T2-weighted Research Articles

Birth-related subdural hemorrhage in asymptomatic neonates: evolution over time and differentiation from traumatic subdural hemorrhage.

Intracranial birth-related subdural hemorrhage frequently occurs in asymptomatic newborns and has no adverse long-term sequelae. It is medically and medicolegally important to differentiate birth-related subdural hemorrhage from other pathological causes of intracranial hemorrhage. There is limited literature available on the incidence of birth-related subdural hemorrhage, its imaging features, and evolution over time, mainly because asymptomatic infants do not routinely undergo cranial MRI. To establish the incidence and distribution of birth-related subdural hemorrhage and evaluate their association with various modes of delivery, identify associated features, and evaluate the sequential evolution of signal changes of the birth-related hemorrhages on MRI. A total of 200 healthy term neonates and young infants were included in this retrospective review study. All infants underwent MRI of the brain and cervical spine at postnatal age of 0-2 months with acquisition of a 3D T1-weighted (T1W), 3D or 2D T2-weighted (T2W), and axial diffusion-weighted imaging (DWI) sequences. The scans were evaluated for the presence and distribution of subdural hemorrhages, other intracranial hemorrhages, and associated injuries. Prevalence of intracranial hemorrhage in various modes of delivery was analyzed. Relationship between the signal intensities of the bleeds on T1W, T2W, and DWI scans and the age of the infants was analyzed. Appropriate tests were applied to test for statistical significance of the data. Out of 200 neonates, 66 (33%) had detectable intracranial hemorrhage on MRI with an age range of 11-25 days, including 31 (47%) males and 35 (53%) females. All of them had subdural hemorrhages, 54 (81.8%) of which were in the posterior fossa. Additional parenchymal hemorrhages were present in a few, but no subarachnoid hemorrhages, cervical spinal canal hemorrhages, cortical bridging vein injury, or cervical spinal ligamentous injury were identified within the limitations of the study. No detectable intracranial hemorrhage was found in subjects above 25 days of age. Overall incidence of subdural hemorrhage by mode of delivery was 8/68 (11.8%) in babies born by cesarean section and 58/132 (43.9%) in babies born by vaginal delivery. Among the vaginal deliveries, the highest incidence was observed in assisted vaginal delivery (19/30, 63.3%). Subjects with birth-related subdural hemorrhage were categorized into three age groups: <13 days, 13-21 days, and >21 days. All detected hemorrhages were T1W hyperintense. In the <13 days group, all bleeds were T2W hypointense. In the 13-21 days group, 73.1% were T2W hypointense, while 26.9% were T2W mixed. All bleeds in the >21 days group were T2W hypointense. All DWI hyperintense bleeds were found in the 13-21 days group. Birth-related subdural hemorrhage occurs in over a third of normal deliveries and has a characteristic distribution, predominantly in the posterior fossa. Associated cervical spinal subdural hemorrhages, cervical spinal ligamentous injury, or cortical bridging vein injury, which are concerning for traumatic etiology, were not identified. Birth-related subdural hemorrhages follow a characteristic pattern of signal changes on MRI. Although not completely reliable, this can help in differentiating them from traumatic intracranial hemorrhages which usually occur postnatally. No birth-related subdural hemorrhages were seen after 25 days of age in our cohort.

Diagnostic performance of magnetic resonance imaging features to differentiate adrenal pheochromocytoma from adrenal tumors with positive biochemical testing results

