Shadow Artifacts Research Articles

BreakNet: discontinuity-resilient multi-scale transformer segmentation of retinal layers

Visible light optical coherence tomography (vis-OCT) is gaining traction for retinal imaging due to its high resolution and functional capabilities. However, the significant absorption of hemoglobin in the visible light range leads to pronounced shadow artifacts from retinal blood vessels, posing challenges for accurate layer segmentation. In this study, we present BreakNet, a multi-scale Transformer-based segmentation model designed to address boundary discontinuities caused by these shadow artifacts. BreakNet utilizes hierarchical Transformer and convolutional blocks to extract multi-scale global and local feature maps, capturing essential contextual, textural, and edge characteristics. The model incorporates decoder blocks that expand pathways to enhance the extraction of fine details and semantic information, ensuring precise segmentation. Evaluated on rodent retinal images acquired with prototype vis-OCT, BreakNet demonstrated superior performance over state-of-the-art segmentation models, such as TCCT-BP and U-Net, even when faced with limited-quality ground truth data. Our findings indicate that BreakNet has the potential to significantly improve retinal quantification and analysis.

POLYCORE: Polygon-based contour refinement for improved Intravascular Ultrasound Segmentation

Segmentation of the coronary vessel wall in intravascular ultrasound is a fundamental step in guiding coronary intervention. However, it is an challenging task, even for highly skilled cardiologists, due to image artefacts and shadowed regions caused by calcified plaque, guide wires and vessel side branches. Recently, dense-based neural networks have been applied to this task, however, they often fail to predict anatomically plausible contours in these low-signal areas. We propose a novel methodology called Polygon-based Contour Refiner (POLYCORE) that addresses topological error in dense-based segmentation networks using a relational inductive bias through higher-order connections between vertices to learn anatomically rational contours. Our approach remedies the over-smoothing phenomena common in polygon networks by introducing a new vector field refinement module which enables pixel-level detail to be added in an iterative process. POLYCORE is enhanced with augmented polygon aggregation which we show is more effective than typical dense-based test-time augmentation strategies. We achieve state-of-the-art results on two diverse datasets, observing particular improvements when segmenting the lumen structure and in topologically-challenging regions containing shadow artefacts. Our source code is available here: http://orcid.org/https://github.com/kitbransby/POLYCORE.

Efficient Environment Map Rendering Based on Decomposition

AbstractThis paper presents an efficient environment map sampling algorithm designed to render high‐quality, low‐noise images with only a few light samples, making it ideal for real‐time applications. We observe that bright pixels in the environment map produce high‐frequency shading effects, such as sharp shadows and shading, while the rest influence the overall tone of the scene. Building on this insight, our approach differs from existing techniques by categorizing the pixels in an environment map into emissive and non‐emissive regions and developing specialized algorithms tailored to the distinct properties of each region. By decomposing the environment lighting, we ensure that light sources are deposited on bright pixels, leading to more accurate shadows and specular highlights. Additionally, this strategy allows us to exploit the smoothness in the low‐frequency component by rendering a smaller image with more lights, thereby enhancing shading accuracy. Extensive experiments demonstrate that our method significantly reduces shadow artefacts and image noise compared to previous techniques, while also achieving lower numerical errors across a range of illumination types, particularly under limited sample conditions.

Three-Dimensional Transperineal Ultrasound Assessment of the Anal Sphincter Immediately After Vaginal Birth: An Exploratory Study.

