Terms Of Image Quality Research Articles

EYE LENS DOSE OPTIMIZATION THROUGH GANTRY TILTING IN BRAIN CT SCAN: THE POTENTIAL EFFECT OF THE RADIOLOGICAL TECHNOLOGISTS' TRAINING.

This study was designed to evaluate the effect of the radiological technologists' training on optimising the eye lens dose in brain computed tomography (CT) examinations. The lens dose of 50 adult patients was measured using thermoluminescent dosimeters before and after technologists' training. Dose values of lenses, dose length product (DLP), volumetric CT dose index (CTDIvol) as well as image quality in terms of quantitative (contrast to noise ratio and signal to noise ratio) and subjective (artefact) parameters were compared before and after training. Lens dose values were 31.57±9.84mGy and 5.36±1.53mGy before and after training, respectively, which was reduced by ~83% (p<0.05). The values of DLP, CTDIvol and image quality parameters were not significantly different (p>0.05) and all images were diagnostically acceptable. Excluding the orbits from the scanning range is an efficient approach to optimize the lens dose; the training of the technologists has also a pivotal role in dose reducing.

Diabetic Retinopathy Screening Using Smartphone-Based Fundus Imaging in India

Early detection and treatment can prevent irreversible blindness from diabetic retinopathy (DR), which is the leading cause of visual impairment among working-aged adults worldwide. Some 80% of affected persons live in low- and middle-income countries, yet lack of resources has largely prevented DR screening implementation in these world regions. Smartphone-based fundus imaging (SBFI) allows for low-cost mobile fundus examination using an adapter on a smartphone; however, key aspects such as image quality, diagnostic accuracy, and comparability of different approaches have not been systematically assessed to date. Evaluation of diagnostic technology. A total of 381 eyes of 193 patients with diabetes were recruited at outreach eye clinics in South India. We compared 4 technically different approaches of SBFI (3 approaches based on direct and 1 approach based on indirect ophthalmoscopy) in terms of image quality and diagnostic accuracy for DR screening. Image quality (sharpness/focus, reflex artifacts, contrast, and illumination), field-of-view, examination time, and diagnostic accuracy for DR screening were analyzed against conventional fundus photography and clinical examination. Smartphone-based fundus imaging based on indirect ophthalmoscopy yielded the best image quality (P < 0.01), the largest field-of-view, and the longest examination time (111 vs. 68-86 seconds, P < 0.0001). Agreement with the reference standard (Cohen's kappa 0.868) and sensitivity/specificity to detect DR were highest for the indirect SBFI approach (0.79/0.99 for any DR and 1.0/1.0 for severe DR, 0.79/1.0 for diabetic maculopathy). Smartphone-based fundus imaging can meet DR screening requirements in an outreach setting; however, not all devices are suitable in terms of image quality and diagnostic accuracy. Smartphone-based fundus imaging might aid in alleviating the burden of DR screening in low- and middle-income countries, and these results will allow for a better selection of SBFI devices in field trials for DR screening.

White spots noise removal of neutron images using improved robust principal component analysis

• A simple white spots removal method is proposed for degraded neutron images. • A‘detection-location-removal’ iterative strategy is introduced for the white spots detection and removal. • The qualitative and quantitative analysis are used for evaluating the performance of the mentioned methods. • The proposed method has a better performance in spots removal and details preservation than the comparison methods. Neutron radiography (NR) technology has been widely used in non-destructive investigations. However, the neutron images are always polluted by random white spots due to the interactions between the gamma rays, scattered neutron and other radiation and chip of the charge-coupled device (CCD) camera. In this paper, we propose a novel denoising method based on a ‘detection-location-removal’ iterative strategy, which adopts an improved robust principal component analysis (RPCA) in combination with the suitable threshold selection strategy and median filters for white spots noise removal of neutron images. Experimental results based on both simulation testing and real neutron images show that the proposed method is effective in improving neutron image quality in terms of numerical criteria and visual results. The comparison with the other denoising methods shows that the proposed method possesses higher robustness and effectiveness in practical denoising applications.

Super-Resolution Magnetic Resonance Imaging of the Knee Using 2-Dimensional Turbo Spin Echo Imaging.

