This study aims to explore a method based on three-dimensional cervical spinal cord reconstruction, radiomics feature extraction, and machine learning to build a postoperative prognosis prediction model for patients with cervical spondylotic myelopathy (CSM). It also evaluates the predictive performance of different cervical spinal cord segmentation strategies and machine learning algorithms. A retrospective analysis is conducted on 126 CSM patients who underwent posterior single-door laminoplasty from January 2017 to December 2022. Three different cervical spinal cord segmentation strategies (narrowest segment, surgical segment, and entire cervical cord C1-C7) are applied to preoperative MRI images for radiomics feature extraction. Good clinical prognosis is defined as a postoperative JOA recovery rate ≥50%. By comparing the performance of 8 machine learning algorithms, the optimal cervical spinal cord segmentation strategy and classifier are selected. Subsequently, clinical features (smoking history, diabetes, preoperative JOA score, and cSVA) are combined with radiomics features to construct a clinical-radiomics prediction model. Among the three cervical spinal cord segmentation strategies, the SVM model based on the narrowest segment performed best (AUC=0.885). Among clinical features, smoking history, diabetes, preoperative JOA score, and cSVA are important indicators for prognosis prediction. When clinical features are combined with radiomics features, the fusion model achieved excellent performance on the test set (accuracy=0.895, AUC=0.967), significantly outperforming either the clinical model or the radiomics model alone. This study validates the feasibility and superiority of three-dimensional radiomics combined with machine learning in predicting postoperative prognosis for CSM. The combination of radiomics features based on the narrowest segment and clinical features can yield a highly accurate prognosis prediction model, providing new insights for clinical assessment and individualized treatment decisions. Future studies need to further validate the stability and generalizability of this model in multi-center, large-sample cohorts.

The prospective, multicenter German National Cohort (NAKO) provides a unique dataset of cardiac magnetic resonance (CMR) cine images. Effective processing of these images requires a robust segmentation and quality control pipeline. A deep learning model for semantic segmentation, based on the nnU-Net architecture, was applied to full-cycle short-axis cine images from 29,908 baseline participants. The primary objective was to determine data on structure and function for both ventricles (LV, RV), including end-diastolic volumes (EDV), end-systolic volumes (ESV), and LV myocardial mass. Quality control measures included a visual assessment of outliers in morphofunctional parameters, inter- and intra-ventricular phase differences, and time-volume curves (TVC). These were adjudicated using a five-point rating scale, ranging from five (excellent) to one (non-diagnostic), with ratings of three or lower subject to exclusion. The predictive value of outlier criteria for inclusion and exclusion was evaluated using receiver operating characteristics analysis. The segmentation model generated complete data for 29,609 participants (incomplete in 1.0%), of which 5,082 cases (17.0%) underwent visual assessment. Quality assurance yielded a sample of 26,899 (90.8%) participants with excellent or good quality, excluding 1,875 participants due to image quality issues and 835 participants due to segmentation quality issues. TVC was the strongest single discriminator between included and excluded participants (AUC: 0.684). Of the two-category combinations, the pairing of TVC and phases provided the greatest improvement over TVC alone (AUC difference: 0.044; p<0.001). The best performance was observed when all three categories were combined (AUC: 0.748). By extending the quality-controlled sample to include mid-level 'acceptable' quality ratings, a total of 28,413 (96.0%) participants could be included. The implemented pipeline facilitated the automated segmentation of an extensive CMR dataset, integrating quality control measures. This methodology ensures that ensuing quantitative analyses are conducted with a diminished risk of bias.

Large Language Models (LLMs) have shown strong performance in text-based healthcare tasks. However, their utility in image-based applications remains unexplored. We investigate the effectiveness of LLMs for medical imaging tasks, specifically glioma classification and segmentation, and compare their performance to that of traditional convolutional neural networks (CNNs). Using the BraTS 2020 dataset of multi-modal brain MRIs, we evaluated a general-purpose vision-language LLM (LLaMA 3.2 Instruct) both before and after fine-tuning, and benchmarked its performance against custom 3D CNNs. For glioma classification (Low-Grade vs. High-Grade), the CNN achieved 80% accuracy and balanced precision and recall. The general LLM reached 76% accuracy but suffered from a specificity of only 18%, often misclassifying Low-Grade tumors. Fine-tuning improved specificity to 55%, but overall performance declined (e.g., accuracy dropped to 72%). For segmentation, three methods - center point, bounding box, and polygon extraction, were implemented. CNNs accurately localized gliomas, though small tumors were sometimes missed. In contrast, LLMs consistently clustered predictions near the image center, with no distinction of glioma size, location, or placement. Fine-tuning improved output formatting but failed to meaningfully enhance spatial accuracy. The bounding polygon method yielded random, unstructured outputs. Overall, CNNs outperformed LLMs in both tasks. LLMs showed limited spatial understanding and minimal improvement from fine-tuning, indicating that, in their current form, they are not well-suited for image-based tasks. More rigorous fine-tuning or alternative training strategies may be needed for LLMs to achieve better performance, robustness, and utility in the medical space.

