Reduce Image Artifacts Research Articles

Artifact reduction in free-breathing, free-running myocardial perfusion imaging with interleaved non-selective RF excitations.

To reduce inflow and motion artifacts in free-breathing, free-running, steady-state spoiled gradient echo T1 -weighted (SPGR) myocardial perfusion imaging. Unsaturated spins from inflowing blood or out-of-plane motion cause flashing artifacts in free-running SPGR myocardial perfusion. During free-running SPGR, 1 non-selective RF excitation was added after every 3 slice-selective RF excitations to suppress inflow artifacts by forcing magnetization in neighboring regions to steady-state. Bloch simulations and phantom experiments were performed to evaluate the impact of the flip angle and non-selective RF frequency on inflowing spins and tissue contrast. Free-running perfusion with (n = 11) interleaved non-selective RF or without (n = 11) were studied in 22 subjects (age = 60.2 ± 14.3 years, 11 male). Perfusion images were graded on a 5-point Likert scale for conspicuity of wall enhancement, inflow/motion artifact, and streaking artifact and compared using Wilcoxon sum-rank testing. Numeric simulation showed that 1 non-selective RF excitation applied after every 3 slice-selective RF excitations produced superior out-of-plane signal suppression compared to 1 non-selective RF excitation applied after every 6 or 9 slice-selective RF excitations. In vitro experiments showed that a 30° flip angle produced near-optimal myocardial contrast. In vivo experiments demonstrated that the addition of interleaved non-selective RF significantly (P < .01) improved conspicuity of wall enhancement (mean score = 4.4 vs. 3.2) and reduced inflow/motion (mean score = 4.5 vs. 2.5) and streaking (mean score = 3.9 vs. 2.4) artifacts. Non-selective RF excitations interleaved between slice-selective excitations can reduce image artifacts in free-breathing, ungated perfusion images. Further studies are warranted to assess the diagnostic accuracy of the proposed solution for evaluating myocardial ischemia.

Motion Artifact Reduction in Contrast-Enhanced Dual-Energy Mammography - A Multireader Study about the Effect of Nonrigid Registration as Motion Correction on Image Quality.

The technically caused delay between low-energy (LE) and high-energy (HE) acquisitions allows motion artifacts in contrast-enhanced dual-energy mammography (CEDEM). In this study the effect of motion correction by nonrigid registration on image quality of the recombined images was investigated. Retrospectively for 354 recombined CEDEM images an additional recombined image was processed from the raw data of LE and HE images using the motion correction algorithm. Five radiologists with many years of experience in breast cancer diagnostic imaging compared side-by-side one conventional processed CEDEM image with the corresponding image processed by the motion correction algorithm. Every pair of images was compared based on six criteria: General image quality (1), sharpness of skin contour (2), reduction of image artifacts (3), sharpness of lesion contour (4), contrast of lesion (5), visibility of lymph nodes (6). These criteria were rated on a Likert scale (improvement: + 1, + 2; deterioration: -1, -2). The mean ratings concerning criteria 1-5 showed a superiority of the recombined images processed by the motion correction algorithm. For example, the mean rating of general image quality was 0.86 (95 % CI: 0.78; 0.93). Only the mean rating concerning criterion 6 showed an inferiority of the recombined images processed by the motion correction algorithm (-0.29 (-0.46; -0.13)). The usage of nonrigid registration for motion correction significantly improves the general image quality and the quality of subordinate criteria on the recombined CEDEM images at the expense of somewhat reduced lymph node visibility in some cases. · The usage of motion correction in CEDEM improves the general image quality. · Motion correction might have the potential to increase diagnostic accuracy. · Alternative methods of motion artifact reduction are not yet available in clinical practice. · Sistermanns M, Kowall B, Hörnig M et al. Motion Artifact Reduction in Contrast-Enhanced Dual-Energy Mammography - A Multireader Study about the Effect of Nonrigid Registration as Motion Correction on Image Quality. Fortschr Röntgenstr 2021; 193: 1183 - 1188.

