MR Imaging Research Articles

Morphological Evaluation of Corpus Callosum Atrophy Over Time in Relapsing Remitting Multiple Sclerosis

Objective: Multiple sclerosis (MS) is a chronic central nervous system (CNS) disease that generally affects young adults and is marked by inflammation, demyelination, and neurodegeneration. Magnetic resonance imaging (MRI) is widely used diagnosis tool for relapsing remitting MS (RRMS). Corpus callosum (CC), the largest commissural tract in brain which is associated with both cognitive and physical impairment by atrophy in MS. Our study aimed to evaluate CC in RRMS patients using MR images and compare it to measurements from healthy controls within the same age. Methods: We manually measured changes in CC thickness in T1 brain MR images of RRMS patients in 2017, 2019, and 2022. Results: Our results showed that control group had greater thickness, length, and index values in all CC sections compared to patient group. Additionally, a significant difference was observed in thickness of genu and splenium sections and CC index between patient and control groups. However, no significant difference was detected in truncus part of CC or overall CC length. CC measurements in patient group decreased over time, with 1st MRI showing greater values than 2nd and 3rd MRI scans. Furthermore, there was a statistically significant difference in thickness of truncus part of CC and volume values of subcortical areas between 2nd-3rd and 1st-3rd MRI measurements. Conclusion: As a result of these findings, our study provides important information about changes in CC measurements for MS patients.

Imaging Findings and MRI Patterns in a Cohort of 18q Chromosomal Abnormalities.

The abnormalities of the long arm of chromosome 18 (18q) constitute a complex spectrum. We aimed to systematically analyze their MR imaging features. We hypothesized that there would be variable but recognizable white matter and structural patterns in this cohort. In this retrospective cohort study, we included pediatric patients with a proved abnormality of 18q between 2000-2022. An age- and sex-matched control cohort was also constructed. Thirty-six cases, median MR imaging age 19.6 months (4.3-59.3), satisfied our inclusion criteria. Most were female (25, 69%, F:M ratio 2.2:1). Fifty MR imaging studies were analyzed, and 35 (70%) had delayed myelination. Two independent readers scored brain myelination with excellent interrater reliability. Three recognizable evolving MR imaging patterns with distinct age distributions and improving myelination scores were identified: Pelizaeus-Merzbacher disease-like (9.9 months, 37), intermediate (22 months, 48), and washed-out pattern (113.6 months, 53). Etiologically, MRIs were analyzed across 3 subgroups: 18q deletion (34, 69%), trisomy 18 (10, 21%), and ring chromosome 18 (5, 10%). Ring chromosome 18 had the highest myelination lag (27, P = .005) and multifocal white matter changes (P = .001). Trisomy 18 had smaller pons and cerebellar dimensions (anteposterior diameter pons, P = .002; corpus callosum vermis, P < .001; and transverse cerebellar diameter, P = .04). In this cohort of 18q chromosomal abnormalities, MR imaging revealed recognizable patterns correlating with improving brain myelination. Imaging findings appear to be on a continuum with more severe white matter abnormalities in ring chromosome 18 and greater prevalence of structural abnormalities of the pons and cerebellum in trisomy 18.

Incidental findings on MRI for the evaluation of endometriosis: prevalence and clinical significance.

This study aimed to analyze the prevalence and clinical significance of incidental findings on MRI for endometriosis. Differences between patients with and without evidence of deep infiltrating endometriosis on MRI were to be examined. This was a retrospective, descriptive cross-sectional single-center study. All patients who received a pelvic MRI for endometriosis between April 2021 and February 2023 were included. The presence and frequency of incidental findings were noted after review of all MR images and radiology reports. The potential clinical significance of the findings was analyzed. Differences in the frequency of incidental findings between patients with and without evidence of deep infiltrating endometriosis on MRI were evaluated, utilizing the Chi-square test, Fisher's exact test and Mann-Whitney U-test. 303 consecutive patients (mean age, 33.4 years ± 8.3) were evaluated. Incidental findings were noted in 299/303 (98.7%) patients. Most frequently, ossification of the hip acetabular rim and degenerative changes of the lumbar spine were noted. In 25/303 (8.3%) patients, incidental findings had high clinical significance. For specific incidental findings, significantly higher prevalences were found in patients with than in patients without evidence of deep infiltrating endometriosis on MRI (hip acetabular rim ossification, p = 0.041; annulus fibrosus fissures, p = 0.006; gallstones, p = 0.042). Incidental findings are very common on pelvic MRI for endometriosis. The detection of incidental findings can lead to the diagnosis of relevant diseases and thus enable early treatment. On the other hand, many incidental findings have no, only minor, or uncertain consequences.