BackgroundIt is extremely essential to accurately differentiate pheochromocytoma from Adrenal incidentalomas (AIs) before operation, especially biochemical tests were inconclusive. We aimed to evaluate the value of magnetic resonance imaging (MRI) features to differentiate pheochromocytomas among adrenal tumors, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive.MethodsWith institutional review board approval, this study retrospectively compared 35 pheochromocytoma (PHEO) patients with 27 non-pheochromocytoma(non-PHEO) patients between January 2022 to September 2023, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive. T test was used for the independent continuous data and the chi-square test was used for categorical variables. Univariate and multivariate logistic regression were applied to find the independent variate of the features to differentiate PHEO from non-PHEO and ROC analysis was applied to evaluate the diagnostic value of the independent variate.ResultsWe found that the T2-weighted (T2W) signal intensity in patients with pheochromocytoma was higher than other adrenal tumors, with greatly significant (p < 0.001). T2W signal intensity ratio (T2W nodule-to-muscle SI ratio) was an independent risk factor for the differential diagnosis of adrenal PHEOs from non-PHEOs. This feature alone had 91.4% sensitivity and 81.5% specificity to rule out pheochromocytoma based on optimal threshold, with an area under the receiver operating characteristics curve (AUC‑ROC) of 0.910(95% C I: 0.833–0.987).ConclusionOur study confirms that T2W signal intensity ratio can differentiate PHEO from non-PHEO, among which the consequences of biochemical screening tests of catecholamines and/or catecholamine metabolites are positive.

Fully automated segmentation and volumetric measurement of ocular adnexal lymphoma by deep learning-based self-configuring nnU-net on multi-sequence MRI: a multi-center study

PurposeTo evaluate nnU-net’s performance in automatically segmenting and volumetrically measuring ocular adnexal lymphoma (OAL) on multi-sequence MRI.MethodsWe collected T1-weighted (T1), T2-weighted and T1-weighted contrast-enhanced images with/without fat saturation (T2_FS/T2_nFS, T1c_FS/T1c_nFS) of OAL from four institutions. Two radiologists manually annotated lesions as the ground truth using ITK-SNAP. A deep learning framework, nnU-net, was developed and trained using two models. Model 1 was trained on T1, T2, and T1c, while Model 2 was trained exclusively on T1 and T2. A 5-fold cross-validation was utilized in the training process. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), sensitivity, and positive prediction value (PPV). Volumetric assessment was performed using Bland-Altman plots and Lin’s concordance correlation coefficient (CCC).ResultsA total of 147 patients from one center were selected as training set and 33 patients from three centers were regarded as test set. For both Model 1 and 2, nnU-net demonstrated outstanding segmentation performance on T2_FS with DSC of 0.80–0.82, PPV of 84.5–86.1%, and sensitivity of 77.6–81.2%, respectively. Model 2 failed to detect 19 cases of T1c, whereas the DSC, PPV, and sensitivity for T1_nFS were 0.59, 91.2%, and 51.4%, respectively. Bland–Altman plots revealed minor tumor volume differences with 0.22–1.24 cm3 between nnU-net prediction and ground truth on T2_FS. The CCC were 0.96 and 0.93 in Model 1 and 2 for T2_FS images, respectively.ConclusionThe nnU-net offered excellent performance in automated segmentation and volumetric assessment in MRI of OAL, particularly on T2_FS images.

Integrating lipid metabolite analysis with MRI-based transformer and radiomics for early and late stage prediction of oral squamous cell carcinoma

BackgroundOral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict the stage of tumors.MethodsThis study involved 198 patients from two medical centers. A detailed analysis of contrast-enhanced T1-weighted (ceT1W) and T2-weighted (T2W) MRI were conducted, integrating these with biochemical indicators for a comprehensive evaluation. Initially, 42 clinical biochemical indicators were selected for consideration. Through univariate analysis and multivariate analysis, only those indicators with p-values less than 0.05 were retained for model development. To extract imaging features, machine learning algorithms in conjunction with Vision Transformer (ViT) techniques were utilized. These features were integrated with biochemical indicators for predictive modeling. The performance of model was evaluated using the Receiver Operating Characteristic (ROC) curve.ResultsAfter rigorously screening biochemical indicators, four key markers were selected for the model: cholesterol, triglyceride, very low-density lipoprotein cholesterol and chloride. The model, developed using radiomics and deep learning for feature extraction from ceT1W and T2W images, showed a lower Area Under the Curve (AUC) of 0.85 in the validation cohort when using these imaging modalities alone. However, integrating these biochemical indicators improved the model’s performance, increasing the validation cohort AUC to 0.87.ConclusionIn this study, the performance of the model significantly improved following multimodal fusion, outperforming the single-modality approach.Clinical relevance statementThis integration of radiomics, ViT models, and lipid metabolite analysis, presents a promising non-invasive technique for predicting the staging of OSCC.

Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study.

Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation. A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models. The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels. Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.

Deep Learning Algorithms for Bladder Cancer Segmentation on Multi-Parametric MRI.

Bladder cancer (BC) segmentation on MRI images is the first step to determining the presence of muscular invasion. This study aimed to assess the tumor segmentation performance of three deep learning (DL) models on multi-parametric MRI (mp-MRI) images. We studied 53 patients with bladder cancer. Bladder tumors were segmented on each slice of T2-weighted (T2WI), diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC), and T1-weighted contrast-enhanced (T1WI) images acquired at a 3Tesla MRI scanner. We trained Unet, MAnet, and PSPnet using three loss functions: cross-entropy (CE), dice similarity coefficient loss (DSC), and focal loss (FL). We evaluated the model performances using DSC, Hausdorff distance (HD), and expected calibration error (ECE). The MAnet algorithm with the CE+DSC loss function gave the highest DSC values on the ADC, T2WI, and T1WI images. PSPnet with CE+DSC obtained the smallest HDs on the ADC, T2WI, and T1WI images. The segmentation accuracy overall was better on the ADC and T1WI than on the T2WI. The ECEs were the smallest for PSPnet with FL on the ADC images, while they were the smallest for MAnet with CE+DSC on the T2WI and T1WI. Compared to Unet, MAnet and PSPnet with a hybrid CE+DSC loss function displayed better performances in BC segmentation depending on the choice of the evaluation metric.

Integrated Approach in Evaluating the Results of Cosmetic Procedures Using Ultrasonography and Magnetic Resonance Tomography (Clinical Study)

Aim. To investigate the capabilities of comprehensive assessment using ultrasound and magnetic resonance imaging (MRI) of the results of cosmetic procedures.Materials and Methods. A study of the soft tissues of the anterior abdominal wall of a 43-year-old woman complaining of skin atony and postpartum stretch marks using highresolution ultrasound and MRI.Results. At ultrasonography in the projection of injection of polylactic acid and calcium hydroxyapatite preparations and radiofrequency treatment, an increase in echogenicity of hypodermis with the presence of acoustic shadow was noted. On MRI at the level of derma and hypoderma of periumbilical region in the projection of radiofrequency treatment, injections of preparations based on polylactic acid on the left, calcium hydroxyapatite on the right in 3 months after subdermal injection, areas of hyperintense MR-signal in T2 weight image (WI) and STIR programs, hypointense in T1 WI, without diffusion limitation on DWI b=1000 were determined. In SWI mode, the focus of hypointense MR signal of rounded shape with clear and smooth contours was visualized on the right side of the image.Conclusion. The clinical study demonstrates the possibilities of an integrated approach in evaluating the results of cosmetic procedures using ultrasonography and MRI.

Is it possible to detect cribriform adverse pathology in prostate cancer with magnetic resonance imaging machine learning-based radiomics?