The purpose of this study was to assess the technical feasibility of 3-dimensional (3D) super-resolution reconstruction (SRR) of 2D turbo spin echo (TSE) knee magnetic resonance imaging (MRI) and to compare its image quality with conventional 3D TSE sampling perfection with application optimized contrast using different flip angle evolutions (SPACE) MRI. Super-resolution reconstruction 2D TSE MRI and 3D TSE SPACE images were acquired from a phantom and from the knee of 22 subjects (8 healthy volunteers and 14 patients) using a clinical 3-T scanner. For SRR, 7 anisotropic 2D TSE stacks (voxel size, 0.5 × 0.5 × 2.0 mm; scan time per stack, 1 minute 55 seconds; total scan time, 13 minutes 25 seconds) were acquired with the slice stack rotated around the phase-encoding axis. Super-resolution reconstruction was performed at an isotropic high-resolution grid with a voxel size of 0.5 × 0.5 × 0.5 mm. Direct isotropic 3D image acquisition was performed with the conventional SPACE sequence (voxel size, 0.5 × 0.5 × 0.5 mm; scan time, 12 minutes 42 seconds). For quantitative evaluation, perceptual blur metrics and edge response functions were obtained in the phantom image, and signal-to-noise and contrast-to-noise ratios were measured in the images from the healthy volunteers. Images were qualitatively evaluated by 2 independent radiologists in terms of overall image quality, edge blurring, anatomic visibility, and diagnostic confidence to assess normal and abnormal knee structures. Nonparametric statistical analysis was performed, and significance was defined for P values less than 0.05. In the phantom, perceptual blur metrics and edge response functions demonstrated a clear improvement in spatial resolution for SRR compared with conventional 3D SPACE. In healthy subjects, signal-to-noise and contrast-to-noise ratios in clinically relevant structures were not significantly different between SRR and 3D SPACE. Super-resolution reconstruction provided better overall image quality and less edge blurring than conventional 3D SPACE, yet the perceived image contrast was better for 3D SPACE. Super-resolution reconstruction received significantly better visibility scores for the menisci, whereas the visibility of cartilage was significantly higher for 3D SPACE. Ligaments had high visibility on both SRR and 3D SPACE images. The diagnostic confidence for assessing menisci was significantly higher for SRR than for conventional 3D SPACE, whereas there were no significant differences between SRR and 3D SPACE for cartilage and ligaments. The interreader agreement for assessing menisci was substantial with 3D SPACE and almost perfect with SRR, and the agreement for assessing cartilage was almost perfect with 3D SPACE and moderate with SRR. We demonstrate the technical feasibility of SRR for high-resolution isotropic knee MRI. Our SRR results show superior image quality in terms of edge blurring, but lower image contrast and fluid brightness when compared with conventional 3D SPACE acquisitions. Further contrast optimization and shortening of the acquisition time with state-of-the-art acceleration techniques are necessary for future clinical validation of SRR knee MRI.

Inside Cover: Comparison between high definition FT‐IR, Raman and AFM‐IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells (J. Biophotonics 5/2020)

AbstractThe family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones. Three vibrational spectroscopy techniques (High Definition FT‐IR, Raman and AFM‐IR) were applied for subcellular chemical imaging of cholesteryl esters in PC‐3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern, and chemical information.Further details can be found in the article by Maciej Roman, Tomasz P. Wrobel, Czeslawa Paluszkiewicz, and Wojciech M. Kwiatek (e201960094). image

Display evaluation for primary diagnosis using digital pathology.

Purpose: As pathology departments around the world contemplate digital microscopy for primary diagnosis, making an informed choice regarding display procurement is very challenging in the absence of defined minimum standards. In order to help inform the decision, we aimed to conduct an evaluation of displays with a range of technical specifications and sizes. Approach: We invited histopathologists within our institution to take part in a survey evaluation of eight short-listed displays. Pathologists reviewed a single haematoxylin and eosin whole slide image of a benign nevus on each display and gave a single score to indicate their preference in terms of image quality and size of the display. Results: Thirty-four pathologists took part in the display evaluation experiment. The preferred display was the largest and had the highest technical specifications (11.8-MP resolution, maximum luminance). The least preferred display had the lowest technical specifications (2.3-MP resolution, maximum luminance). A trend was observed toward an increased preference for displays with increased luminance and resolution. Conclusions: This experiment demonstrates a preference for large medical-grade displays with the high luminance and high resolution. As cost becomes implicated in procurement, significantly less expensive medical-grade displays with slightly lower technical specifications may be the most cost-effective option.