Impactful Cardiac CT and MRI Articles from 2023.

Cardiac imaging is important in diagnosing, treating, and predicting prognosis in patients with cardiovascular disease. Imaging protocols and analysis are consistently evolving, and the implementation of artificial intelligence-based applications is of increasing interest. This review presents recent advancements in noninvasive cardiac imaging, specifically focusing on cardiac CT and MRI, from notable publications across multidisciplinary journals in 2023 of interest to both radiologists and referring clinicians in the field. The discussion encompasses the latest trials of CT fractional flow reserve and the performance of the newest generation of photon-counting detector CT, particularly in coronary stenosis quantification. Additionally, it addresses coronary plaque quantification using artificial intelligence applications and their implications from large patient cohorts, alongside prognostic outcomes, and the value of coronary artery calcification scores. Various aspects of CT trials, such as anatomic planning before revascularization, high-risk plaque features, outcomes, and pericoronary fat index, are evaluated. New insights from cardiac MRI trials for cardiomyopathies, including cardiac amyloidosis, dilated cardiomyopathy, hypertrophic cardiomyopathy, myocarditis, and valvular disease, are also outlined. The review concludes by highlighting impactful societal statements and guidelines. Keywords: CT Angiography, MR Imaging, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Coronary Arteries, Heart, Left Ventricle © RSNA, 2024.

Generation of a Human Induced Pluripotent Stem Cell Line Expressing a Magnetic Resonance Imaging Reporter Gene.

The objective of the current study is to develop a new method for tracking transplanted human induced pluripotent stem cells-derived cardiomyocytes (hiPSC-CMs) using magnetic resonance imaging (MRI). The CRISPR/dCas9 activation system is employed to overexpress ferritin heavy chain (FHC) in hiPSC-CMs. The mRNA and protein expression of FHC in hiPSC and hiPSC-CMs significantly increased after transfection. Iron chloride does not affect the cell viability in a concentration range from 0 to 2000 µm. hiPSCs overexpressing FHC (hiPSC- FHCOE) and hiPSC-CMs overexpressing FHC (hiPSC-CM-FHCOE) significantly enhanced cellular uptake of iron chloride but with no changes in electrophysiological properties compared to hiPSC-CM-Control. Furthermore, hiPSC-CM-FHCOE presented robust contrast and lower T2* values, signifying their potential as highly effective candidates for cardiac MRI. Next, hiPSC-CM-FHCOE is injected into mouse hearts and after 3 days of transplantation, MR images are obtained. hiPSC-CM-FHCOE cells exhibited clear signals in the hearts with lower T2* and rapid signal decay. Collectively, data from this proof-of-concept study demonstrated that endogenous labeling with FHC in hiPSC-CMs can be a potent strategy for enhancing the accuracy of cardiac MRI. This technology represents a significant step forward in tracking the transplanted hiPSC-CMs in the hearts of live animals.

Correlations of gray matter volume with peripheral cytokines in Parkinson's disease

IntroductionPeripheral cytokine levels may affect specific brain volumes. Few studies have examined this possible relationship. ObjectiveIn a case–control study, we used magnetic resonance imaging (MRI) voxel-based morphological analysis techniques to examine the relationship between gray matter volume changes and cognitive, motor and emotional dysfunction as well as between gray matter volume changes and peripheral blood cytokine levels. MethodA total of 134 subjects, comprising 66 PD patients and 68 healthy controls, were recruited. Peripheral venous blood was collected to measure the concentrations of 12 cytokines, including IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, IFN-α, IFN-γ, and TNF-α. All the subjects also underwent MRI, where 3D-T1-weighted MR images were used for the analysis. In addition, the Montreal Cognitive Assessment (MoCA), Mini-Mental Status Examination (MMSE), Unified Parkinson's disease Rating Scale (UPDRS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) scores were assessed in PD patients. Statistical parameter mapping 12 software was used for the statistical analysis of the images. ResultCompared with control patients, PD patients presented decreased gray matter volume (GMV) in the bilateral frontal lobe, temporal lobe, parietal lobe, occipital lobe, insula, and right cerebellar lobule VIII. Regional GMV in the temporal lobe, parietal lobe, and cerebellum was correlated with MoCA, MMSE, UPDRS, HAMA, and HAMD scores in PDs. In addition, the regional GMV in PDs was correlated with the concentrations of cytokines, including IL-4, IL-6, IFN-γ, and TNF-α. The IL-6 concentration was negatively correlated with the UPDRS-IV score. ConclusionPD patients exhibit gray matter atrophy in a wide range of brain regions, which are symmetrically distributed and mainly concentrated in the frontal and temporal lobes, and these changes may be linked to motor disorders and neuropsychiatric manifestations. Cytokine concentrations in peripheral blood are correlated with regional gray matter volume in PDs, and the IL-6 level affects gray matter volume in the left precentral gyrus and the manifestation of motor complications.