Novel flat-panel cone-beam CT compared to multi-detector CT for assessment of acute ischemic stroke: A prospective study

PurposeCone beam CT (CBCT) imaging assessment of acute ischemic stroke (AIS) patients with large-vessel occlusion (LVO) in the angiosuite may improve stroke workflow and decrease time to recanalization. In order for this workflow to gain widespread acceptance, current CBCT imaging needs further development to improve image quality. Our study aimed to compare the image quality of a new CBCT protocol performed directly in the angiosuite with imaging from multidetector CT as a gold standard. MethodsAIS patients with an LVO who were candidates for endovascular treatment were prospectively included in this study. Following conventional multidetector CT (MDCT), patients underwent unenhanced cone beam CT (XperCT, Philips) imaging in the angiosuite, using two different protocols: a standard 20.8 s XperCT and/or an improved 10.4 s XperCT protocol. Images were evaluated using both qualitative and quantitative methods. ResultsWe included 65 patients in the study. Patients received CBCT imaging prior to endovascular treatment; 18 patients were assessed with a standard 20.8 s protocol scans and 47 with a newer 10.4 s scan. The quantitative analysis showed that the mean contrast-to-noise ratio (CNR) was significantly higher for the newer 10.4 s protocol compared with the 20.8 s protocol (2.08 +/- 0.64 vs. 1.15 +/- 0.27, p < 0.004) and the mean image noise was significantly lower for the 10.4 s XperCTs when compared with the 20.8 s XperCTs (6.30 +/- 1.34 vs. 7.82 +/- 2.03, p=<0.003). Qualitative analysis, including 6 measures of image quality, demonstrated that 74.1 % of the 10.4 s XperCT scans were ranked as ‘Acceptable’ for assessing parenchymal imaging in AIS patients(scoring 3–5 points on a 5-point Likert-scale), compared with 32.4 % of the standard 20.8 s XperCT and 100 % of the MDCT scans. Compared to the MDCT studies, 83 % of the 10.4 s XperCT scans were deemed sufficient image quality for a direct-to-angiosuite selection, compared to only 11 % for the standard 20.8 s scans. The largest image quality improvements included grey/white matter differentiation (59 % improvement), and reduction of image noise and artefacts (63 % & 50 % improvement, respectively). ConclusionsContinued advances in cone-beam CT allow marked improvements in image quality for the assessment of brain parenchyma, which supports a direct-to-angiosuite approach for AIS patients eligible for thrombectomy treatment.

Fully Leveraging Deep Learning Methods for Constructing Retinal Fundus Photomontages

Retinal photomontages, which are constructed by aligning and integrating multiple fundus images, are useful in diagnosing retinal diseases affecting peripheral retina. We present a novel framework for constructing retinal photomontages that fully leverage recent deep learning methods. Deep learning based object detection is used to define the order of image registration and blending. Deep learning based vessel segmentation is used to enhance image texture to improve registration performance within a two step image registration framework comprising rigid and non-rigid registration. Experimental evaluation demonstrates the robustness of our montage construction method with an increased amount of successfully integrated images as well as reduction of image artifacts.

A reconstruction method for cone-beam computed laminography based on projection transformation

X-ray computed tomography (CT) is a widely popular nondestructive testing technique for engineering and medical purposes, but its limitation is openly recognized in the inspection of large components, particularly for plate-type structures. Computed laminography (CL) avoids this defect. Nowadays, most of the existing analytical CL reconstruction approaches ignore the problem that the projection data under the CL scanning structure does not strictly meet the conditions of the standard filtered back-projection (FBP) or Feldkamp–Davis–Kress (FDK) method. Since the original CL data are directly filtered without considering the influence of the tilt angle of the CL rotation axis, this will affect the quality of the reconstructed images. In view of this situation, a conversion method of cone-beam CL data based on projection transformation is proposed, which is also referred to as the CL re-projection (CLRP). The collected CL projections can be corrected to satisfy the filtering requirements. We establish a virtual CT detector and use the CLRP to convert the known CL data into the projection on the virtual detector. Then, the FDK method commonly used in cone-beam CT is applied to reconstruct the converted data. Through the above two steps to achieve CL reconstruction. Computer simulations and experimental results show that the CLRP algorithm can accurately convert the raw CL data into those which satisfying the FDK method. The CLRP can decline the information aliasing to a certain extent. Compared with the existing CL-FBP algorithm, the CLRP-FDK method for CL reconstruction can effectively reduce image artifacts. The CLRP algorithm provides a new idea for CL reconstruction and plays an important role in practical engineering applications.