Perianal fistula mapping at 3 T: volumetric versus conventional MRI sequences

To evaluate volumetric contrast-enhanced three-dimensional T1-weighted (CE 3D T1) turbo spin-echo (TSE) with variable flip angle (SPACE), CE 3D T1 volumetric interpolated breath-hold examination (VIBE) sequences with conventional CE-two-dimensional (2D)-T1 and 2D-T2-weighted imaging (WI) sequences in assessing perianal fistulas. Twenty-three patients with perianal fistula were included in this prospective study and underwent pelvic magnetic resonance imaging (MRI) at 3 T including fat-supressed CE 3D T1 SPACE, CE 3D T1 VIBE, axial and coronal 2D-T1WI together with 2D-T2WI sequences in the axial and coronal planes. Acquisition times were recorded. Performance of each sequence was evaluated in terms of image quality, presence of artefacts, lesion conspicuity, fistula type, presence of abscess, visibility of internal orifice, and number of internal orifices. Results were compared with post-surgical findings defined as the reference standard. Both CE 3D T1 VIBE and CE 3D T1 SPACE were the best sequences to determine fistula type and in terms of image quality, artefacts, and determining locations of internal orifices. The number of internal orifices was detected correctly in 23 (100%) patients for CE 3D T1 SPACE and CE 3D T1 VIBE sequences, in 17 (73.9%) patients on CE-T1WI, and in seven (30.4%) patients on 2D-T2WI. Lesion conspicuity was higher for CE 3D T1 SPACE and CE 3D T1 VIBE sequences compared to the 2D sequences (p<0.05). The overall acquisition time for each 3D sequence was shorter compared to the 2D sequences combined. CE 3D T1 SPACE and CE 3D T1 VIBE sequences may outperform conventional 2D sequences in the evaluation of perianal fistulas in terms of visibility and number of internal orifices with a shorter scanning time.

Upscaling UAS Paradigm to UltraLight Aircrafts: A Low-Cost Multi-Sensors System for Large Scale Aerial Photogrammetry

The use of unmanned aerial systems (UASs) has rapidly grown in many civil applications since the early 2010s. Nowadays, a large variety of reliable low-cost UAS sensors and controllers are available. However, contrary to ultralight aircrafts (ULAs), UASs have a too small operational range to efficiently cover large areas. Flight regulations prevailing in many countries further reduced this operational range; in particular, the “within visual line of sight” rule. This study presents a new system for image acquisition and high-quality photogrammetry of large scale areas (&gt;10 km²). It was developed by upscaling the UAS paradigm, i.e., low-cost sensors and controllers, little (or no) on-board active stabilization, and adequate structure from motion photogrammetry, to an ULA platform. Because the system is low-cost (good quality-price ratio of UAS technologies), multi-sensors (large variety of available UAS sensors) and versatile (high ULA operational flexibility and more lenient regulation than for other platforms), the possible applications are numerous in miscellaneous research domains. The system was described in detail and illustrated from the flight and images acquisition to the photogrammetric routine. The system was successfully used to acquire high resolution and high quality RGB and multispectral images, and produced precisely georeferenced digital elevation model (DEM) and orthophotomosaics for a forested area of 1200 ha. The system can potentially carry any type of sensors. The system compatibility with any sensor can be tested, in terms of image quality and flight plan, with the proposed method. This study also highlighted a major technical limitation of the low-cost thermal infrared cameras: the too high integration time with respect to the flight speed of most UASs and ULAs. By providing the complete information required for reproducing the system, the authors seek to encourage its implementation in different geographical locations and scientific contexts, as well as, its combination with other sensors, in particular, laser imaging detection and ranging (LiDAR) and hyperspectral.

Optimize non-contrast head CT imaging tasks using multiple virtual monochromatic image sets in dual-energy spectral CT.