Automated multiple sclerosis progression rate computation of a patient from 2D FLAIR images with Rayleigh-Weibull-Fuzzy imaging and augmented morphing method

Multiple sclerosis (MS) is a neurological demyelinating disorder affecting brain and spinal cord by attacking the myelin sheaths of nerves. Estimation of the volumetric changes in MS lesions is a challenging and specialized task which is executed and judged by medical experts. The change in the volume of the lesions provides crucial information on MS progression or regression by comparing the magnetic resonance images (MRI) taken in successive scans. However, visual comparison of the images, even with an expert eye, would not always lead to a conclusive decision nor a consensus on progression or regression. Therefore, we present an automated expert system for estimating MS progression rate by automatic lesion segmentation and volume estimation using two-dimensional MRIs, which is also adaptable to various parameters, slice thickness and increment. A clinical dataset is specially formed for this research which contains three sets of 135 MR images of an MS patient generated within approximately 23- and 6-month periods consecutively with identical device parameters. The lesions are segmented by a novel Rayleigh-Weibull-Fuzzy (RWF) imaging method based on the Nakagami distribution and specialized fuzzy 2-means. Subsequently, the segmentation module is trained to fit the ground truths images created by experts to achieve the highest dice score possible for a total number of 56 images containing lesions, which is found as 93.76%. Afterwards, several imaginary image sequences are generated by augmented linear and nonlinear morphing for re-segmentation of imaginary lesions by RWF. Finally, we estimated the volumetric change between the first two MRI sequences to adjust the morphing module and to predict the progression rate of the lesions in time. The framework automatically selected the highest accuracy, which is 99.9% in the training session and estimated the progression rate in the testing phase with 99.69% accuracy, which are not achievable without augmented morphing methodology. For the first time in the literature, an automated framework could estimate the MS progression rate from the raw MR images, which is also the main innovation of this paper and the outputs would be beneficial for the experts working on this field.

Diffusion weighted image-guided transitional zone scoring in the detection of transitional zone prostate cancer: comparison with current PI-RADS v2.1 scoring.

To compare the performance of diffusion-weighted imaging-guided transitional zone (TZ) lesion scoring on T2-weighted imaging (DWI-guided TZ scoring) to conventional PI-RADS TZ scoring. Forty patients carried transition zone prostate cancer (TZPCa), and 40 patients had benign prostatic hyperplasia without TZPCa. A lesion-base, one-to-one correlation between the pathologic mapping sheet and the corresponding MR imaging was conducted by consensus between the genitourinary-specialized radiologist and pathologist. DWI-guided TZ scoring was defined as evaluating the DWI/apparent diffusion coefficient (ADC) images first, identifying the suspicious foci, then correlating the foci with the T2-weighted imaging, and finally assigning the PI-RADS score based on PI-RADS v2.1. Three other radiologists independently recorded the PI-RADS v2.1 scoring for TZ and the DWI-guided TZ scoring, with a time interval of 4weeks. When a PI-RADS score of ≥ 3 was considered a positive lesion, the specificity, PPV, NPV and sensitivity between the DWI-guided TZ scoring and conventional PI-RADS TZ scoring were 0.896 vs. 0.542 (p < .001), 0.764 vs. 0.439 (p < .001), 0.853 vs. 0.759 (p = .001), and 0.687 vs. 0.676 (p = .836), respectively. When PI-RADS scores ≥ 4 was considered cancer-positive, the specificity and PPV were also higher when applying DWI-guided TZ scoring (0.986 vs. 0.944, p = .007; 0.943 vs. 0.810, p = .009, respectively); however, the sensitivity and NPV were not statistically different (0.468 vs. 0.468, p = .998; 0.785 vs. 0.776, p = .537, respectively). The interobserver agreement presented as κ-value was higher in DWI-guided TZ scoring (0.584) than in conventional PI-RADS TZ scoring (0.155) (p = .003). DWI-guided TZ scoring improves the interobserver agreement, specificity, and predictive value without impairing the sensitivity.