To assess the feasibility of acquiring adequate transperineal ultrasound (TPUS) volumes of the anal sphincter (AS) immediately after vaginal birth, the reproducibility of its measurements, and detecting defects therein. Secondary analysis of TPUS volumes of the AS, acquired immediately after vaginal birth with a transversely oriented convex probe. Two independent experts ranked off-line image quality as "inadequate," "adequate," or "ideal" using the Point-of-Care Ultrasound Image Quality scale. On "adequate" and "ideal" quality volumes, the length of the external AS at 6 and 12 o'clock, and the volume of the external and internal AS were measured. Additionally, volumes were screened for AS defects on tomographic ultrasound imaging. Subsequently, we rated the intra- and interrater agreement on those findings. Of 183 volumes, 162 were considered "adequate" or of "ideal" quality (88.5%). Reasons for "inadequacy" were shadow artifacts (16/21), poor resolution (3/21), incomplete acquisition (1/21), or aberrant AS morphology (1/21). The intrarater reliability of two-dimensional (2D) and three-dimensional (3D) measurements was excellent, whereas interrater reliability was fair to good for 2D measurements and good for 3D measurements. In those tomographic ultrasound imaging (TUI) sequences including AS defects, the intra- and interrater reliability of the defect measurement were excellent [intraclass correlation coefficient (ICC) = 0.92 (0.80-0.94)] and moderate [ICC = 0.72 (0.63-0.79)]. In this cohort, there were only few (4/48; 8.3%) AS defects. However, grading them was poorly reproducible between experts. TPUS of the AS immediately after vaginal birth yields adequate image quality and allows for reproducible measurements. In the few patients with AS defects, there was good agreement on the presence, but it was poor for the extent of defects.

Seeing beyond skin deep: High-resolution ultrasound in dermatology-A comprehensive review and future prospects.

High-resolution ultrasound (HRUS), operating at frequencies of 20-25 MHz, is a non-invasive imaging tool that offers dermatologists the ability to visualize structures beneath the skin surface. The objective of this review is to present a comprehensive overview of HRUS applications, emphasising its utility in diagnosing, characterising and managing various dermatological conditions. We undertook a comprehensive literature review on the dermatological application of HRUS across Medline, Embase and Cochrane Library databases, while also incorporating our own clinical experience of over 16 years with the tool. In normal skin, the epidermis and dermis are hyperechoic, and the subcutaneous layer is hypoechoic. Basal cell carcinomas appear hypoechoic with irregular margins, while the presence of hyperechoic inclusion bodies suggests aggressive pathology. Squamous cell carcinomas pose challenges due to acoustic shadow artefacts from the thickened stratum corneum. Melanomas are homogenous hypoechoic lesions, with HRUS used to accurately predict Breslow thickness. HRUS provides dermatologists with a valuable adjunct to traditional clinical examination. Future advancement in image resolution and the standardisation of diagnostic parameters may further expand its utility.

Maximum spherical mean value filtering for whole-brain QSM.

To develop a tissue field-filtering algorithm, called maximum spherical mean value (mSMV), for reducing shadow artifacts in QSM of the brain without requiring brain-tissue erosion. Residual background field is a major source of shadow artifacts in QSM. The mSMV algorithm filters large field-magnitude values near the border, where the maximum value of the harmonic background field is located. The effectiveness of mSMV for artifact removal was evaluated by comparing existing QSM algorithms in numerical brain simulation as well as using in vivo human data acquired from 11 healthy volunteers and 93 patients. Numerical simulation showed that mSMV reduces shadow artifacts and improves QSM accuracy. Better shadow reduction, as demonstrated by lower QSM variation in the gray matter and higher QSM image quality score, was also observed in healthy subjects and in patients with hemorrhages, stroke, and multiple sclerosis. The mSMV algorithm allows QSM maps that are substantially equivalent to those obtained using SMV-filtered dipole inversion without eroding the volume of interest.

Physics-based Optical Coherence Tomography Angiography (OCTA) Image Correction for Shadow Compensation.

Optical coherence tomography (OCT) is being widely applied in clinical studies to investigate insight into the retina under the retinal pigment epithelium. Optical coherence tomography angiography (OCTA) is one of the functional extensions of OCT, for visualizing retinal circulation. Due to obstruction of light propagation, such as vitreous floaters or pupil boundaries, OCTA remains challenged by shadow artifacts that can disrupt volumetric data. Detecting and removing these shadow artifacts are crucial when quantifying indicators of retinal disease progression. We simplified an optical attenuation model of shadow formation in OCTA to a linear illumination transformation. And learn its parameters using an adversarial neural network. Our framework also consists of a sub-network for shadows automatic detection. We experimented our method on 28 OCTA images of normal eyes and compared the non-perfusion area (NPA), an index to measure retinal vascularity. The results showed that the NPA adjusted to a reasonable range after image processing using our method. Furthermore, we tested 150 OCTA images of synthesis artifacts, and the mean absolute error(MAE) values reached 0.83 after shadow removal.