To demonstrate the value of optimizing non-contrast head imaging tasks by using the multiple virtual monochromatic spectral imaging (VMSI) sets in dual-energy spectral computed tomography (CT) and determine the optimal energy levels for various tasks in imaging head. This retrospective study includes 55 patients (29 women and 26 men, average age of 54.5±12.3years) who underwent non-contrast head CT in spectral imaging mode. For each patient, 21 VMSI sets from 40 to 140 keV at 5 keV intervals and 120 kVp-like image were reconstructed. Signal-to-noise ratio (SNR) for gray matter (GM) and white matter (WM), contrast-to-noise ratio (CNR) between GM and WM, subcalvarial artifact index (SAI) and posterior fossa artifact index (PFAI) were measured by placing regions-of-interest (ROIs) in nine areas of the brain. Two radiologists subjectively rated the image quality in terms of GM-WM differentiation, beam hardening artifacts in the subcalvarial space, and posterior fossa. The overall image noise was evaluated using a 5-point Likert scale. Measurement data were then statistically analyzed. The optimal energy level for viewing the supratentorial brain and reducing beam hardening artifacts in the subcalvarial space and posterior fossa was 65 keV and 75 keV, respectively. All corresponding measurements at these energy levels were significantly better than those measured at other energy levels (all P < 0.05). Dual-energy spectral CT imaging provides a set of virtual monochromatic spectral imaging and can be selected on-demand to optimize the imaging tasks in non-contrast head CT. VMSI reconstruction of dual-energy unenhanced head CT scans at 65-75 keV enables to maximize image quality.

Linear and Circular UWB Millimeter-Wave and Terahertz Monostatic Near-Field Synthetic Aperture Imaging.

Millimeter-wave and terahertz frequencies offer unique characteristics to simultaneously obtain good spatial resolution and penetrability. In this paper, a robust near-field monostatic focusing technique is presented and successfully applied for the internal imaging of different penetrable geometries. These geometries and environments are related to the growing need to furnish new vehicles with radar-sensing devices that can visualize their surroundings in a clear and robust way. Sub-millimeter-wave radar sensing offers enhanced capabilities in providing information with a high level of accuracy and quality, even under adverse weather conditions. The aim of this paper was to research the capability of this radar system for imaging purposes from an analytical and experimental point of view. Two sets of measurements, using reference targets, were performed in the W band at 100 GHz (75 to 110 GHz) and terahertz band at 300 GHz (220 to 330 GHz). The results show spatial resolutions of millimeters in both the range (longitudinal) and the cross-range (transversal) dimensions for the two different imaging geometries in terms of the location of the transmitter and receiver (frontal or lateral views). The imaging quality in terms of spatial accuracy and target material parameter was investigated and optimized.

CHARACTERIZATION OF TWO CT SYSTEMS USING A CHANNELIZED HOTELLING OBSERVER AND NPS METRIC.

We investigated the performances of two computed tomography (CT) systems produced by the same manufacturers (Somatom Flash and Edge Siemens) with different detector technologies (Ultrafast Ceramic and Stellar) and different generation of iterative reconstruction (IR) algorithms (SAFIRE and ADMIRE). A homemade phantom was scanned and the images were reconstructed with filtered back-projection (FBP) and IR algorithms. In terms of image quality, the performances of the systems were checked using the low-contrast detectability, evaluated by a Channelized Hotelling Observer (CHO), and the noise power spectrum (NPS). The analysis with CHO showed the best performance of Edge respect to Flash system for both FBP and IR algorithms. This better behavior, which reaches 20%, has been ascribed to the Stellar detector. From the NPS analysis, the noise reduction due to Stellar detector was 57%, moreover ADMIRE algorithm preserves a more traditional CT image texture appearance versus SAFIRE due to a lower NPS peak shift.

Hybrid higher‐order total variation model for multiplicative noise removal

As an important, challenging, and difficult problem in image processing, multiplicative noise removal (MNR) has attracted great attention. To this end, many variational methods have been effectively proposed in the past few decades. Among these variational methods, total variation (TV) and its higher-order extensions are very effective, where the former can preserve sharp edges but cause some undesirable staircase effects and the latter can better reduce the staircase effects but sometimes smooth the image details. To overcome the drawbacks while taking full use of their merits, the authors propose a novel hybrid higher-order TV regularisation model for MNR, in which the novelty of the proposed model consists of combining the image prior information of first-order and second-order derivatives to propose a novel higher-order regulariser, named as hybrid higher-order TV (HHTV). More specifically, a more preferable equivalent formulation of HHTV is derived. Then, they use the derived equivalent formulation to design an efficient alternating iterative algorithm to solve the proposed model. Finally, the experimental results demonstrate that the proposed HHTV method outperforms several state-of-the-art methods in terms of image quality and convergence speed.