Motion Artifact Detection for T1-Weighted Brain MR Images Using Convolutional Neural Networks.

Quality assessment (QA) of magnetic resonance imaging (MRI) encompasses several factors such as noise, contrast, homogeneity, and imaging artifacts. Quality evaluation is often not standardized and relies on the expertise, and vigilance of the personnel, posing limitations especially with large datasets. Machine learning based on convolutional neural networks (CNNs) is a promising approach to address these challenges by performing automated inspection of MR images. In this study, a CNN for the detection of random head motion artifacts (RHM) in T1-weighted MRI as one aspect of image quality is proposed. A two-step approach aimed to first identify images exhibiting pronounced motion artifacts, and second to evaluate the feasibility of a more detailed three-class classification. The utilized dataset consisted of 420 T1-weighted whole-brain image volumes with isotropic resolution. Human experts assigned each volume to one of three classes of artifact prominence. Results demonstrate an accuracy of 95% for the identification of images with pronounced artifact load. The addition of an intermediate class retained an accuracy of 76%. The findings highlight the potential of CNN-based approaches to increase the efficiency of post-hoc QAs in large datasets by flagging images with potentially relevant artifact loads for closer inspection.

141 - Water-excitation improves the image quality of diffusion-weighted MR imaging in esophageal cancer:Comparison with spectral attenuated inversion recovery diffusion-weighted imaging

141 - Water-excitation improves the image quality of diffusion-weighted MR imaging in esophageal cancer:Comparison with spectral attenuated inversion recovery diffusion-weighted imaging

Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging

BackgroundMagnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. However, MRI accessibility is challenging for patients with contraindications or in rural areas globally with limited clinical resources. PurposeThis study investigates the possibility of achieving MRI-level prostate auto-segmentation accuracy using CT-only input via a deep learning (DL) model trained with CT-MRI registered segmentation. Methods and MaterialsA cohort of 111 definitive prostate radiotherapy patients with both CT and MRI images was retrospectively grouped into training (n = 37) and validation (n = 20) (where reference contours were derived from CT-MRI registration), and testing (n = 54) sets. Two commercial DL models were benchmarked against the reference contours in the training and validation sets. A custom DL model was incrementally retrained using the training dataset, quantitatively evaluated on the validation dataset, and qualitatively assessed by two different physician groups on the validation and testing datasets. A contour quality assurance (QA) model, established from the proposed model on the validation dataset, was applied to the test group to identify potential errors, confirmed by human visual inspection. ResultsTwo commercial models exhibited large deviations in the prostate apex with CT-only input (median: 0.77/0.78 for Dice similarity coefficient (DSC), and 0.80 cm/0.83 cm for 95 % directed Hausdorff Distance (HD95), respectively). The proposed model demonstrated superior geometric similarity compared to commercial models, particularly in the apex region, with improvements of 0.05/0.17 cm and 0.06/0.25 cm in median DSC/HD95, respectively. Physician evaluation on MRI-CT registration data rated 69 %-78 % of the proposed model’s contours as clinically acceptable without modifications. Additionally, 73 % of cases flagged by the contour quality assurance (QA) model were confirmed via visual inspection. ConclusionsThe proposed incremental learning strategy based on CT-MRI registration information enhances prostate segmentation accuracy when MRI availability is limited clinically.

Assessment Of Neuroradiological Features Of Wolfram Syndrome

The WOLFRAM syndrome is a rare hereditary neurodegenerative disorder described for the first time in 1938 as the coexistence of a diabetes mellitus and optic atrophy to which join frequently a diabetes insipidus and a bilateral deafness. Here we report cranial MRI findings in a 20 year old patient who presented with diabetes mellitus, decreased visual loss since 3 years with progressive loss of hearing since 6 months. We reviewed the brain MR images to determine the frequency and characteristics of common neuroradiologic findings. Imaging findings include absence of the normal high signal of the neurohypophysis, atrophy of visual pathways, the brainstem, cerebellum and cerebral cortex and abnormal signal changes in pons

Facial Nerve Tractography Using Diffusion MRI: A Comparison of Acquisition b -Values and Single- and Multifiber Tracking Strategies.