3D structural Magnetic Resonance Imaging (MRI) brain scans are commonly acquired in clinical settings to monitor a wide range of neurological conditions, including neurodegenerative disorders and stroke. While deep learning models have shown promising results analyzing 3D MRI across a number of brain imaging tasks, most are highly tailored for specific tasks with limited labeled data, and are not able to generalize across tasks and/or populations. The development of self-supervised learning (SSL) has enabled the creation of large medical foundation models that leverage diverse, unlabeled datasets ranging from healthy to diseased data, showing significant success in 2D medical imaging applications. However, even the very few foundation models for 3D brain MRI that have been developed remain limited in resolution, scope, or accessibility. In this work, we present a general, high-resolution SimCLR-based SSL foundation model for 3D brain structural MRI, pre-trained on 18,759 patients (44,958 scans) from 11 publicly available datasets spanning diverse neurological diseases. We compare our model to Masked Autoencoders (MAE), as well as two supervised baselines, on four diverse downstream prediction tasks in both in-distribution and out-of-distribution settings. Our fine-tuned SimCLR model outperforms all other models across all tasks. Notably, our model still achieves superior performance when fine-tuned using only 20% of labeled training samples for predicting Alzheimer's disease. We use publicly available code and data, and release our trained model at https://github.com/emilykaczmarek/3D-Neuro-SimCLR, contributing a broadly applicable and accessible foundation model for clinical brain MRI analysis.

T2 weighted magnetic resonance imaging has become a commonly used noninvasive examination method for the diagnosis of Polymyositis (PM). The data regarding the comparison of deep learning and radiomics in the diagnosis of PM is still lacking. This study investigates the feasibility of 3D convolutional neural network (CNN) in the prediction of PM, with comparison to radiomics. A total of 120 patients (with 60 PM) were from center A, and 30 (with 15 PM) were from B, and 46 (with 23 PM) were from C. The data from center A was used as training data, and data from B as validation data, and data from C as external test data. The magnetic resonance radiomics features of rectus femoris were obtained for all cases. The maximum correlation minimum redundancy and least absolute shrinkage and selection operator regression were used before establishing a radiomics score model. A 3D CNN classification model was trained with "monai" based on 150 data with labels. A 3D Unet segmentation model was also trained with "monai" based on 196 original data and their segmentation of rectus femoris. The accuracy on the external test data was compared between 2 methods by using the paired chi-square test. PM and non-PM cases did not differ in age or gender (P > .05). The 3D CNN classification model achieved accuracy of 97% in validation data. The sensitivity, specificity, accuracy and positive predictive value of the 3D CNN classification model in the external test data were 96% (22/23), 91% (21/23), 93% (43/46), and 92% (22/24), respectively. The radiomics score achieved accuracy of 90% in the validation data. The sensitivity, specificity, accuracy, and positive predictive value of the radiomics score in the external test data were 70% (16/23), 65% (15/23), 67% (31/46), and 67% (16/24), respectively, significantly lower than that of CNN model (P = .035). The 3D segmentation model for rectus femoris on T2 weighted magnetic resonance images was obtained with dice similarity coefficient of 0.71. 3D CNN model is not inferior to radiomics score in the prediction of PM. The combination of deep learning and radiomics is recommended for the evaluation of PM in future clinical practice.

Automated thalamic nuclear segmentation has contributed towards a shift in neuroimaging analyses from treating the thalamus as a homogeneous, passive relay, to a set of individual nuclei, embedded within distinct brain-wide circuits. However, many studies continue to widely rely on FreeSurfers segmentation of T1-weighted structural MRIs, despite their poor intrathalamic nuclear contrast. Meanwhile, a convolutional neural network tool has been developed for FreeSurfer, using information from both diffusion and T1-weighted MRIs. Another popular thalamic nuclear segmentation technique is HIPS-THOMAS, a multi-atlas-based method that leverages white-matter-like contrast synthesized from T1-weighted MRIs. However, rigorous comparisons amongst methods remain scant, and the thalamic atlases against which these methods have been assessed have their own limitations. These issues may compromise the quality of cross-species comparisons, structural and functional connectivity studies in health and disease, as well as the efficacy of neuromodulatory interventions targeting the thalamus. Here, we report, for the first time, comparisons amongst HIPS-THOMAS, the standard FreeSurfer segmentation, and its more recent development, against two thalamic atlases as silver-standard ground-truths. We used two cohorts of healthy adults, and one cohort of patients in the chronic phase of autoimmune limbic encephalitis. In healthy adults, HIPS-THOMAS surpassed, not only the standard FreeSurfer segmentation, but also its more recent, diffusion-based update. The improvements made with the latter relative to the former were limited to a few nuclei. Finally, the standard FreeSurfer method underperformed, relative to the other two, in distinguishing between patients and healthy controls based on the affected anteroventral and pulvinar nuclei. In light of the above findings, we provide recommendations on the use of automated segmentation methods of the human thalamus using structural brain imaging.