A Preliminary Study of Gadobenate Dimeglumine Enhanced Magnetic Resonance Angiography Detection of Prostatic Artery Anatomy

Background: To describe the feasibility of anatomy and imaging findings of the prostatic arteries (PAs) on contrast-enhanced MR angiography (ce-MRA). Methods: This prospective study enrolled 7 patients undergoing pelvic 3.0 T ce-MRA and selective pelvic digital subtraction angiography before prostatic artery embolization (PAE). Four patients underwent fluoroscopically triggered ce-MRA and 3 patients underwent time-resolved imaging of contrast kinetics. PAE was performed within one week of MRA. Two radiologists independently analyzed MRA findings. The analyzed parameters included the number of independent PAs and their origin, trajectory, and anastomoses with adjacent arteries. Image quality in terms of signal-to-noise ratio (SNR) and contrast-tonoise ratio (CNR) to visualize common iliac artery and its branches including prostatic arteries was compared between the two groups. Results: A total of 17 PAs were demonstrated in 7 cases. The diagnostic accuracy of MRA in detecting bilateral PAs was 58.82%. Moreover, 71.42% of the patients had only one PA in one side, and 7.14% had independent PAs. The mean diameter of 10 PAs was 1.39 ± 0.46 mm. About 29.41% of PAs originated from the obturator arteries and 29.41% from the pudendal arteries. Bilateral PAs were found to be symmetric in 3 cases and asymmetric in 4 cases. No significance difference was found between the image quality of the two groups, except for the signal intensity of the right common iliac artery and right internal iliac artery and SNR and CNR of the left common iliac artery. Conclusions: This study shows that ce-MRA is able to detect PA, and can provide guidance for PAE.

Deringing and denoising in extremely under-sampled Fourier single pixel imaging.

Undersampling in Fourier single pixel imaging (FSI) is often employed to reduce imaging time for real-time applications. However, the undersampled reconstruction contains ringing artifacts (Gibbs phenomenon) that occur because the high-frequency target information is not recorded. Furthermore, by employing 3-step FSI strategy (reduced measurements with low noise suppression) with a low-grade sensor (i.e., photodiode), this ringing is coupled with noise to produce unwanted artifacts, lowering image quality. To improve the imaging quality of real-time FSI, a fast image reconstruction framework based on deep convolutional autoencoder network (DCAN) is proposed. The network through context learning over FSI artifacts is capable of deringing, denoising, and recovering details in 256 × 256 images. The promising experimental results show that the proposed deep-learning-based FSI outperforms conventional FSI in terms of image quality even at very low sampling rates (1-4%).

Radiation dose reduction and static image quality assessment using a channelized hotelling observer on an angiography system upgraded with clarity IQ

The goal of this paper was the comparison of radiation dose and imaging quality before and after the Clarity IQ technology installation in a Philips AlluraXper FD20/20 angiography system using a Channelized Hotelling Observer model (CHO). The core characteristics of the Allura Clarity IQ technology are its real-time noise reduction algorithms (NRT) combined with state-of-the-art hardware; this technology allows to implement acquisition protocols able to significantly reduce patient entrance dose. To measure the system performances in terms of image quality we used a contrast detail phantom in a clinical scatter condition. A Leeds TO10 phantom has been imaged between two 10 cm thick homogeneous solid water slabs. Fluoroscopy images were acquired using a cerebral protocol at 3 dose levels (low, medium and high) with a field- of view (FOV) of 31 cm. Cineangiography images were acquired using a cerebral protocol at 2 fps. Thus, 4 acquisitions were obtained for the conventional technology and 4 acquisitions were taken after the Clarity IQ upgrade, for a total of 8 different image sets. A validated 40 Gabor channels CHO with an internal noise model compared the image sets. Human observers’ studies were carried out to tune the internal noise parameter. We showed that the CHO did not detect any significant difference between any of the image sets acquired using the two technologies. Consequently, this x–ray imaging technology provides a non-inferior image quality with an average patient dose reduction of 57% and 28% respectively in cineangiography and fluoroscopy. The Clarity IQ installation has certainly allowed a considerable improvement in patient and staff safety, while maintaining the same image quality.

Comparison between high definition FT-IR, Raman and AFM-IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells.