This study investigates the impact of different diffusion magnetic imaging (dMRI) acquisition settings and mathematical fiber models on tractography performance for depicting cranial nerve (CN) VII in healthy young adults. The aim of this study is to optimize visualization of CN VII for preoperative assessment in surgeries near the nerve in the cerebellopontine angle, reducing surgery-associated complications. The study analyzes 100 CN VII in dMRI images from the Human Connectome Project, using three separate sets with different b values ( b = 1,000 s/mm 2 , b =2,000 s/mm 2 , b =3,000 s/mm 2 ) and four different tractography methods, resulting in 1,200 tractographies analyzed. The results show that multifiber and free water (FW) compartment models produce significantly more streamlines than single-fiber tractography. The addition of an FW compartment significantly increases the mean streamline fractional anisotropy (FA). Expert quality ratings showed that the highest rated tractography was the 1 tensor (1T) method without FW at b values of 1,000 s/mm2. In this young and healthy cohort, best tractography results are obtained by using a 1T model without a FW compartment in b =1,000 diffusion MR images. The FW compartment increased the contrast between streamlines and cerebrospinal fluid (higher mean streamline FA). This finding may help ongoing research to improve CN VII tractography results in tumor cases where the nerve is often stretched and thinned by the tumor.

Adaptive wavelet-VNet for single-sample test time adaptation in medical image segmentation.

In medical image segmentation, a domain gap often exists between training and testing datasets due to different scanners or imaging protocols, which leads to performance degradation in deep learning-based segmentation models. Given the high cost of manual labeling and the need for privacy protection, it is often challenging to annotate the testing (target) domain data for model fine-tuning or to collect data from different domains to train domain generalization models. Therefore, using only unlabeled target domain data for test-time adaptation (TTA) presents a more practical but challenging solution. To improve the segmentation accuracy of deep learning-based models on unseen datasets, and especially to enhance the efficiency and stability of TTA for individual samples from heterogeneous domains. In this study, we proposed to dynamically adapt a wavelet-VNet (WaVNet) to unseen target domains with a hybrid objective function, based on each unlabeled test sample during the test time. We embedded multiscale wavelet coefficients into a V-Net encoder and adaptively adjusted the spatial and spectral features according to the input, and the model parameters were optimized by three loss functions. We integrated a shape-aware loss to focus on the foreground segmentations, a Refine loss to correct the incomplete and noisy segmentations caused by domain shifts, and an entropy loss to promote the global consistency of the segmentations. We evaluated the proposed method on multidomain liver and prostate segmentation datasets to assess its advantages over other TTA methods. For the source domain model training of the liver dataset, we used 15 3D MR image samples for training and 5 for validation. Correspondingly, for the prostate dataset, we used 22 3D MR image samples for training and 7 for validation. In the target domain, we used a single 3D MR image sample for adaptation and testing. The total number of testing samples is 60 in the liver dataset (for 3 different domains) and 116 in the prostate dataset (for 6 different domains). The proposed method showed the highest segmentation accuracy among all methods, achieving a mean(±SD) Dice coefficient (DSC) of 78.10±5.23% and a mean 95th Hausdorff distance (HD95) of 15.52±5.84mm on the liver dataset; and a mean DSC of 80.02±3.89% and a mean HD95 of 9.18±3.47mm on the prostate dataset. The DSC is 11.67% (in absolute terms) and 15.27% higher than that of the baseline (no adaptation) method, for the liver and the prostate datasets, respectively. The proposed adaptive WaVNet enhanced the image segmentation accuracy from unseen domains during the test time via unsupervised learning and multi-objective optimization. It can benefit clinical applications where data are scarce or with changing data distributions, including online adaptive radiotherapy. The code will be released at: https://github.com/sanny1226/WaVNet.

