To examine the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically multi-coil and inherently noisy. Although DL-based reconstruction methods trained on fully sampled data can enable high reconstruction quality, obtaining large, noise-free datasets is impractical. We leverage Generalized Stein's Unbiased Risk Estimate (GSURE) for denoising. We evaluate two DL-based reconstruction methods: Diffusion Probabilistic Models (DPMs) and Model-Based Deep Learning (MoDL). We evaluate the impact of denoising on the performance of these DL-based methods in solving accelerated multi-coil magnetic resonance imaging (MRI) reconstruction. The experiments were carried out on T2-weighted brain and fat-suppressed proton-density knee scans. We observed that self-supervised denoising enhances the quality and efficiency of MRI reconstructions across various scenarios. Specifically, employing denoised images rather than noisy counterparts when training DL networks results in lower normalized root mean squared error (NRMSE), higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) across different SNR levels, including 32, 22, and 12 dB for T2-weighted brain data, and 24, 14, and 4 dB for fat-suppressed knee data. We showed that denoising is an essential pre-processing technique capable of improving the efficacy of DL-based MRI reconstruction methods under diverse conditions. By refining the quality of input data, denoising enables training more effective DL networks, potentially bypassing the need for noise-free reference MRI scans.

To develop a self-supervised and memory-efficient deep learning image reconstruction method for 4D non-Cartesian MRI with high resolution and a large parametric dimension. The deep factor model (DFM) represents a parametric series of 3D multicontrast images using a neural network conditioned by the inversion time using efficient zero-filled reconstructions as input estimates. The model parameters are learned in a single-shot learning (SSL) fashion from the k-space data of each acquisition. A compatible transfer learning (TL) approach using previously acquired data is also developed to reduce reconstruction time. The DFM is compared to subspace methods with different regularization strategies in a series of phantom and in vivo experiments using the MPnRAGE acquisition for multicontrast <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> </mrow> <annotation>$$ {T}_1 $$</annotation></semantics> </math> imaging and quantitative <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> </mrow> <annotation>$$ {T}_1 $$</annotation></semantics> </math> estimation. DFM-SSL improved the image quality and reduced bias and variance in quantitative <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics> <mrow> <msub><mrow><mi>T</mi></mrow> <mrow><mn>1</mn></mrow> </msub> </mrow> <annotation>$$ {T}_1 $$</annotation></semantics> </math> estimates in both phantom and in vivo studies, outperforming all other tested methods. DFM-TL reduced the inference time while maintaining a performance comparable to DFM-SSL and outperforming subspace methods with multiple regularization techniques. The proposed DFM offers a superior representation of the multicontrast images compared to subspace models, especially in the highly accelerated MPnRAGE setting. The self-supervised training is ideal for methods with both high resolution and a large parametric dimension, where training neural networks can become computationally demanding without a dedicated high-end GPU array.

Chordomas are rare, aggressive tumors of notochordal origin, commonly affecting the spine and skull base. Skull Base Chordomas (SBCs) comprise approximately 39% of cases, with an incidence of less than 1 per million annually in the U.S. Prognosis remains poor due to resistance to chemotherapy, often requiring extensive surgical resection and adjuvant radiotherapy. Current classification methods based on chromosomal deletions are invasive and costly, presenting a need for alternative diagnostic tools. Radiomics allows for non-invasive SBC diagnosis and treatment planning. We developed and validated radiomic-based models using MRI data to predict Overall Survival (OS) and Progression-Free Survival following Surgery (PFSS) in SBC patients. Machine learning classifiers, including eXtreme Gradient Boosting (XGBoost), were employed along with feature selection techniques. Unsupervised clustering identified radiomic-based subgroups, which were correlated with chromosomal deletions and clinical outcomes. Our XGBoost model demonstrated superior predictive performance, achieving an area under the curve (AUC) of 83.33% for OS and 80.36% for PFSS, outperforming other classifiers. Radiomic clustering revealed two SBC groups with differing survival and molecular characteristics, strongly correlating with chromosomal deletion profiles. These findings indicate that radiomics can non-invasively characterize SBC phenotypes and stratify patients by prognosis. Radiomics shows promise as a reliable, non-invasive tool for the prognostication and classification of SBCs, minimizing the need for invasive genetic testing and supporting personalized treatment strategies.