Deep Semi-Supervised Ultrasound Image Segmentation by Using a Shadow Aware Network With Boundary Refinement.

Accurate ultrasound (US) image segmentation is crucial for the screening and diagnosis of diseases. However, it faces two significant challenges: 1) pixel-level annotation is a time-consuming and laborious process; 2) the presence of shadow artifacts leads to missing anatomy and ambiguous boundaries, which negatively impact reliable segmentation results. To address these challenges, we propose a novel semi-supervised shadow aware network with boundary refinement (SABR-Net). Specifically, we add shadow imitation regions to the original US, and design shadow-masked transformer blocks to perceive missing anatomy of shadow regions. Shadow-masked transformer block contains an adaptive shadow attention mechanism that introduces an adaptive mask, which is updated automatically to promote the network training. Additionally, we utilize unlabeled US images to train a missing structure inpainting path with shadow-masked transformer, which further facilitates semi-supervised segmentation. Experiments on two public US datasets demonstrate the superior performance of the SABR-Net over other state-of-the-art semi-supervised segmentation methods. In addition, experiments on a private breast US dataset prove that our method has a good generalization to clinical small-scale US datasets.

Full 3-D modulation transfer function estimation of tomosynthesis system using modified Richardson-Lucy deconvolution.

Tomosynthesis systems are three-dimensional (3-D) medical imaging devices that operate over limited acquisition angles using low radiation dosages. To measure the spatial resolution performance of a tomosynthesis system, the modulation transfer function (MTF) is widely used as a quantitative evaluationmetric. We previously introduced a method to estimate the full 3-D MTF of a cone-beam computed tomography system using two-dimensional (2-D) Richardson-Lucy (RL) deconvolution with Tikhonov-Miller regularization. However, this method can not be applied directly to estimate the 3-D MTF of a tomosynthesis system, since the unique artifacts (i.e., shadow artifacts, spreading tails, directional blurring, and high-level noise) of the system produce several errors that lower the estimation performance. Varying positions of the negative pixels due to shadow artifacts and spreading tails cause inconsistent deconvolution performances at each of the directional projections, and the severe noise in the reconstructed images cause noise amplification during estimation. This work proposes several modifications to the previous method to resolve the inconsistent performance and noise amplification errors to increase the full 3-D MTF estimationaccuracy. Three modifications were introduced to the 2-D RL deconvolution to prevent estimation errors and improve MTF estimation performance: non-negativity relaxation function, cost function to terminate the iterative process of RL deconvolution, and regularization strength for noise control. To validate the effectiveness of the proposed modifications, we reconstructed sphere phantoms from simulation and experimental tomosynthesis studies in the iso-center and offset-center positions as well as estimated the full 3-D MTFs using the previous and proposed methods. We compared the 3-D render images, central plane images, and center profiles of the estimated 3-D MTFs and calculated the full widths at half and tenth maximum for quantitativeevaluation. The previous method cannot estimate the full 3-D MTF of a tomosynthesis system; its inaccurate negative pixel relaxation produces circular-shaped errors, and the mean squared error based simple cost function for termination causes inconsistent estimation at each directional projection to diminish the clear edges of the low-frequency drop and missing sample regions. Noise amplification from lack of noise regularization is also seen in the previous method results. Compared to the previous method, the proposed method shows superior estimation performance at reducing errors in both the simulation and experimental studies regardless of object position. The proposed method preserves the low-frequency drop, missing sample regions from the limited acquisition angles, and missing cone region from the offset-center position; the estimated MTFs also show FWHM and FWTM values close to those of the ideal MTFs than with the previousmethod. This work presents a method to estimate the full 3-D MTF of a tomosynthesis system. The proposed modifications prevent circular-shaped errors and noise amplification due to the geometry for limited acquisition angles and high noise levels. Compared to our previous method, the proposed scheme show better performance for estimating the 3-D MTF of the tomosynthesissystem.