Rationale and objectives: Cribriform patterns are accepted as aggressive variants of prostate cancer. These adverse pathologies are closely associated with early biochemical recurrence, metastasis, castration resistance, and poor disease-related survival. A few publications exist to diagnose these two adverse pathologies with multiparametric magnetic resonance imaging (mpMRI). Most of these publications are retrospective and are not studies that have made a difference in diagnosing adverse pathology. It is also known that fusion biopsies taken from lesions detected in mpMRI are insufficient to detect these adverse pathologies. Our study aims to diagnose this adverse pathology using machine learning-based radiomics data from MR images. Materials and methods: A total of 88 patients who had pathology results indicating the presence of cribriform pattern and prostate adenocarcinoma underwent preoperative MRI examinations and radical prostatectomy. Manual slice-by-slice 3D volumetric segmentation was performed on all axial images. Data processing and machine learning analysis were conducted using Python 3.9.12 (Jupyter Notebook, Pycaret Library). Results: Two radiologists, SE and MAG, with 7 and 8 years of post-graduate experience, respectively, evaluated the images using the 3D-Slicer software without knowledge of the histopathological findings. One hundred seventeen radiomic tissue features were extracted from T1 weighted (T1W) and apparent diffusion coefficient (ADC) sequences for each patient. The interobserver agreement for these features was analyzed using the intraclass correlation coefficient (ICC). Features with excellent interobserver agreement (ICC &gt; 0.90) were further analyzed for collinearity between predictors using Pearson’s correlation. Variables showing a very high correlation (r ≥ ±0.80) were disregarded. The selected features for T1W and ADC images were First-order maximum, First-order skewness, First-order 10th percentile for ADC, and Gray level size zone matrix, Large area low gray level emphasis for T1W.As a result of the classification of PyCaret, the three best models were found. A single model was obtained by blending these three models. AUC, accuracy, recall, precision, and F1 scores were 0.79, 0.77, 0.85, 0.82, and 0.83, respectively. Conclusion: ML-based MRI radiomics of prostate cancer can predict the cribriform pattern. This prognostic factor cannot be determined through qualitative radiological evaluation and may be overlooked in preoperative histopathological specimens.

MRI-based Tumor Habitat Analysis for Treatment Evaluation of Radiotherapy on Esophageal Cancer

Introduction: We aim to evaluate the performance of pre-treatment MRI-based habitat imaging to segment tumor micro-environment and its potential to identify patients with esophageal cancer who can achieve pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT). Material and methods: A total of 18 patients with locally advanced esophageal cancer (LAEC) were recruited into this retrospective study. All patients underwent MRI before nCRT and surgery using a 3.0 T scanner (Ingenia 3.0 CX, Philips Healthcare). A series of MR sequences including T2-weighted (T2), diffusion-weighted imaging (DWI), and Contrast Enhance-T1 weighted (CE-T1) were performed. A clustering algorithm using a two-stage hierarchical approach groups MRI voxels into separate clusters based on their similarity. The t-test and receiver operating characteristic (ROC) analysis were used to evaluate the predictive effect of pCR on habitat imaging results. Cross-validation of 18 folds is used to test the accuracy of predictions. Results: A total of 9 habitats were identified based on structural and physiologic features. The predictive performance of habitat imaging based on these habitat volume fractions (VFs) was evaluated. Students’ t-tests identified 2 habitats as good classifiers for pCR and non-pCR patients. ROC analysis shows that the best classifier had the highest AUC (0.82) with an average prediction accuracy of 77.78%. Conclusion: We demonstrate that MRI-based tumor habitat imaging has great potential for predicting treatment response in LAEC. Spatialized habitat imaging results can also be used to identify tumor non-responsive sub-regions for the design of focused boost treatment to potentially improve nCRT efficacy.

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

BackgroundArtificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice.ObjectiveTo assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging.Materials and methodsThis prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios.ResultsAI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively.ConclusionWe conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.

Unique presentation of acute neuro-Behçet's disease involving a cytotoxic edema core surrounded by vasogenic edema.

A 53-year-old woman with recurrent stomatitis, genital ulcers, and folliculitis was admitted to our hospital after experiencing visual disturbances for the past two weeks, and a non-throbbing headache for the past three days. She had also developed numbness in her left extremities. An ophthalmological examination revealed inflammatory changes in the eye. Cerebrospinal fluid analysis showed increased cell counts, protein, and interleukin-6 levels. Brain magnetic resonance imaging revealed multiple high signal intensities on T2-weighted (T2W)/fluid-attenuated inversion recovery (FLAIR) images of the pons and occipital and parietal lobes. The T2W/FLAIR high-signal-intensity lesion in the pons was hyperintense on diffusion-weighted imaging (DWI) and hypointense on apparent diffusion coefficient mapping (ADC), suggesting cytotoxic edema. Another high-signal-intensity lesion on T2W/FLAIR was isointense to hyperintense on DWI and hyperintense on ADC, indicating vasogenic edema. The vasogenic edema in the left occipital lobe contained a small core that was hyperintense on DWI and hypointense on ADC, suggesting cytotoxic edema. The patient was diagnosed with acute neuro-Behçet's disease (neuro-BD) and responded well to high-dose glucocorticoid and colchicine treatment. The present report emphasizes that patients with acute neuro-BD may present with cytotoxic edema in the pons and cerebral spheres. Further reports of similar cases would contribute to a better understanding of the role of cytotoxic edema in the pathophysiology of neuro-BD and help elucidate the mechanisms underlying a unique presentation characterized by a central cytotoxic edema core within vasogenic edema. (233 words).