A unified scatter rejection and correction method for cone beam computed tomography.

Scattered radiation is a major cause of image quality degradation in flat panel detector-based cone beam CT (CBCT). While recently introduced 2D antiscatter grids reject the majority of scatter fluence, the small percentage of scatter fluence still transmitted to the detector remains a major challenge for implementation of quantitative imaging techniques such as dual energy imaging in CBCT. Additionally, this residual scatter is also a major source of grid-induced artifacts, which impedes implementation of 2D grids in CBCT. We therefore present a new method to achieve both robust scatter rejection and residual scatter correction using a 2D antiscatter grid; in doing so, we expand the role of 2D grids from mere scatter rejection devices to scatter measurement devices. In our method, the radiopaque septa of the 2D grid emulate a micro array of beam-stops placed on the detector which introduce spatially periodic septal shadows. By selecting sufficiently thin grid septa, the primary intensity can be reduced while preserving the uniformity of scatter intensity. This enables us to correlate the modulated pixel signal intensity in septal shadows with local scatter intensity. Our method then exploits this correlation to measure and remove residual scatter intensity from projections. No assumptions are made about the object being imaged. We refer to this as Grid-based Scatter Sampling (GSS). In this work, we evaluate the principle of signal modulation with grid septa, the accuracy of scatter estimates, and the effect of the GSS method on image quality using simulations and measurements. We also implement the GSS method experimentally using a 2D grid prototype. Our results demonstrate that the GSS method increased CT number accuracy and reduced image artifacts associated with scatter. With 2D grid and residual scatter correction, HU nonuniformity was reduced from 65HU to 30HU in pelvis sized phantoms, and HU variations due to change in phantom size were reduced from 59HU to 20HU, when compared to use of only a 2D grid. With residual scatter correction via GSS method, grid-induced ring artifacts were suppressed, leading to a 41% reduction in noise. The shape of the modulation transfer function (MTF) was preserved before and after suppression of ring artifacts. Our grid-based scatter sampling method enables utilization of a 2D grid as a scatter measurement and correction device. This method significantly improves quantitative accuracy in CBCT, further reducing the image quality gap between CBCT and multi-detector CT. By correcting residual scatter with the proposed method, grid-induced line artifacts in projections and associated ring artifacts in CBCT images were also suppressed with no compromise of spatial resolution.

Effectiveness of Commercial Software–Enhanced Image Artifact Reduction Software

IntroductionArtifact reduction (AR) software has been incorporated into some cone-beam computed tomographic (CBCT) systems to reduce the severity of beam hardening (BH) artifacts and improve image quality. This study quantifies BH artifact and evaluates the effectiveness of AR in 2 CBCT systems. MethodsPalatal roots of Dent-Alike (Dentsply Sirona, Tulsa, OK) teeth were prepared and root filled with gutta-percha and EndoSequence BC Sealer (Brasseler, Savannah, GA). Six teeth were imaged with and without AR software using the ProMax3D (Planmeca Oy, Helsinki, Finland) and the Pax-i3D (Vatech, Hwaseong-si, South Korea) systems. FSL (FMRIB, Oxford, UK) software was used to quantify the light and dark components of the BH artifact along the tooth root using a specific region of interest approach and an image-wide analysis approach. Statistical analysis was performed using paired t tests and corrected for multiple comparisons with cluster mass correction using a nonparametric statistical analysis to evaluate the differences in the artifact volumes and areas with and without AR. ResultsA significant reduction in the light artifact was observed with the Planmeca system (P < .05), but no significant differences were observed for either the light or dark artifacts with the Vatech system when AR was applied. There were also significant reductions in the volumes of light and dark artifacts along the entire root length when the AR was applied with the Planmeca system (cluster mass P < .05), but no significant differences were observed with the Vatech system. ConclusionsProprietary AR software is not equally effective in reducing the light and/or dark components of CBCT BH artifacts.