187 - MR Imaging research in large populations versus the clinical setting: a radiographer experience from the Generation R study

187 - MR Imaging research in large populations versus the clinical setting: a radiographer experience from the Generation R study

CellKine clinical trial: first report from a phase 1 trial of allogeneic bone marrow–derived mesenchymal stem cells in subjects with painful lumbar facet joint arthropathy

Abstract Background: Lumbar facet joint arthropathy (LFJA) is a major cause of low back pain (LBP), with current treatments offering limited long-term benefits. Bone marrow–derived mesenchymal stem cells (BM-MSCs) show promise due to their immunomodulatory and trophic effects, potentially addressing underlying degenerative processes in LFJA. Objectives: This initial report describes the outcomes of the first treated patient in an ongoing mutidisciplinary phase 1 clinical trial evaluating the safety and feasibility of intra-articular allogeneic BM-MSCs for painful LFJA. Methods: Following enrollment in our IRB-approved protocol, symptomatic LFJA was confirmed through double blocks on L4 and L5 medial branches. Two 1-mL syringes, each containing 10 million BM-MSCs, were prepared in the cGMP facility and administered bilaterally to the patient’s L4-L5 lumbar facet joints. The patient underwent standardized follow-ups, including clinical examinations and functional and imaging assessments for 2 years, utilizing patient-reported outcomes measurement information system—computer adaptive tests (PROMIS CATs), visual analogue scale, Oswestry disability index, work functional status and opioid pain medication use, and MR imaging Fenton–Czervionke score. Results: The patient tolerated the procedure well, with no drug-related adverse events during the study period. Pain, spine function, and work functional status improved at multiple follow-ups. This patient also reported improvements in mental and social health, along with a notable improvement in the grade of facet synovitis observed at the one-year follow-up MRI evaluation. Conclusions: This case report suggests the safety and feasibility of administering intra-articular allogeneic BM-MSCs, offering therapeutic benefits for pain management and functional activities.

Morphological analysis of the trigeminal nerve in trigeminal neuralgia using the nerve's centerline and multiple cross-sections of a 3D model.

Morphological changes such as angulation and torsion of the trigeminal nerve have been reported to cause trigeminal neuralgia (TN). The authors sought to quantify and objectively evaluate the morphological changes of the trigeminal nerve and to elucidate the cause of TN. The authors retrospectively analyzed the cases of patients with primary TN who had undergone microvascular decompression at a single facility between January 2016 and December 2022 and had both single-artery compression and a good postoperative outcome. The authors performed segmentation of the trigeminal nerve by using the patients' pre- and postoperative high-resolution MR images, and they then created a 3D model. The centerline of the trigeminal nerve was obtained using volume skeletonization, and the authors created multiple cross-sectional images by reslicing the 3D model perpendicular to the centerline. The parameters analyzed were as follows: the 1) centerline length; 2) centerline curvature; 3) centerline torsion; 4) cross-sectional area; 5) cross-sectional flattening ratio; and 6) cross-sectional long-axis angle. Comparisons were made for each parameter between the affected and unaffected side and between preoperative and postoperative trigeminal nerve findings. After exclusions, 70 of the 127 patients who underwent microvascular decompression during the study period were included in the analysis. In the preoperative images, the trigeminal nerve on the affected side had a significantly longer centerline length (p = 0.0003), greater curvature (p = 0.0012), smaller cross-sectional area (p < 0.0001), and greater flattening ratio (p = 0.0059) than the unaffected side. On the affected side, the preoperative trigeminal nerve had a significantly longer centerline length (p < 0.0001), greater curvature (p = 0.0028), and smaller cross-sectional area (p < 0.0001) compared to the postoperative trigeminal nerve. It is possible to analyze the morphological changes of the trigeminal nerve by using this method. In the preoperative trigeminal nerve on the affected side, the centerline is long and curved, and the cross-sectional area is small and flat. Further analyses may help clarify the pathophysiology, aid in diagnoses, and predict the efficacy of treatment.