Assessing the efficacy of 3D Dual-CycleGAN model for multi-contrast MRI synthesis

BackgroundThis research presents a novel methodology for synthesizing 3D multi-contrast MRI images utilizing the 3D Dual-CycleGAN architecture. The performance of the model is evaluated on different MRI sequences, including T1-weighted (T1W), T1-weighted contrast-enhanced (T1c), T2-weighted (T2W), and FLAIR sequences.ResultsOur approach demonstrates proficient learning capabilities in transforming T1W images into target modalities. The proposed framework encompasses a combination of different loss functions including voxel-wise, gradient difference, perceptual, and structural similarity losses. These loss components, along with adversarial and dual cycle-consistency losses, contribute significantly to realistic and accurate syntheses. Evaluation metrics including MAE, PMAE, RMSE, PCC, PSNR, and SSIM are employed to assess the fidelity of synthesized images compared to their ground truth counterparts. Empirical results indicate the effectiveness of the 3D Dual-CycleGAN model in generating T1c images from T1W inputs with minimal average discrepancies (MAE of 2.8 ± 2.61) and strong similarity (SSIM of 0.82 ± 0.28). Furthermore, the synthesis of T2W and FLAIR images yields promising outcomes, demonstrating acceptable average discrepancies (MAE of 3.87 ± 3.32 for T2W and 3.82 ± 3.32 for FLAIR) and reasonable similarities (SSIM of 0.82 ± 0.28 for T2W and 0.80 ± 0.29 for FLAIR) relative to the original images.ConclusionsThese findings underscore the efficacy of the 3D Dual-CycleGAN model in generating high-fidelity images, with significant implications for diverse applications in the field of medical imaging.

Deep learning-based magnetic resonance imaging analysis for chronic cerebral hypoperfusion risk.

Chronic cerebral hypoperfusion (CCH) is a frequently encountered clinical condition that poses a diagnostic challenge due to its nonspecific symptoms. To enhance the diagnosis of CCH and non-CCH through Magnetic Resonance Imaging (MRI), offering support in clinical decision-making and recommendations to ultimately elevate diagnostic accuracy and optimize patient treatment outcomes. In the retrospective research, we collected 204 routine brain magnetic resonance imaging (MRI) from March 1 to September 10 2022, as training and testing cohorts. And a validation cohort with 108 samples was collected from November 14 2022 to August 4 2023. MRI sequences were processed to obtain T1-weighted (T1WI) and T2-weighted (T2WI) sequence images for each patient. We propose CCH-Network (CCHNet), an end-to-end deep learning model, integrating convolution and Transformer modules to capture local and global structural information. Our novel adversarial training method improves feature knowledge capture, enhancing both generalization ability and efficiency in predicting CCH risk. We assessed the classification performance of the proposed model CCHNet by comparing it with existing state-of-the-art deep learning algorithms, including ResNet34, DenseNet121, VGG16, Convnext, ViT, Coat, and TransFG. To better validate model performance, we compared the results of the proposed model with eight neurologists to evaluate their consistency. CCHNet achieved an AUC of 91.6% (95% CI: 86.8-99.1), with an accuracy (ACC) of 85.0% (95% CI: 75.6-95.2). It demonstrated a sensitivity (SE) of 80.0% (95% CI: 71.6-95.6) and a specificity (SP) of 90.0% (95% CI: 82.3-97.8) in the testing cohort. In the validation cohort, the model demonstrated an AUC of 86.0% (95% CI: 80.3-93.0), an ACC of 84.2% (95% CI: 70.2-93.6), a SE of 83.3% (95% CI: 68.3-95.5), and a SP of 84.7% (95% CI: 70.3-96.8). The model improved the diagnostic performance of MRI with high SE and SP, providing a promising method for the diagnosis of CCH.