Optimized cervical spinal cord perfusion MRI after traumatic injury in the rat

Despite the potential to guide clinical management of spinal cord injury and disease, noninvasive methods of monitoring perfusion status of the spinal cord clinically remain an unmet need. In this study, we optimized pseudo-continuous arterial spin labeling (pCASL) for the rodent cervical spinal cord and demonstrate its utility in identifying perfusion deficits in an acute contusion injury model. High-resolution perfusion sagittal images with reduced imaging artifacts were obtained with optimized background suppression and imaging readout. Following moderate contusion injury, perfusion was clearly and reliably decreased at the site of injury. Implementation of time-encoded pCASL confirmed injury site perfusion deficits with blood flow measurements corrected for variability in arterial transit times. The noninvasive protocol of pCASL in the spinal cord can be utilized in future applications to examine perfusion changes after therapeutic interventions in the rat and translation to patients may offer critical implications for patient management.

Effect and correction of optode coupling errors in breast imaging using diffuse optical tomography.

In diffuse optical tomography (DOT) and spectroscopy (DOS) using handheld probes, tissue curvature can cause bad fiber-to-tissue contact. Understanding and minimizing image artifacts caused by these coupling errors would significantly improve DOT and DOS image quality. In this work, we utilized Monte Carlo simulations and experiments with gelatin-Intralipid phantoms to systematically study the influence of source or detector (optode) coupling errors. Optode coupling errors can increase the amplitude and decrease the phase of the measured diffuse reflectance, creating artifacts in the reconstructed absorption maps, such as hot spots on the edges. We propose an outlier removal algorithm that can correct these image artifacts, and we demonstrate its performance using simulations, phantom experiments, and breast patient data acquired with bad probe contact due to a dense or small breast. Further, we designed and implemented a new resistance-type thin-film force sensor array that provides real-time optode coupling feedback and guides the outlier removal to minimize optode coupling errors. Our approaches and study results have significant implications for reducing image artifacts arising from handheld probes, which are commonly used with mobile and wearable DOT and DOS devices.

Proof of concept image artifact reduction by energy-modulated proton computed tomography (EMpCT).

To reduce imaging artifacts and improve image quality of a specific proton computed tomography (pCT) prototype scanner by combining pCT data acquired at two different incident proton energies to avoid protons stopping in sub-optimal detector sections. Image artifacts of a prototype pCT scanner are linked to protons stopping close to internal structures of the scanner's multi-stage energy detector. We aimed at avoiding such protons by acquiring pCT data at two different incident energies and combining the data in post-processing from which artifact-reduced images of the relative stopping power (RSP) were calculated. Energy-modulated pCT (EMpCT) images were assessed visually and quantitatively and compared to the original mono-energetic images in terms of RSP accuracy and noise. Data were acquired for a homogeneous water phantom. RSP images reconstructed from the mono-energetic datasets displayed local image artifacts which were ring-shaped due to the homogeneity of the phantom. The merged EMpCT dataset achieved a superior visual image quality with reduced artifacts and only minor remaining rings. The inter-quartile range (25/75) of RSP values was reduced from 0.7% with the current standard acquisition to 0.2% with EMpCT due to the reduction of ring artifacts. In this study, dose was doubled compared to a standard scan, but we discuss strategies to reduce excess dose. EMpCT allows to effectively avoid regions of the energy detector that cause image artifacts. Thereby, image quality is improved.

Dynamic MRI reconstruction with end-to-end motion-guided network.