Organ boundary delineation for automated diagnosis from multi-center using ultrasound images

Delineation of prostate boundary is a critical procedure in multi-modal prostate registration and patient-specific anatomical modeling for surgical planning and image-guided biopsy of prostate cancer. Accurate segmentation of the prostate from medical transrectal ultrasound (TRUS) images is challenging due to the presence of shadow artifacts that may cause missing or ambiguous boundaries of the prostate. In this work, we developed a novel hybrid model consisting of four primary merits to address this issue: (1) integrating the properties of the principal curve to fit the data center intelligently and of the artificial intelligence model to decrease model error; (2) developing a principal curve-based adaptive polygon tracking model to acquire the vertices sequence, which is designed via newly merging an automatic judgment of data radius algorithm; (3) introducing the quantum characteristics into the historical storage-based evolutionary model, which is proposed for the first attempt, whilst innovatively introduced new mutation technique and cuckoo search model; (4) presenting an interpretable mathematical model (explained via parameters of neural network) to express the smooth boundary. Promising experimental outcomes denote that our model has satisfactory segmentation performance. Future work will concentrate on improving the trustworthy performance of the model.

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation.

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

The ability of Segmenting Anything Model (SAM) to segment ultrasound images.

Accurate ultrasound (US) image segmentation is important for disease screening, diagnosis, and prognosis assessment. However, US images typically have shadow artifacts and ambiguous boundaries that affect US segmentation. Recently, Segmenting Anything Model (SAM) from Meta AI has demonstrated remarkable potential in a wide range of applications. The purpose of this paper was to conduct an initial evaluation of the ability for SAM to segment US images, particularly in the event of shadow artifacts and ambiguous boundaries. We evaluated SAM's performance on three US datasets of different tissues, including multi-structure cardiac tissue, thyroid nodules, and the fetal head. Results indicated that SAM generally performs well with US images with clear tissue structures, but it has limited performance in the event of shadow artifacts and ambiguous boundaries. Thus, creating an improved SAM that considers the characteristics of US images is significant for automatic and accurate US segmentation.

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.

Dynamic Infrared Imaging of Vitreous Floaters.

To evaluate the use of dynamic infrared (IR) imaging as a tool for the objective evaluation of symptomatic vitreous floaters and to correlate it with the patient symptomatology. Retrospective study that examined 66 eyes of 44 patients with symptomatic and asymptomatic vitreous opacities. Patients were imaged using the Heidelberg Spectralis dynamic infrared (IR) image in video mode to record the vitreous movements and shadow artifacts within 30 degrees of the center of the macula. Patients were also asked how symptomatic their vitreous floaters were from absent to severe. After reviewing IR videos and OCT, a grading system was created to evaluate the floaters and a masked reader was asked to evaluate the videos and OCT based on the grading system created. A total of 66 eyes were identified and examined with the IR videos, 50 were symptomatic, and 16 were asymptomatic. After masked review and analysis of the IR videos, there were 4 characteristics that correlated with the patient's symptoms: size, location, movement, and density of obscuration of the OCT B Scan by the vitreous opacity. A table with grading of these characteristics was created to analyze how symptomatic patients were. A masked grader was asked to grade the videos and OCT using the grading system created. A positive correlation was found between the masked grader and the symptoms of patients. (0.70039; p < 0.00001). Dynamic IR video capture of vitreous opacities is a new imaging technique that can qualitatively assess vitreous opacities in a way that correlates to a patient's symptoms. This imaging modality can provide a qualitative assessment of the patient's severity of symptoms based on the location, density, and movement of the visualized vitreous opacities in the imaged video.

Artifact removal for unpaired thorax CBCT images using a feature fusion residual network and contextual loss.