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones, especially in the case of demanding biological samples. Three vibrational spectroscopy techniques (high definition Fourier-transform infrared [FT-IR], Raman and atomic force microscopy infrared [AFM-IR]) were applied for subcellular chemical imaging of cholesteryl esters in PC-3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern and chemical information. All tested techniques were found to be useful in chemical imaging of cholesterol derivatives in cancer cells. The results obtained from FT-IR and Raman imaging showed to be comparable, whereas those achieved from AFM-IR study exhibited higher spectral heterogeneity. It confirms AFM-IR method as a powerful tool in local chemical imaging of cells at the nanoscale level. Furthermore, due to polarization effect, p-polarized AFM-IR spectra showed strong enhancement of lipid bands when compared to FT-IR.

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.

Deep unfolding of a proximal interior point method for image restoration

Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution. However, the level of performance of these methods significantly depends on a set of parameters, which can be estimated through computationally expensive and time-consuming methods. In contrast, deep learning offers very generic and efficient architectures, at the expense of explainability, since it is often used as a black-box, without any fine control over its output. Deep unfolding provides a convenient approach to combine variational-based and deep learning approaches. Starting from a variational formulation for image restoration, we develop iRestNet, a neural network architecture obtained by unfolding a proximal interior point algorithm. Hard constraints, encoding desirable properties for the restored image, are incorporated into the network thanks to a logarithmic barrier, while the barrier parameter, the stepsize, and the penalization weight are learned by the network. We derive explicit expressions for the gradient of the proximity operator for various choices of constraints, which allows training iRestNet with gradient descent and backpropagation. In addition, we provide theoretical results regarding the stability of the network for a common inverse problem example. Numerical experiments on image deblurring problems show that the proposed approach compares favorably with both state-of-the-art variational and machine learning methods in terms of image quality.

Propagation-Based Phase-Contrast CT of the Breast Demonstrates Higher Quality Than Conventional Absorption-Based CT Even at Lower Radiation Dose

Propagation-based phase-contrast CT (PB-CT) is an advanced X-ray imaging technology that exploits both refraction and absorption of the transmitted X-ray beam. This study was aimed at optimizing the experimental conditions of PB-CT for breast cancer imaging and examined its performance relative to conventional absorption-based CT (AB-CT) in terms of image quality and radiation dose. Surgically excised breast mastectomy specimens (n = 12) were scanned using both PB-CT and AB-CT techniques under varying imaging conditions. To evaluate the radiological image quality, visual grading characteristics (VGC) analysis was used in which 11 breast specialist radiologists compared the overall image quality of PB-CT images with respect to the corresponding AB-CT images. The area under the VGC curve was calculated to measure the differences between PB-CT and AB-CT images. The highest radiological quality was obtained for PB-CT images using a 32 keV energy X-ray beam and by applying the Homogeneous Transport of Intensity Equation phase retrieval with the value of its parameter γ set to one-half of the theoretically optimal value for the given materials. Using these optimized conditions, the image quality of PB-CT images obtained at 4 mGy and 2 mGy mean glandular dose was significantly higher than AB-CT images at 4 mGy (AUCVGC = 0.901, p = 0.001 and AUCVGC = 0.819, p = 0.011, respectively). PB-CT achieves a higher radiological image quality compared to AB-CT even at a considerably lower mean glandular dose. Successful translation of the PB-CT technique for breast cancer imaging can potentially result in improved breast cancer diagnosis.

DeepImaging: A Ground Moving Target Imaging Based on CNN for SAR-GMTI System

Imaging of ground multiple moving targets in a synthetic aperture radar (SAR) system is a challenging task due to the fact that targets are defocused owing to motions and contaminated by the strong background clutter. Motivated by recent advances in deep learning, a novel deep convolutional neural network (CNN)-based method, DeepImaging, is proposed for ground moving target imaging (GMTIm). Different from conventional imaging methods relying on the prior knowledge of imaging, the proposed DeepImaging is directly trained to learn an implicit imaging model of multiple moving targets. It is free of motion parameter estimation and iteration process. Then, the trained DeepImaging, as an imaging processor, can be applied to the SAR complex received data after clutter suppression to achieve the multiple moving target imaging and the residual clutter elimination simultaneously. Simulations and experiments on the Gotcha data show that the proposed method achieves significant improvements over existing state-of-the-art GMTIm methods in terms of imaging quality and efficiency.