Temporal correlation in dynamic magnetic resonance imaging (MRI), such as cardiac MRI, is informative and important to understand motion mechanisms of body regions. Modeling such information into the MRI reconstruction process produces temporally coherent image sequence and reduces imaging artifacts and blurring. However, existing deep learning based approaches neglect motion information during the reconstruction procedure, while traditional motion-guided methods are hindered by heuristic parameter tuning and long inference time. We propose a novel dynamic MRI reconstruction approach called MODRN and an end-to-end improved version called MODRN(e2e), both of which enhance the reconstruction quality by infusing motion information into the modeling process with deep neural networks. The central idea is to decompose the motion-guided optimization problem of dynamic MRI reconstruction into three components: Dynamic Reconstruction Network, Motion Estimation and Motion Compensation. Extensive experiments have demonstrated the effectiveness of our proposed approach compared to other state-of-the-art approaches.

Ficoll as testing material for diffusion weighted imaging-quality assurance phantoms

PurposeThe aim of this work is to test the use of aqueous solutions of Ficoll®**, a highly branched polymer displaying crowding properties, to build a phantom suitable for Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI). MethodsWe developed a test object made of a cylindrical plastic container with a precise geometrical arrangement suitable for measuring several samples at the same time. The container was designed to host single vials with variable geometry and number, and to fit inside common commercial head coils for MRI scanners.In our experiments, vials were filled with 8 aqueous solutions of Ficoll 70 and Ficoll 400 spanning a range of polymer concentration from 5 to 30% by weight. Vials containing ultra-pure water were also used as reference. Experiments were performed on both 1.5 and 3 T clinical scanners (GE, Philips and Siemens), under the conditions of a standard clinical examination. ResultsThe geometry of the phantom provided reduced imaging artifacts, especially image distortions at magnetic interfaces. We found that the Apparent Diffusion Coefficient (ADC) varied in the range of 0.00125–0.00223 mm2/s and decreased with Ficoll concentration. ADC vs Ficoll concentration exhibited a linear trend. Results were consistent over time and among different MRI clinical scanners, showing an average variability of 3% at 1.5 T and of 7.5% at 3 T. Moreover, no substantial difference was found between Ficoll 70 and 400. By varying Ficoll concentration, ADC can be modulated to approach tissue-mimicking values. Preliminary results for relaxation measurements proved that both T1 and T2 decreased with Ficoll concentration in the ranges 1.3–2.4 s and 150–800 ms respectively. ConclusionsIn this work, we propose a 3D phantom design based on the widespread crowding agent Ficoll, which is suitable for DWI quality assurance purposes in MRI acquisitions. Aqueous Ficoll solutions provide good performance in terms of stability, ease of preparation, and safety.

Reconstruction of Endoscopic Photoacoustic Tomographic Images for Attenuating Acoustic Tissues: A Preliminary Study

Biological endoscopic photoacoustic tomography (EPAT) Has been rapidly developed in the past decades as a hybrid functional imaging modality with multi-physics coupling. The acoustic inhomogeneity of biological tissues results in acoustic reflection, scattering, diffusion and absorption, thus the attenuated photoacoustic signals measured by the detector. The idealized assumptions on the acoustic homogeneity usually lead to reconstructed images with reduced quality and imaging depth. This paper focuses on a preliminary study of EPAT image reconstruction accounting for acoustic attenuation. The correlation equation related to ideal and measured photoacoustic signals is deduced. The equation is then solved by using singular value decomposition (SVD) to recover the approximation of the ideal signals from the measured signals. Finally, the cross-sectional images of optical distribution are reconstructed from the ideal signals. Numerical results indicate that this method can effectively reduce image artifacts caused by acoustic attenuation and improve image focusing. Comparison results suggest that it outperforms the method of correcting the frequency-dependent attenuation which requires the prior knowledge about attenuation coefficients of tissues.

Deep-learning-based direct inversion for material decomposition.

