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Evaluation of Perfusion Change According to Pancreatic Cancer and Pancreatic Duct Dilatation Using Free-Breathing Golden-Angle Radial Sparse Parallel (GRASP) Magnetic Resonance Imaging

To evaluate perfusion changes in the pancreas with pancreatic cancer and pancreatic duct dilatation using dynamic contrast-enhanced MRI (DCE-MRI). We evaluate the pancreas DCE-MRI of 75 patients. The qualitative analysis includes pancreas edge sharpness, motion artifacts, streak artifacts, noise, and overall image quality. The quantitative analysis includes measuring the pancreatic duct diameter and drawing six regions of interest (ROIs) in the three areas of the pancreas (head, body, and tail) and three vessels (aorta, celiac axis, and superior mesenteric artery) to measure the peak-enhancement time, delay time, and peak concentration. We evaluate the differences in three quantitative parameters among the ROIs and between patients with and without pancreatic cancer. The correlations between pancreatic duct diameter and delay time are also analyzed. The pancreas DCE-MRI demonstrates good image quality, and respiratory motion artifacts show the highest score. The peak-enhancement time does not differ among the three vessels or among the three pancreas areas. The peak-enhancement time and concentrations in the pancreas body and tail and the delay time in the three pancreas areas are significantly longer (p < 0.05) in patients with pancreatic cancer than in those without pancreatic cancer. The delay time was significantly correlated with the pancreatic duct diameters in the head (p < 0.02) and body (p < 0.001). DCE-MRI can display the perfusion change in the pancreas with pancreatic cancer. A perfusion parameter in the pancreas is correlated with the pancreatic duct diameter reflecting a morphological change in the pancreas.

Multi-material blind beam hardening correction in near real-time based on non-linearity adjustment of projections

Beam hardening (BH) is one of the major artifacts that severely reduces the quality of computed tomography (CT) imaging. This BH artifact arises due to the polychromatic nature of the X-ray source and causes cupping and streak artifacts. This work aims to propose a fast and accurate BH correction method that requires no prior knowledge of the materials and corrects first and higher-order BH artifacts. This is achieved by performing a wide sweep of the material based on an experimentally measured look-up table to obtain the closest estimate of the material. Then, the non-linearity effect of the BH is corrected by adding the difference between the estimated monochromatic and the polychromatic simulated projections of the segmented image. The estimated polychromatic projection is accurately derived using the least square estimation (LSE) method by minimizing the difference between the experimental projection and the linear combination of simulated polychromatic projections. As a result, an accurate non-linearity correction term is derived that leads to an accurate BH correction result. The simulated projections in this work are performed using a multi-GPU-accelerated forward projection model which ensures a fast BH correction in near real-time. To evaluate the proposed BH correction method, we have conducted extensive experiments on real-world CT data. It is shown that the proposed method results in images with improved contrast-to-noise ratio (CNR) in comparison to the images corrected from only the scatter artifacts and the BH-corrected images using the state-of-the-art empirical BH correction method.

Evaluation of image quality according to the distance to the anterior surfaces on CT with barium composite shielding in a phantom study

This study aimed to determine the optimal distance between the evaluated shielding agent and a patient's superficial organs by evaluating the quantitative noise in the image on performing CT scans using an anthropomorphic phantom. The shielding agent was composed of barium sulfate and was employed to protect superficial organs with high radiation sensitivity during the CT scan. More specifically, a barium sulfate (BaSO4)-based shielding agent was employed. The distance between the anthropomorphic phantom and the shielding agent was delineated using a sponge at intervals of 10, 20, 30, and 40 mm, as well as in a no-gap condition. The size of the shielding agent used for the breast was 430 × 205 mm, and the lead equivalent was 0.06 mmPb. To evaluate Hounsfield unit (HU) values and noise on CT imaging when using a barium shielding agent, the region of interest in the phantom was set to 100 mm2. Quantitative noise on CT imaging was observed only in the no-gap condition. Moreover, HU values and quantitative image noise were significantly increased in images scanned with no gap between the shielding agent and the phantom (p < .05), HU values and noise decreased as the distance from the shield to the surface increased. In images in which the shield and phantom surface were in direct contact, streak artifacts were observed only in the no-gap image. Therefore, in CT scans performed using a shielding agent, we conclude that the distance between the shielding agent and surface should be 10 mm or more in order to obtain an image with reduced noise.