External Validation of SpineNetV2 on a Comprehensive Set of Radiological Features for Grading Lumbosacral Disc Pathologies

BackgroundIn recent years, the integration of Artificial Intelligence (AI) models has revolutionized the diagnosis of Low Back Pain (LBP) and associated disc pathologies. Among these, SpineNetV2 stands out as a state-of-the-art, open-access model for detecting and grading various intervertebral disc pathologies. However, ensuring the reliability and applicability of AI models like SpineNetV2 is paramount. Rigorous validation is essential to guarantee their robustness and generalizability across diverse patient cohorts and imaging protocols. MethodsWe conducted a retrospective analysis of MRI images of 1747 lumbosacral intervertebral discs (IVDs) from 353 patients (mean age, 54 ± 15.4 years, 44.5% female) with various spinal disorders, collected between September 2021 and February 2023 at X-Ray Service s.r.l. The SpineNetV2 system was used to grade 11 distinct lumbosacral disc pathologies, including Pfirrmann grading, disc narrowing, central canal stenosis, spondylolisthesis, (upper & lower) endplate defects, (upper & lower) marrow changes, (right & left) foraminal stenosis, and disc herniation, using T2-weighted sagittal MR images. Performance metrics included accuracy, balanced accuracy, precision, F1 score, Matthew's Correlation Coefficient, Brier Score Loss, Lin's concordance correlation coefficients, and Cohen's kappa coefficients. Two expert radiologists provide annotations for these discs. The evaluation of SpineNetV2′s grading is compared against expert radiologists' assessments. ResultsSpineNetV2 demonstrated strong performance across various metrics, with high agreement scores (Cohen's Kappa, Lin's Concordance, and Matthew's Correlation Coefficient exceeding 0.7) for most pathologies. However, lower agreement was found for foraminal stenosis and disc herniation, underscoring the limitations of sagittal MR images for evaluating these conditions. ConclusionsThis study highlights the importance of external validation, emphasizing the need for comprehensive assessments of deep learning models. SpineNetV2 exhibits promising results in predicting disc pathologies, with findings guiding further improvements. The open-source release of SpineNetV2 enables researchers to independently validate and extend the model's capabilities. This collaborative approach promotes innovation and accelerates the development of more reliable and comprehensive deep learning tools for the assessment of spine pathology.

Clinical Validation Study of Deep Learning-Generated Magnetic Resonance Images

This research utilizes a deep learning-based image generation algorithm to generate pseudo-sagittal STIR sequences from sagittal T1WI and T2WI MR images. The evaluations include both subjective assessments by two physicians and objective analyses, measuring image quality through SNR and CNR in ROIs of five different tissues. Further analyses, including MAE, PSNR, SSIM, and COR, establish a strong correlation between the generated STIR sequences and the gold standard, with Bland-Altman analysis indicating pixel consistency. The findings indicate that the deep learning-generated STIR sequences not only align with but potentially surpass the gold standard in terms of image quality and clinical diagnostic capabilities. Moreover, the approach demonstrates promise for clinical implementation, offering reduced scan time and enhanced imaging efficiency.

Symptomatic Cerebellar Thrombosed Developmental Venous Anomaly: A Case Report

Abstract Background With the rising prevalence of advanced neuroradiological imaging, detection of incidental developmental venous anomalies (DVAs) is more commonplace. DVAs are rarely symptomatic (&amp;lt;2%). Typically, symptoms arise due to flow-related complications or mechanical effects. Of particular concern is venous thrombosis, a frequent flow-related complication, which can cause venous ischemia or haemorrhage. Thus, early recognition and intervention is important. Methods We describe a case of a 54-year-old male, with background of hypertension, presenting three days after sudden onset headache, ataxia, and diplopia. Initial clinical examination revealed bidirectional horizontal gaze-evoked nystagmus, skew deviation, and decreased temperature sensation of the left face. Ophthalmological examination identified left inferior rectus limitation. Results Initial non-contrast CT brain identified hypoattenuation in the right pons, suggestive of sub-acute infarction. Secondary prevention was initiated. Subsequent MRI performed 5 days later revealed hyperattenuation on T2 and FLAIR in the pons, right middle cerebellar peduncle, and right cerebellar hemisphere without restricted diffusion. SWI sequences identified an abnormal venous structure in the right cerebellum, with increased signal within it, suggestive of thrombus. The patient was diagnosed with a thrombosed DVA with associated venous oedema. Symptoms had resolved on dual antiplatelet therapy, without anticoagulation. Conclusion This case highlights the potential for DVAs, generally considered a benign entity, to become symptomatic with associated parenchymal damage and a rare, but important, stroke mimic. Moreover, it underscores the pivotal role of MRI imaging; while a CT scan might have led to the assumption of a subacute infarct, MR imaging led to a complete re-evaluation of the diagnosis in this case.