To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi-energy computed tomography (CT) images without performing conventional material decomposition. The proposed CNN (denoted as Incept-net) followed the general framework of encoder-decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi-energy CT. Incept-net was implemented with a customized loss function, including an in-house-designed image-gradient-correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood-iodine mixture, and fat] were scanned using a research photon-counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different-configurations of full field-of-view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept-net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least-square-based material decomposition (LS-MD), total-variation regularized material decomposition (TV-MD), and U-net-based method. Incept-net improved accuracy of the predicted mass density of basis materials compared with the U-net, TV-MD, and LS-MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57mgI/cc for Incept-net, U-net, TV-MD, and LS-MD, respectively, across all iodine-present inserts (2.0-24.0mgI/cc). With the LS-MD as the baseline, Incept-net and U-net achieved comparable noise reduction (both around 95%), both higher than TV-MD (85%). The proposed IGC regularizer effectively helped both Incept-net and U-net to reduce image artifact. Incept-net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept-net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept-net achieved relatively improved performance than the comparator U-net, indicating that performance gain by Incept-net was not achieved by simply increasing network learning capacity. Incept-net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept-net generalized and performed well with unseen image structures and different material mass densities. This study provided preliminary evidence that the proposed CNN may be used to improve the material decomposition quality in multi-energy CT.

Deep learning-based compressed image artifacts reduction based on multi-scale image fusion

One of the visually noticeable compression artifacts in block-based image/video compression platforms is called blocking artifact. Several post-processing methods were presented to reduce such kind of artifacts. However, most methods in the literature often induce visibly blurring artifacts. The paper presents a deep network to eliminate image compression artifacts (usually denoted by image deblocking) based on image fusion in multi-scale manner. Recent deep learning-based related methods usually learn deep models using a loss function in per-pixel manner based on explicit image priors in order to directly produce clean images. In place of existing deep learning-guided approaches, the problem is reformulated in this paper to the learning of the residuals (or artifacts) between the received images and their corresponding clean images (ground truths). In the presented deep framework, an input image is first down-sampled while naturally reducing the blocking artifacts. Then, our multi-scale image fusion model is used for fusing the different down-scaled versions (of less artifacts) with the input image (with severer artifacts) to estimate the blocking artifacts. Then, by deducting the estimated artifacts from the input image, the blocking artifacts can be significantly eliminated and most original image details are preserved simultaneously. The presented method is well applicable to any vision-based computer systems with digital visual codec embedded.

Reduced and standard field-of-view diffusion weighted imaging in patients with rectal cancer at 3 T—Comparison of image quality and apparent diffusion coefficient measurements

PurposeTo compare a zoomed EPI-DWI (z-EPI) with a standard EPI-DWI (s-EPI) in the primary diagnostics of rectal cancer and assess its potential of reduced image artifacts. Method22 therapy-naïve patients with rectal cancer underwent rectal MRI at a 3 T-system. The protocols consisted of a z-EPI DWI and s-EPI DWI sequence. Images were assessed by two independent and experienced readers regarding overall image quality and artifacts on a 5-point Likert scale, as well as overall sequence preference. In a lesion-based analysis, tumor and lymph node detection were rated on a 4-point Likert scale. Apparent diffusion coefficient (ADC) measurements were performed. ResultsOverall Image quality score for z-EPI and s-EPI showed no statistically significant differences (p = 0.80/0.54, reader 1/2) with a median score of 4 (“good” image quality) for both sequences. The image quality preference rank for z-EPI and s-EPI was given the category ‘no preference’ in 64 % (reader 1) and 50 % (reader 2). Most artifact-related scores (susceptibility, motion and distortion) did not show reproducible significant differences between z-EPI and s-EPI. The two sequences exhibited comparable, mostly good and excellent quality scores for tumor and lymph node detection (p = 0.19−0.99). ADC values were significantly lower for z-EPI than for s-EPI (p = 0.001/0.002, reader 1/2) with good agreement of ADC measurements between both readers. ConclusionOur data showed comparable image quality and lesion detection for the z-EPI and the s-EPI sequence in MRI of rectal cancer, whereas the mean ADC of the tumor was significantly lower in z-EPI compared to s-EPI.