Letter: Arthrodesis With Titanium Implants: A Different Picture.

ABBREVIATION: DECT Dual-energy computed tomography. To the Editor: Psuedoarthrosis is a commonly known complication of trying to achieve solid body spinal fusion (arthrodesis) in the setting of spine surgery. Although recent advances in spinal surgery instrumentation, imaging, biologics, and management have occurred, it is still a prominent complication with significant morbidity and high cost burden.1 Deciding when to operate on these patients is variable and is primarily dependent on clinician gestalt regarding degree of intervention, physical examination findings, and the presence or lack of boney bridge formation across interbody implantation. Complicating this matter even further is the presence of psuedoarthrosis in the setting of no clinical symptoms. As it stands, computed tomography (CT) is the gold standard for detecting the presence of psuedoarthrosis. When evaluating multiple modalities of imaging for effectiveness in evaluating psuedoarthrosis, Peters et al found the pooled CT odds ratio to be highest in comparison with other modalities such as flexion/extension radiography, magnetic resonance, and scintigraphy. Furthermore, the authors went on to cite several instances in the literature where psuedoarthrosis was not detected through radiographic means but was detected by unveiling motion between adjacent segments.2 Although CT imaging may be a useful tool in imaging operated vertebrae with polyetheretherketone, carbon fiber, metal alloy, or other radiolucent implants, it can be less than reliable in titanium implants which often display significant artifact. Similar artifacts are seen on magnetic resonance imaging, posing an obstacle for thorough postoperative evaluation. Altogether, these findings illustrate the necessity of alternative modalities to better evaluate arthrodesis in the setting of spine surgery using titanium implants. Dual-energy CT (DECT) may prove a step in the right direction for evaluating psuedoarthrosis. In opposition to conventional CT imaging which uses a single image set, DECT uses 2 separate x-ray photon energy spectra; thus, allowing the interrogation of different attenuation properties at different energies.3 Artifact secondary to metal prosthesis (ie, titanium spinal interbody implants) typically occurs when low-energy photons within a polychromatic x-ray beam scatter radiation and abut image quality. Using DECT, these low-energy photons are removed; thus, limiting artifact. As of now, artifact in conventional CT is a major limitation in the evaluation of arthrodesis because initial boney bridge formation can be subtle or absent. To date, several articles have investigated the use of DECT in the setting of spinal fusion. Zeng et al4 in a retrospective review of 40 patients who underwent DECT after lumbar internal fixation found superior image quality with metal artifact reduction compared with conventional methods. Srinivasan et al5 in a retrospective review analyzing transpedicular screw interfacing noted the beneficial effects of metallic streak artifact reduction alongside better visualization of the hardware-bone interface. To our knowledge, there have been no prospective studies that study the differences between DECT and non-DECT images of the same patients. Several radiological fusion scales exist for the grading and measurement of fusion including a quantitative scale such as CT-Hounsfield Unit and qualitative scales such as the Lenke, Bridwell, and Brantigan, Steffe, Fraser scales.6,7 However, these are limited in that they are significantly dependent on subjective interpretation by an individual clinician and limited in their utility in assessment of titanium implants that present with significant artifact. Taken together, these findings highlight the need for a novel, quantized scale for assessing fusion with titanium implants using DECT.

Metallic Artifact Reduction in Midfacial CT Scans Using Patient-Specific Polymer Implants Enhances Image Quality.

