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In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets. [ABSTRACT FROM AUTHOR]

Background and purpose: This study sought to evaluate the relationship of progressive corticospinal tract (CST) degeneration with survival in patients with amyotrophic lateral sclerosis (ALS). Methods: Forty‐one ALS patients and 42 healthy controls were prospectively recruited from the Canadian ALS Neuroimaging Consortium. Magnetic resonance imaging scanning and clinical evaluations were performed on participants at three serial visits with 4‐month intervals. Texture analysis was performed on T1‐weighted magnetic resonance imaging scans and the texture feature 'autocorrelation' was quantified. Whole‐brain group‐level comparisons were performed between patient subgroups. Linear mixed models were used to evaluate longitudinal progression. Region‐of‐interest and 3D voxel‐wise Cox proportional‐hazards regression models were constructed for survival prediction. For all survival analyses, a second independent cohort was used for model validation. Results: Autocorrelation of the bilateral CST was increased at baseline and progressively increased over time at a faster rate in ALS short survivors. Cox proportional‐hazards regression analyses revealed autocorrelation of the CST as a significant predictor of survival at 5 years follow‐up (hazard ratio 1.28, p = 0.005). Similarly, voxel‐wise whole‐brain survival analyses revealed that increased autocorrelation of the CST was associated with shorter survival. ALS patients stratified by median autocorrelation in the CST had significantly different survival times using the Kaplan–Meier curve and log‐rank tests (χ2 = 7.402, p = 0.007). Conclusions: Severity of cerebral degeneration is associated with survival in ALS. CST degeneration progresses faster in subgroups of patients with shorter survival. Neuroimaging holds promise as a tool to improve patient management and facilitation of clinical trials. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SISR) using deep convolutional neural networks (CNNs) achieves the state-of-the-art performance. Most existing SISR models mainly focus on pursuing high peak signal-to-noise ratio (PSNR) and neglect textures and details. As a result, the recovered images are often perceptually unpleasant. To address this issue, in this paper, we propose a texture and detail-preserving network (TDPN), which focuses not only on local region feature recovery but also on preserving textures and details. Specifically, the high-resolution image is recovered from its corresponding low-resolution input in two branches. First, a multi-reception field based branch is designed to let the network fully learn local region features by adaptively selecting local region features in different reception fields. Then, a texture and detail-learning branch supervised by the textures and details decomposed from the ground-truth high resolution image is proposed to provide additional textures and details for the super-resolution process to improve the perceptual quality. Finally, we introduce a gradient loss into the SISR field and define a novel hybrid loss to strengthen boundary information recovery and to avoid overly smooth boundary in the final recovered high-resolution image caused by using only the MAE loss. More importantly, the proposed method is model-agnostic, which can be applied to most off-the-shelf SISR networks. The experimental results on public datasets demonstrate the superiority of our TDPN on most state-of-the-art SISR methods in PSNR, SSIM and perceptual quality. We will share our code on https://github.com/tocaiqing/TDPN. [ABSTRACT FROM AUTHOR]

Progressive cerebral degeneration in amyotrophic lateral sclerosis (ALS) remains poorly understood. Here, three‐dimensional (3D) texture analysis was used to study longitudinal gray and white matter cerebral degeneration in ALS from routine T1‐weighted magnetic resonance imaging (MRI). Participants were included from the Canadian ALS Neuroimaging Consortium (CALSNIC) who underwent up to three clinical assessments and MRI at four‐month intervals, up to 8 months after baseline (T0). Three‐dimensional maps of the texture feature autocorrelation were computed from T1‐weighted images. One hundred and nineteen controls and 137 ALS patients were included, with 81 controls and 84 ALS patients returning for at least one follow‐up. At baseline, texture changes in ALS patients were detected in the motor cortex, corticospinal tract, insular cortex, and bilateral frontal and temporal white matter compared to controls. Longitudinal comparison of texture maps between T0 and Tmax (last follow‐up visit) within ALS patients showed progressive texture alterations in the temporal white matter, insula, and internal capsule. Additionally, when compared to controls, ALS patients had greater texture changes in the frontal and temporal structures at Tmax than at T0. In subgroup analysis, slow progressing ALS patients had greater progressive texture change in the internal capsule than the fast progressing patients. Contrastingly, fast progressing patients had greater progressive texture changes in the precentral gyrus. These findings suggest that the characteristic longitudinal gray matter pathology in ALS is the progressive involvement of frontotemporal regions rather than a worsening pathology within the motor cortex, and that phenotypic variability is associated with distinct progressive spatial pathology. [ABSTRACT FROM AUTHOR]

An efficient unambiguous stereo matching technique is presented in this paper. Our main contribution is to introduce a new reliability measure to dynamic programming approaches in general. For stereo vision application, the reliability of a proposed match on a scanline is defined as the cost difference between the globally best disparity assignment that includes the match and the globally best assignment that does not include the match. A reliability-based dynamic programming algorithm is derived accordingly, which can selectively assign disparities to pixels when the corresponding reliabilities exceed a given threshold. The experimental results show that the new approach can produce dense (>70 percent of the unoccluded pixels) and reliable (error rate Index Terms--Stereo, dynamic programming.