Cone-beam computed tomography (CBCT) has become a more and more active cutting-edge topic in the international computed tomography (CT) research due to its advantages of fast scanning speed, high ray utilization rate and high precision. However, scatter artifacts affect the imaging performance of CBCT, which hinders its application seriously. Therefore, our study aimed to propose a novel algorithm for scatter artifacts suppression in thorax CBCT based on a feature fusion residual network (FFRN), where the contextual loss was introduced to adapt the unpaired datasets better. In the method we proposed, a FFRN with contextual loss was used to reduce CBCT artifacts in the region of chest. Unlike L1 or L2 loss, the contextual loss function makes input images which are not aligned strictly in space available, so we performed it on our unpaired datasets. The algorithm aims to reduce artifacts via studying the mapping between CBCT and CT images, where the CBCT images were set as the beginning while planning CT images as the end. The proposed method effectively removes artifacts in thorax CBCT, including shadow artifacts and cup artifacts which can be collectively referred to as uneven grayscale artifacts, in the CBCT image, and perform well in preserving details and maintaining the original shape. In addition, the average PSNR number of our proposed method achieved 27.7, which was higher than the methods this paper referred which indicated the significance of our method. What is revealed by the results is that our method provides a highly effective, rapid, and robust solution for the removal of scatter artifacts in thorax CBCT images. Moreover, as is shown in Table 1, our method has demonstrated better artifact reduction capability than other methods.

Ultrasonographic assessment of early leakage in intestinal sutures in dogs.

Intestinal suture dehiscence is one of the most feared complications following gastrointestinal surgery in both human and veterinary medicine, increasing the morbidity and mortality of these patients. Clinical and laboratory early signs of septic peritonitis are not always easily identifiable while prompt treatment should help decrease postoperative morbidity and mortality. The aim of this study is to describe the ultrasonographic (US) features of confirmed leakage of intestinal sutures (LIS) and to evaluate if this imaging technique can be useful as noninvasive tool for the early diagnosis of LIS. Seven dogs developed LIS in a range of three-four days after gastrointestinal surgery and four of these developed a second dehiscence. On B-mode ultrasonography, all intestinal surgical sites were identified and characterized by a bowel focal thickening with reduced or absent wall layering and the presence of hyperechoic, double-walled foci at regular intervals (suture material). Furthermore, hyperechoic linear interfaces associated with dirty acoustic shadowing and comet-tail artifacts crossing the intestinal wall to free-float in peritoneal cavity or in a saccate collection have been documented. On the basis of these preliminary results, canine abdominal ultrasound seems to be a useful diagnostic technique for post-operative monitoring of patients undergoing intestinal surgery, allowing early detection of signs of a LIS, before the patient develops clinical signs of septic peritonitis.

Caseous Mitral Annular Calcification Presenting as Retinal Artery Occlusion

Caseous mitral annular calcification (CMAC), sometimes called liquefaction necrosis of mitral annular calcification (MAC), is a rare variant of MAC, a chronic degenerative process that progresses with age. It is a degenerative abnormality of the fibrous tissue and typically involves the posterior annulus, appearing as a smooth mass with no flow or acoustic shadow artefacts. It can be differentiated from other cardiac masses by CT and MRI. Whilst benign in nature, it is associated with a range of pathologies, such as mitral valve dysfunction, arrhythmias, and systemic embolisation. Given the risk of systemic embolisation, surgery may be appropriate, but there is no clear consensus in the literature in patients who are asymptomatic. This case highlights a case of CMAC presenting with a retinal artery occlusion, and was managed conservatively. Background The mitral annulus is a key component of the mitral valve, ensuring appropriate function by facilitating complete closure of the leaflets during systole. With age, MAC can occur, potentially resulting in mitral valve dysfunction, infective endocarditis, and arrhythmias. CMAC is a rare variant of MAC, typically involving the posterior annulus. Whilst benign, the risk of complications, such as systemic embolisation, may indicate a surgical approach to management, even in patients who are asymptomatic. Case Presentation A 60-year-old female, presenting with sudden loss of vision in the lower part of her left eye due to a branch retinal artery embolus, was admitted from ophthalmology services. An outpatient transthoracic echocardiogram showed an abnormal mass on the posterior annulus of the mitral valve. Transoesophageal echocardiography identified a calcified posterior aspect of the mitral annulus with normal leaflet mobility and trivial regurgitation, and an echogenic mass attached to the ventricular aspect of the mitral annulus (at the level of posteromedial commissure), 9x4 mm in size. A cardiac CT showed a caseous mitral valve with evidence of rupture of the calcified shell. Discussion with the surgical multidisciplinary team resulted in a conservative approach, with follow-up echo for monitoring. Conclusion Whilst a benign and rare variant of MAC, CMAC is associated with a range of pathologies, including mitral valve disease, arrhythmias, and systemic embolisation. Currently, there is no standardised management approach for CMAC. Surgery is currently recommended in the context of already known surgically indicated pathologies, such as severe mitral valve disease or systemic embolisation. However, this conservatively-managed patient has had no further complications with stable echocardiographic appearance on repeat echo 3 months following initial exam.