Application of Short T1 Inversion Recovery Sequence in Increased Signal Intensity Following Cervical Spondylotic Myelopathy

To compare magnetic resonance (MR) short T1 inversion recovery (STIR) sequence with MR T2-weighted (T2W) sequence for detecting increased signal intensity (ISI) and assessing outcomes of ISI in cervical spondylotic myelopathy (CSM). Data of patients with CSM who showed ISI on MR imaging and had undergone cervical spine surgery were retrospectively reviewed. STIR and T2W images were examined to assess signal intensity ratio (SIR), length and grading of the ISI, maximal spinal cord compression (MSCC), canal narrowing ratio (CNR), and ligamentum flavum hypertrophy (LFH). The patients were divided into good and poor groups based on their outcomes. Chi-square tests and variance analysis were used to assess intergroup differences. Univariate and multivariate logistic regression analyses were performed to identify risk factors for poor outcomes, and receiver operating characteristic curves were plotted to detect prognostic effects. SIR and ISI lengths were significantly different between the STIR and T2 images. In the univariate logistic regression analysis, age, diabetes, SIRT2, SIRSTIR, and ISISTIR grading were significant factors. Accordingly, in the multivariate logistic regression analysis, age, diabetes, SIRT2, and SIRSTIR were included in the model. Among patients with diabetes, we observed a significant difference between SIRT2 and SIRSTIR. The STIR sequence demonstrated superior capability to the T2W sequence in detecting ISI; however, there was no obvious difference in predicted outcomes. STIR sequence has a better prognostic value than T2W sequence in patients with diabetes who have CSM. ISI grading based on the STIR sequence may be a clinically valuable indicator.

Radiogenomics Map-Based Molecular and Imaging Phenotypical Characterization in Localised Prostate Cancer Using Pre-Biopsy Biparametric MR Imaging.

To create a radiogenomics map and evaluate the correlation between molecular and imaging phenotypes in localized prostate cancer (PCa), using radical prostatectomy histopathology as a reference standard. Radiomic features were extracted from T2-weighted (T2WI) and Apparent Diffusion Coefficient (ADC) images of clinically localized PCa patients (n = 15) across different Gleason score-based risk categories. DNA extraction was performed on formalin-fixed, paraffin-embedded (FFPE) samples. Gene expression analysis of androgen receptor expression, apoptosis, and hypoxia was conducted using the Chromosome Analysis Suite (ChAS) application and OSCHIP files. The relationship between gene expression alterations and textural features was assessed using Pearson's correlation analysis. Receiver operating characteristic (ROC) analysis was utilized to evaluate the predictive accuracy of the model. A significant correlation was observed between radiomic texture features and copy number variation (CNV) of genes associated with apoptosis, hypoxia, and androgen receptor (p-value ≤ 0.05). The identified radiomic features, including Sum Entropy ADC, Inverse Difference ADC, Sum Variance T2WI, Entropy T2WI, Difference Variance T2WI, and Angular Secondary Moment T2WI, exhibited potential for predicting cancer grade and biological processes such as apoptosis and hypoxia. Incorporating radiomics and genomics into a prediction model significantly improved the prediction of prostate cancer grade (clinically significant prostate cancer), yielding an AUC of 0.95. Radiomic texture features significantly correlate with genotypes for apoptosis, hypoxia, and androgen receptor expression in localised prostate cancer. Integration of these into the prediction model improved prediction accuracy of clinically significant prostate cancer.

MyoFold: Joint myocardial tissue composition and wall motion quantification via a highly folded sequence.