Multi-energy CT imaging for large patients using dual-source photon-counting detector CT

Multi-energy CT imaging of large patients with conventional dual-energy (DE)-CT using an energy-integrating-detector (EID) is challenging due to photon starvation-induced image artifacts, especially in lower tube potential (80–100 kV) images. Here, we performed phantom experiments to investigate the performance of DECT for morbidly obese patients, using an iodine and water material decomposition task as an example, on an emulated dual-source (DS)-photon-counting-detector (PCD)-CT, and compared its performance with a clinical DS-EID-CT. An abdominal CT phantom with iodine inserts of different concentrations was wrapped with tissue-equivalent gel layers to emulate a large patient (50 cm lateral size). The phantom was scanned on a research whole-body single-source (SS)-PCD-CT (140 kV tube potential), a DS-PCD-CT (100/Sn140 kV; Sn140 indicates 140 kV with Sn filter), and a clinical DS-EID-CT (100/Sn140 kV) with the same radiation dose. Phantom scans were repeated five times on each system. The DS-PCD-CT acquisition was emulated by scanning twice on the SS-PCD-CT using different tube potentials. The multi-energy CT images acquired on each system were then reconstructed, and iodine- and water-specific images were generated using material decomposition. The root-mean-square-error (RMSE) between true and measured iodine concentrations were calculated for each system and compared. The images acquired on the DS-EID-CT showed severe artifacts, including ringing, reduced uniformity, and photon starvation artifacts, especially for low-energy images. These were largely reduced in DS-PCD-CT images. The CT number difference that was measured using regions-of-interest across field-of-view were reduced from 20.3 ± 0.9 (DS-EID-CT) to 2.5 ± 0.4 HU on DS-PCD-CT, showing improved image uniformity using DS-PCD-CT. Iodine RMSE was reduced from 3.42 ± 0.03 mg ml−1 (SS-PCD-CT) and 2.90 ± 0.03 mg ml−1 (DS-EID-CT) to 2.39 ± 0.05 mg ml−1 using DS-PCD-CT. DS-PCD-CT out-performed a clinical DS-EID-CT for iodine and water-based material decomposition on phantom emulating obese patients by reducing image artifacts and improving iodine quantification (RMSE reduced by 20%). With DS-PCD-CT, multi-energy CT can be performed on large patients that cannot be accommodated with current DECT.

Jacobi–Fourier phase masks to increase performance of wavefront-coded optical systems for random or varying aberration alleviation

Wavefront coding is a hybrid optical–digital technique proposed to increase the effective depth of focus of optical systems. The key to wavefront coding lies in the design of a suitable phase mask placed at the system’s aperture to achieve invariant imaging properties over a range of defocus. In systems limited by temporally or spatially varying aberrations the use of wavefront coding has not yet been demonstrated. Here, we propose the use of Jacobi–Fourier shaped phase masks to produce sharp and clear images for optical systems affected by random and varying aberrations. When choosing the mask, noise levels must be taken into account too. In order to illustrate the potential use of the masks we will show by experimental simulations that a Jacobi–Fourier mask can be designed to alleviate temporally varying aberrations due to atmospheric turbulences being robust to noise while keeping acceptable resolution and reducing image artifacts.

Accurate and Efficient Image Super-Resolution via Global-Local Adjusting Dense Network

Convolutional neural network-based (CNN-based) method has shown its superior performance on the image super-resolution (SR) task. However, several researches have shown that obtaining a better reconstruction result often leads to the significant increase in parameters and computation. To alleviate the burden in computational needs, we propose a novel global-local adjusting dense super-resolution network (GLADSR) to build a powerful yet lightweight CNN-based SR model. To enhance the network capacity, we present a global-local adjusting module (GLAM) which can adaptively reallocate the processing resources with local selective block (LSB) and global guided block (GGB). The GLAMs are linked with nested dense connections to make better use of the global-local adjusted features. In addition, we also introduce a separable pyramid upsampling (SPU) module to replace the regular upsampling operation, which thus brings a substantial reduction of its parameters and obtains better results. Furthermore, we show that the proposed refinement structure is capable of reducing image artifacts in SR processing. Extensive experiments on benchmark datasets show that the proposed GLADSR outperforms the state-of-the-art methods with much fewer parameters and much less computational cost.

SIMBA: Scalable Inversion in Optical Tomography Using Deep Denoising Priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables high-quality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixed-point convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.