A multi-channel input convolutional neural network for artifact reduction in quantitative susceptibility mapping

To develop a deep learning-based QSM reconstruction method for reducing artifacts to improve the accuracy of magnetic susceptibility results. To eliminate artifacts caused by susceptibility interfaces with gigantic differences, we propose a multi-channel input convolutional neural network for artifact reduction (MAR-CNN) for solving the dipole inversion problem in QSM. In this neural network, the original tissue field was first separated into two components, which were subsequently imported as additional channels into a multi-channel 3D U-Net. MAR-CNN was compared with 3 conventional model-based methods, namely truncated k-space deconvolution (TKD), morphology enabled dipole inversion (MEDI), and improved sparse linear equation and least squares method (iLSQR), and with a deep learning method (QSMnet). High-frequency error norm, peak signal-to-noise ratio, normalized root mean squared error, and structure similarity index were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the results obtained using MAR-CNN had superior peak signal-to-noise ratio (43.12±1.19) and normalized root mean squared error (51.98± 3.65) to those of TKD, MEDI, iLSQR and QSMnet. MAR-CNN outperformed QSMnet reconstruction on all the 4 quantitative metrics with significant differences (P < 0.05). Experiment on data of simulated hemorrhagic lesion demonstrated that MAR-CNN produced less shadow artifacts around the bleeding lesion than the other 4 methods. The proposed MAR-CNN for artifact reduction is capable of improving the accuracy of deep learning- based QSM reconstruction to effectively reduce artifacts.

Diagnostic value ultrasound signs of stones less than or equal to 10 mm and clinico-radiological variants of ureteric colic

ObjectiveTo determine the diagnostic value of ultrasound signs of urinary stones less than or equal to 10 mm and to determine clinico-radiological variants of ureteric colic. MethodsA total of 455 ultrasound investigations were performed in patients referring to emergency department with urolithiasis and symptoms suspected of ureteric colic between January 2021 and May 2021. In addition to microscopic evaluation of urine sediment to detect different crystals and non-contrast spiral computed tomography to detect stones, B-mode and color Doppler sonography was performed to assess the presence of acoustic shadow (AS) and twinkle artifacts (TA) as possible signs of stone(s) in ureter. ResultsWhile the sensitivity and specificity of AS and TA were higher than 90% in patients with stones greater than 5 mm; positive prognostic values of these parameters were found to be extremely low for stones with sizes of 1–3 mm with specificity and sensitivity values not exceeding 53%. The sensitivity and specificity of AS and TA in the upper and lower ureters were higher for stones greater than or equal to 5 than for compared to those less than 5 mm. At the same time, the diagnostic values of TA and AS for middle ureter stones were very limited. The most prevalent clinico-radiological variants of ureteric colic were types I, III, and V being observed in 39%, 28% and 21% cases, respectively. ConclusionOur results demonstrate that TA and AS parameters seem to have a very low sensitivity and specificity in the diagnosis of urinary stones less than 5 mm. The diagnostic value of TA and AS increase significantly in stones greater than or equal to 5 mm. Therefore, clinicians need to be very careful for overestimating the diagnostic values of TA and AS for stones less than 5 mm and non-contrast spiral computed tomography must be the method of choice for patients presenting to emergency department with ureteric colic.