This study aims to develop an paraspinal muscle-based radiomics model using a machine learning approach and assess its utility in predicting postoperative outcomes among patients with lumbar degenerative spondylolisthesis (LDS). This retrospective study included a total of 155 patients diagnosed with LDS who underwent single-level posterior lumbar interbody fusion (PLIF) surgery between January 2021 and October 2023. The patients were divided into train and test cohorts in a ratio of 8:2.Radiomics features were extracted from axial T2-weighted lumbar MRI, and seven machine learning models were developed after selecting the most relevant radiomic features using T-test, Pearson correlation, and Lasso. A combined model was then created by integrating both clinical and radiomics features. The performance of the models was evaluated through ROC, sensitivity, and specificity, while their clinical utility was assessed using AUC and Decision Curve Analysis (DCA). The LR model demonstrated robust predictive performance compared to the other machine learning models evaluated in the study. The combined model, integrating both clinical and radiomic features, exhibited an AUC of 0.822 (95% CI, 0.761-0.883) in the training cohorts and 0.826 (95% CI, 0.766-0.886) in the test cohorts, indicating substantial predictive capability. Moreover, the combined model showed superior clinical benefit and increased classification accuracy when compared to the radiomics model alone. The findings suggest that the combined model holds promise for accurately predicting postoperative outcomes in patients with LDS and could be valuable in guiding treatment strategies and assisting clinicians in making informed clinical decisions for LDS patients.

Convolutional neural network (CNN) models, such as U-Net, V-Net, and DeepLab, have achieved remarkable results across various medical imaging modalities, and ultrasound. Additionally, hybrid Transformer-based segmentation methods have shown great potential in medical image analysis. Despite the breakthroughs in feature extraction through self-attention mechanisms, these methods are computationally intensive, especially for three-dimensional medical imaging, posing significant challenges to graphics processing unit (GPU) hardware. Consequently, the demand for lightweight models is increasing. To address this issue, we designed a high-accuracy yet lightweight model that combines the strengths of CNNs and Transformers. We introduce Slim UNEt TRansformers++ (Slim UNETR++), which builds upon Slim UNETR by incorporating Medical ConvNeXt (MedNeXt), Spatial-Channel Attention (SCA), and Efficient Paired-Attention (EPA) modules. This integration leverages the advantages of both CNN and Transformer architectures to enhance model accuracy. The core component of Slim UNETR++ is the Slim UNETR++ block, which facilitates efficient information exchange through a sparse self-attention mechanism and low-cost representation aggregation. We also introduced throughput as a performance metric to quantify data processing speed. Experimental results demonstrate that Slim UNETR++ outperforms other models in terms of accuracy and model size. On the BraTS2021 dataset, Slim UNETR++ achieved a Dice accuracy of 93.12% and a 95% Hausdorff distance (HD95) of 4.23mm, significantly surpassing mainstream relevant methods such as Swin UNETR.

Tear size and shape are known to prognosticate the efficacy of surgical rotator cuff (RC) repair; however, current manual measurements on magnetic resonance images (MRIs) exhibit high interobserver variabilities and exclude 3-dimensional (3D) morphologic information. This study aimed to develop algorithms for automatic 3D analyses of posterosuperior full-thickness RC tear to enable efficient and precise tear evaluation and 3D tear visualization. A deep-learning network for automatic segmentation of the tear region in coronal and sagittal multicenter MRI was trained with manually segmented (consensus of 3 experts) proton density- and T2-weighted MRI of shoulders with full-thickness posterosuperior tears (n = 200). Algorithms for automatic measurement of tendon retraction, tear width, tear area, and automatic Patte classification considering the 3D morphology of the shoulder were implemented and evaluated against manual segmentation (n = 59). Automatic Patte classification was calculated using automatic segmented humerus and scapula on T1-weighted MRI of the same shoulders. Tears were automatically segmented, enabling 3D visualization of the tear, with a mean Dice coefficient of 0.58 ± 0.21 compared to an interobserver variability of 0.46 ± 0.21. The mean absolute error of automatic tendon retraction and tear width measurements (4.98 ± 4.49 mm and 3.88 ± 3.18 mm) were lower than the interobserver variabilities (5.42 ± 7.09 mm and 5.92 ± 1.02 mm). The correlations of all measurements performed on automatic tear segmentations compared with those on consensus segmentations were higher than the interobserver correlation. Automatic Patte classification achieved a Cohen kappa value of 0.62, compared with the interobserver variability of 0.56. Retraction calculated using standard linear measures underestimated the tear size relative to measurements considering the curved shape of the humeral head, especially for larger tears. Even on highly heterogeneous data, the proposed algorithms showed the feasibility to successfully automate tear size analysis and to enable automatic 3D visualization of the tear situation. The presented algorithms standardize cross-center tear analyses and enable the calculation of additional metrics, potentially improving the predictive power of image-based tear measurements for the outcome of surgical treatments, thus aiding in RC tear diagnosis, treatment decision, and planning.