Background: Texture analysis (TA) is an image-analysis technique that detects complex intervoxel statistical patterns. 3D TA has shown potential in detecting cerebral degeneration not perceptible to the human eye in many neurological disorders. The reliability of this method's application in a multicenter study is unknown.Purpose: To assess the intrasite and intersite reliability of a novel 3D TA method from data acquired systematically from the Canadian ALS Neuroimaging Consortium (CALSNIC).Study Type: Prospective multicenter data with harmonized MR sequence parameters acquired from five sites.Population: Six healthy subjects.Field Strength: 3T 3D-MPRAGE and 3D-SPGR T1 -weighted MRI of the brain.Assessment: Voxel-based 3D TA was performed on the whole brain to produce texture maps.Statistical Tests: Intra- and intersite reliability of texture features was assessed using a two-way mixed-effects model for intraclass correlation coefficients (ICC). ICCs were calculated in a region-of-interest (ROI) analysis of predetermined anatomically relevant areas. A voxelwise approach was used to assess the whole brain.Results: In the ROI analyses, intrasite reliability was excellent (ICC > 0.75) across most regions and texture features (autocorrelation [autoc], contrast [contr], energy [energ]). Intersite reliability was excellent for most regions with autoc, ranging from fair to excellent for contr, and ICCs ranging from poor to good (<0.40-0.75) for energ. Voxelwise analyses revealed a large range in ICC across the brain for both intrasite and intersite ICCs (0.0-0.90), with higher reliability in the cortical gray matter compared with deeper subcortical structures.Data Conclusion: Overall, the reliability of 3D TA was highly dependent on texture feature, region studied, and method of analysis (ROI or voxelwise). Intrasite reproducibility was good to excellent, and better than intersite. ROI-based analyses present higher reliability in comparison with voxelwise analyses. Autoc has overall excellent reliability. These factors might be considered when designing future 3D TA studies.Level Of Evidence: 2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2020;51:1200-1209. [ABSTRACT FROM AUTHOR]

Most existing trackers use bounding boxes for object tracking. However, the background contained in the bounding box inevitably decreases the accuracy of the target model, which affects the performance of the tracker and is particularly pronounced for non-rigid objects. To address the above issue, this paper proposes a novel hybrid level set model, which can robustly address the issue of topology changing, occlusions and abrupt motion in non-rigid object tracking by accurately tracking the object contour. In particular, an appearance model is first obtained by repeatedly training and relabeling the initial labeled frame using competing one-class SVMs. Then, by integrating the trained appearance model, an edge detector and image spatial information into the level set model, a new hybrid level set model is presented, which accurately locates the object contour and feeds back to the competing one-class SVMs to update the appearance model of the next frame. In addition, a motion model is defined to predict the accurate location of the object when occlusion and abrupt motion occur in the next frame. Finally, the experimental results on state-of-the-art benchmarks demonstrate the feasibility and effectiveness of the proposed model and the superiority of the proposed method over existing trackers in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity and noise are two common issues in images but inevitably lead to significant challenges for image segmentation and is particularly pronounced when the two issues simultaneously appear in one image. As a result, most existing level set models yield poor performance when applied to this images. To this end, this paper proposes a novel hybrid level set model, named adaptive variational level set model (AVLSM) by integrating an adaptive scale bias field correction term and a denoising term into one level set framework, which can simultaneously correct the severe inhomogeneous intensity and denoise in segmentation. Specifically, an adaptive scale bias field correction term is first defined to correct the severe inhomogeneous intensity by adaptively adjusting the scale according to the degree of intensity inhomogeneity while segmentation. More importantly, the proposed adaptive scale truncation function in the term is model-agnostic, which can be applied to most off-the-shelf models and improves their performance for image segmentation with severe intensity inhomogeneity. Then, a denoising energy term is constructed based on the variational model, which can remove not only common additive noise but also multiplicative noise often occurred in medical image during segmentation. Finally, by integrating the two proposed energy terms into a variational level set framework, the AVLSM is proposed. The experimental results on synthetic and real images demonstrate the superiority of AVLSM over most state-of-the-art level set models in terms of accuracy, robustness and running time. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fully automatic image colorization method for grayscale images using neural network and optimization. For a determined training set including the gray images and its corresponding color images, our method segments grayscale images into superpixels and then extracts features of particular points of interest in each superpixel. The obtained features and their RGB values are given as input for, the training colorization neural network of each pixel. To achieve a better image colorization effect in shorter running time, our method further propagates the resulting color points to neighboring pixels for improved colorization results. In the propagation of color, we present a cost function to formalize the premise that neighboring pixels should have the maximum positive similarity of intensities and colors; we then propose our solution to solving the optimization problem. At last, a guided image filter is employed to refine the colorized image. Experiments on a wide variety of images show that the proposed algorithms can achieve superior performance over the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls. [ABSTRACT FROM AUTHOR]