Midfacial reconstruction after tumor resection surgery is commonly conducted by using autologous bone grafts or alloplastic implants. Titanium is the most frequently used osteosynthesis material in these cases but causes disturbing metallic artifacts in CT imaging. The purpose of this experimental study was to evaluate whether the use of midfacial polymer implants reduces metallic artifacts in CT imaging to improve image quality. Zygomatic titanium (n = 1) and polymer (n = 12) implants were successively implanted in a human skull specimen. Implants were analyzed for their effect on Hounsfield Unit values (streak artifacts) and virtual growth in CT images (blooming artifacts) as well as image quality. Multi-factorial ANOVA and Bonferroni's post hoc test were used. Titanium (173.7 HU; SD ± 5.1) and hydroxyapatite containing polymers (155.3 HU; SD ± 5.9) were associated with significantly more streak artifacts compared to all other polymer materials. There was no significant difference in blooming artifacts between materials. The metallic artifact reduction algorithm showed no significant difference. Image quality was slightly better for polymer implants compared to titanium. Personalized polymer implants for midfacial reconstruction significantly reduce metallic artifacts in CT imaging which improves image quality. Hence, postoperative radiation therapy planning and radiological tumor aftercare around the implants are facilitated.

DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction.

Sparse-view Computed Tomography (CT) has the ability to reduce radiation dose and shorten the scan time, while the severe streak artifacts will compromise anatomical information. How to reconstruct high-quality images from sparsely sampled projections is a challenging ill-posed problem. In this context, we propose the unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) based on a novel iterative reconstruction framework to synergize the merits of deep learning and iterative reconstruction. DREAM-Net performs constraints using deep neural networks in the projection domain, residual space, and image domain simultaneously, which is different from the routine practice in deep iterative reconstruction frameworks. First, a projection inpainting module completes the missing views to fully explore the latent relationship between projection data and reconstructed images. Then, the residual awareness module attempts to estimate the accurate residual image after transforming the projection error into the image space. Finally, the image refinement module learns a non-standard regularizer to further fine-tune the intermediate image. There is no need to empirically adjust the weights of different terms in DREAM-Net because the hyper-parameters are embedded implicitly in network modules. Qualitative and quantitative results have demonstrated the promising performance of DREAM-Net in artifact removal and structural fidelity.

Design of stationary computed tomography baggage scanner with π-angle sparsity and compressed-sensing reconstruction: simulation study

Continuing our research on X-ray R&D for aviation security, we designed a novel stationary computed tomography (SCT) baggage scanner with π-angle sparsity, comprising several dozen pairs of nanotube-based X-ray source and linear array-type detector placed within a scan angle of 180° at an equiangular distance. Simultaneously, we developed a reconstruction method for the specific configuration adopting an iterative algorithm based on the popular compressed-sensing (CS) scheme. To validate the efficacy of the proposed SCT design, we conducted a series of simulations using a numerical baggage phantom before its practical implementation. The results showed that the proposed SCT design and corresponding reconstruction method significantly reduced the streak artifacts appearing in the filtered-backprojection (FBP) reconstruction, thereby considerably improving the image quality. The contrast-to-noise ratio evaluated under a test condition of 180°/P20/CS was approximately 16.2, approximately 4.7 and 1.1 times larger than those for 360°/P20/FBP and 360°/P20/CS, respectively. The proposed SCT design enables saving scan time, improving image quality, and restricting radiation exposure zone.

Affine transformation edited and refined deep neural network for quantitative susceptibility mapping

Deep neural networks have demonstrated great potential in solving dipole inversion for Quantitative Susceptibility Mapping (QSM). However, the performances of most existing deep learning methods drastically degrade with mismatched sequence parameters such as acquisition orientation and spatial resolution. We propose an end-to-end AFfine Transformation Edited and Refined (AFTER) deep neural network for QSM, which is robust against arbitrary acquisition orientation and spatial resolution up to 0.6 mm isotropic at the finest. The AFTER-QSM neural network starts with a forward affine transformation layer, followed by a Unet for dipole inversion, then an inverse affine transformation layer, followed by a Residual Dense Network (RDN) for QSM refinement. Simulation and in-vivo experiments demonstrated that the proposed AFTER-QSM network architecture had excellent generalizability. It can successfully reconstruct susceptibility maps from highly oblique and anisotropic scans, leading to the best image quality assessments in simulation tests and suppressed streaking artifacts and noise levels for in-vivo experiments compared with other methods. Furthermore, ablation studies showed that the RDN refinement network significantly reduced image blurring and susceptibility underestimation due to affine transformations. In addition, the AFTER-QSM network substantially shortened the reconstruction time from minutes using conventional methods to only a few seconds.