To assess the performance of an industry-developed deep learning (DL) algorithm to reconstruct low-resolution Cartesian T1-weighted dynamic contrast-enhanced (T1w) and T2-weighted turbo-spin-echo (T2w) sequences and compare them to standard sequences. Female patients with indications for breast MRI were included in this prospective study. The study protocol at 1.5 Tesla MRI included T1w and T2w. Both sequences were acquired in standard resolution (T1_S and T2_S) and in low-resolution with following DL reconstructions (T1_DL and T2_DL). For DL reconstruction, two convolutional networks were used: (1) Adaptive-CS-Net for denoising with compressed sensing, and (2) Precise-Image-Net for resolution upscaling of previously downscaled images. Overall image quality was assessed using 5-point-Likert scale (from 1=non-diagnostic to 5=excellent). Apparent signal-to-noise (aSNR) and contrast-to-noise (aCNR) ratios were calculated. Breast Imaging Reporting and Data System (BI-RADS) agreement between different sequence types was assessed. A total of 47 patients were included (mean age, 58±11 years). Acquisition time for T1_DL and T2_DL were reduced by 51% (44 vs. 90 s per dynamic phase) and 46% (102 vs. 192 s), respectively. T1_DL and T2_DL showed higher overall image quality (e.g., 4 [IQR, 4-4] for T1_S vs. 5 [IQR, 5-5] for T1_DL, P<0.001). Both, T1_DL and T2_DL revealed higher aSNR and aCNR than T1_S and T2_S (e.g., aSNR: 32.35±10.23 for T2_S vs. 27.88±6.86 for T2_DL, P=0.014). Cohen k agreement by BI-RADS assessment was excellent (0.962, P<0.001). DL for denoising and resolution upscaling reduces acquisition time and improves image quality for T1w and T2w breast MRI.

Cortical lesions in multiple sclerosis (MS) severely affect cognition, but their longitudinal evolution and impact on specific cognitive functions remain understudied. This study investigates the evolution of function-specific cognitive functioning over 10 years in people with MS and assesses the influence of cortical lesion load and formation on these trajectories. In this prospectively collected study, people with MS underwent 3T MRI (T1 and fluid-attenuated inversion recovery) at 3 study visits between 2008 and 2022. Cognitive functioning was evaluated based on neuropsychological assessment reflecting 7 cognitive functions: attention; executive functioning (EF); information processing speed (IPS); verbal fluency; and verbal, visuospatial, and working memory. Cortical lesions were manually identified on artificial intelligence-generated double-inversion recovery images. Linear mixed models were constructed to assess the temporal evolution between cortical lesion load and function-specific cognitive decline. In addition, analyses were stratified by MS disease stage: early and late relapsing-remitting MS (cutoff disease duration at 15 years) and progressive MS. The study included 223 people with MS (mean age, 47.8 ± 11.1 years; 153 women) and 62 healthy controls. All completed 5-year follow-up, and 37 healthy controls and 94 with MS completed 10-year follow-up. At baseline, people with MS exhibited worse functioning of IPS and working memory. Over 10 years, cognitive decline was most severe in attention, verbal memory, and EF. At baseline, people with MS had a median cortical lesion count of 7 (range 0-73), which was related to subsequent decline in attention (B[95% CI] = -0.22 [-0.40 to -0.03]) and verbal fluency (B[95% CI] = -0.23[-0.37 to -0.09]). Over time, cortical lesions increased by a median count of 4 (range -2 to 71), particularly in late and progressive disease, and was related to decline in verbal fluency (B [95% CI] = -0.33 [-0.51 to -0.15]). The associations between (change in) cortical lesion load and cognitive decline were not modified by MS disease stage. Cognition worsened over 10 years, particularly affecting attention, verbal memory, and EF, while preexisting impairments were worst in other functions such as IPS. Worse baseline cognitive functioning was related to baseline cortical lesions, whereas baseline cortical lesions and cortical lesion formation were related to cognitive decline in functions less affected at baseline. Accumulating cortical damage leads to spreading of cognitive impairments toward additional functions.

Study DesignLiterature review of key topics related to degenerative cervical myelopathy (DCM) with critical appraisal and clinical recommendations.ObjectiveThis article summarizes several key current topics related to the management of DCM.MethodsRecent literature related to the management of DCM was reviewed. Four articles were selected and critically appraised. Recommendations were graded as Strong or Conditional.ResultsArticle 1: The Relationship Between pre-operative MRI Signal Intensity and outcomes. <b>Conditional</b> recommendation to use diffusion-weighted imaging MR signal changes in the cervical cord to evaluate prognosis following surgical intervention for DCM. Article 2: Efficacy and Safety of Surgery for Mild DCM. <b>Conditional</b> recommendation that surgery is a valid option for mild DCM with favourable clinical outcomes. Article 3: Effect of Ventral vs Dorsal Spinal Surgery on Patient-Reported Physical Functioning in Patients With Cervical Spondylotic Myelopathy: A Randomized Clinical Trial. <b>Strong</b> recommendation that there is equipoise in the outcomes of anterior vs posterior surgical approaches in cases where either technique could be used. Article 4: Machine learning-based cluster analysis of DCM phenotypes. <b>Conditional</b> recommendation that clinicians consider pain, medical frailty, and the impact on health-related quality of life when counselling patients.ConclusionsDCM requires a multidimensional assessment including neurological dysfunction, pain, impact on health-related quality of life, medical frailty and MR imaging changes in the cord. Surgical treatment is effective and is a valid option for mild DCM. In patients where either anterior or posterior surgical approaches can be used, both techniques afford similar clinical benefit albeit with different complication profiles.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.