Brain tumour detection and classification are critical for improving patient prognosis and treatment planning. However, manual identification from magnetic resonance imaging (MRI) scans is time-consuming, error-prone, and reliant on expert interpretation. The increasing complexity of tumour characteristics necessitates automated solutions to enhance accuracy and efficiency. This study introduces a novel ensemble deep learning model, boosted deformable and residual convolutional network with bi-directional convolutional long short-term memory (BDefRCNLSTM), for the classification and segmentation of brain tumours. The proposed framework integrates entropy-based local binary pattern (ELBP) for extracting spatial semantic features and employs the enhanced sooty tern optimisation (ESTO) algorithm for optimal feature selection. Additionally, an improved X-Net model is utilised for precise segmentation of tumour regions. The model is trained and evaluated on Figshare, Brain MRI, and Kaggle datasets using multiple performance metrics. Experimental results demonstrate that the proposed BDefRCNLSTM model achieves over 99% accuracy in both classification and segmentation, outperforming existing state-of-the-art approaches. The findings establish the proposed approach as a clinically viable solution for automated brain tumour diagnosis. The integration of optimised feature selection and advanced segmentation techniques improves diagnostic accuracy, potentially assisting radiologists in making faster and more reliable decisions.

Predicting the spatio-temporal progression of brain tumors is essential for guiding clinical decisions in neuro-oncology. We propose a hybrid mechanistic learning framework that combines a mathematical tumor growth model with a guided denoising diffusion implicit model (DDIM) to synthesize anatomically feasible future MRIs from preceding scans. The mechanistic model, formulated as a system of ordinary differential equations, captures temporal tumor dynamics including radiotherapy effects and estimates future tumor burden. These estimates condition a gradient-guided DDIM, enabling image synthesis that aligns with both predicted growth and patient anatomy. We train our model on the BraTS adult and pediatric glioma datasets and evaluate on 60 axial slices of in-house longitudinal pediatric diffuse midline glioma (DMG) cases. Our framework generates realistic follow-up scans based on spatial similarity metrics. It also introduces tumor growth probability maps, which capture both clinically relevant extent and directionality of tumor growth as shown by 95th percentile Hausdorff Distance. The method enables biologically informed image generation in data-limited scenarios, offering generative-space-time predictions that account for mechanistic priors.

The diffusion MRI Neurite Exchange Imaging model offers a promising framework for probing gray matter microstructure by estimating parameters such as compartment sizes, diffusivities, and inter-compartmental water exchange time. However, existing protocols require long scan times. This study proposes a reduced acquisition scheme for the Connectome 2.0 scanner that preserves model accuracy while substantially shortening scan duration. We developed a data-driven framework using explainable artificial intelligence with a guided recursive feature elimination strategy to identify an optimal 8-feature subset from a 15-feature protocol. The performance of this optimized protocol was validated in vivo and benchmarked against the full acquisition and alternative reduction strategies. Parameter accuracy, preservation of anatomical contrast, and test-retest reproducibility were assessed. The reduced protocol yielded parameter estimates and cortical maps comparable to the full protocol, with low estimation errors in synthetic data and minimal impact on test-retest variability. Compared to theory-driven and heuristic reduction schemes, the optimized protocol demonstrated superior robustness, reducing the deviation in water exchange time estimates by over two-fold. In conclusion, this hybrid optimization framework enables viable imaging of neurite exchange in 14 minutes without loss of parameter fidelity. This approach supports the broader application of exchange-sensitive diffusion magnetic resonance imaging in neuroscience and clinical research, and offers a generalizable method for designing efficient acquisition protocols in biophysical parameter mapping.

Magnetic resonance imaging (MRI) is the gold standard for brain imaging. Deep learning (DL) algorithms have been proposed to aid in the diagnosis of diseases such as Alzheimer's disease (AD) from MRI scans. However, DL algorithms can suffer from shortcut learning, in which spurious features, not directly related to the output label, are used for prediction. When these features are related to protected attributes, they can lead to performance bias against underrepresented protected groups, such as those defined by race and sex. In this work, we explore the potential for shortcut learning and demographic bias in DL based AD diagnosis from MRI. We first investigate if DL algorithms can identify race or sex from 3D brain MRI scans to establish the presence or otherwise of race and sex based distributional shifts. Next, we investigate whether training set imbalance by race or sex can cause a drop in model performance, indicating shortcut learning and bias. Finally, we conduct a quantitative and qualitative analysis of feature attributions in different brain regions for both the protected attribute and AD classification tasks. Through these experiments, and using multiple datasets and DL models (ResNet and SwinTransformer), we demonstrate the existence of both race and sex based shortcut learning and bias in DL based AD classification. Our work lays the foundation for fairer DL diagnostic tools in brain MRI. The code is provided at https://github.com/acharaakshit/ShortMR