This study aims to develop and evaluate a novel cardiovascular MR sequence, MyoFold, designed for the simultaneous quantifications of myocardial tissue composition and wall motion. MyoFold is designed as a 2D single breathing-holding sequence, integrating joint T1/T2 mapping and cine imaging. The sequence uses a 2-fold accelerated balanced SSFP (bSSFP) for data readout and incorporates electrocardiogram synchronization to align with the cardiac cycle. MyoFold initially acquires six single-shot inversion-recovery images, completed during the diastole of six successive heartbeats. T2 preparation (T2-prep) is applied to introduce T2 weightings for the last three images. Subsequently, over the following six heartbeats, segmented bSSFP is performed for the movie of the entire cardiac cycle, synchronized with an electrocardiogram. A neural network trained using numerical simulations of MyoFold is used for T1 and T2 calculations. MyoFold was validated through phantom and in vivo experiments, with comparisons made against MOLLI, SASHA, T2-prep bSSFP, and the conventional cine. In phantom studies, MyoFold exhibited a 10% overestimation in T1 measurements, whereas T2 measurements demonstrated high accuracy. In vivo experiments revealed that MyoFold T1 had comparable accuracy to SASHA and precision similar to MOLLI. MyoFold demonstrated good agreement with T2-prep bSSFP in myocardial T2 measurements. No significant differences were observed in the quantification of left-ventricle wall thickness and function between MyoFold and the conventional cine. MyoFold presents as a rapid, simple, and multitasking approach for quantitative cardiovascular MR examinations, offering simultaneous assessment of tissue composition and wall motion. The sequence's multitasking capabilities make it a promising tool for comprehensive cardiac evaluations in clinical settings.

The Predictive Effect of Quantitative Analysis of Signal Intensity Heterogeneity on T2-Weighted MR Images for High-intensity Focused Ultrasound Treatment of Uterine Fibroids

Rationale and ObjectiveTo investigate whether the quantitative index of signal intensity (SI) heterogeneity on T2-weighted (T2W) magnetic resonance images can predict the difficulty and efficacy of high-intensity focused ultrasound (HIFU) ablation for uterine fibroids. Materials and MethodsThe standard deviation (SD) of T2W image (T2WI) SI was used to quantify SI heterogeneity. The correlation between SD and the non-perfused volume ratio (NPVR) in 575 patients undergoing HIFU treatment was retrospectively analyzed, and the efficacy of SD in predicting NPVR was discussed. Three classifications were made based on the SD, and the ablation difficulty and ablation effect of different grades were compared. A total of 65 cases from another center were used as an external validation set to verify the classification performance of SD. ResultsThe SD of SI was negatively correlated with NPVR (r=-0.460, p<0.001). The predictive efficiency of SD for the ablation effect was higher than that of the scaled signal intensity (0.767 vs. 0.701, p=0.006). Univariate and multivariate logistic regression analyses showed that SD was an independent predictor of ablation effect. Based on SD, the three classifications were divided into SD I: SD<101.0, SD II: 101.0≤SD<138.7, and SD III: SD≥138.7. The treatment time, sonication time, treatment intensity, and total energy of SD I were lower than those of SD II and III (p<0.05). ConclusionThe heterogeneity of T2WI SI of uterine fibroids is negatively correlated with NPVR. The SD of SI can be used to predict the ablation difficulty and ablation effect of HIFU.

Predictability of the radiological response to Yttrium-90 transarterial radioembolization by dynamic magnetic resonance imaging-based radiomics analysis in patients with intrahepatic cholangiocarcinoma

The study aims to investigate the predictability of the radiological response in intrahepatic cholangiocarcinoma (iCC) patients undergoing Yttrium-90 transarterial radioembolization (TARE) with a combined model built on dynamic magnetic resonance imaging (MRI)-based radiomics and clinical features. Thirty-six naive iCC patients who underwent TARE were included in this study. The tumor segmentation was performed on the axial T2-weighted (T2W) without fat suppression, axial T2W with fat suppression, and axial T1-weighted (T1W) contrast-enhanced (CE) sequence in equilibrium phase (Eq). At the sixth month MRI follow-up, all patients were divided into responders and non-responders according to the modified Response Evaluation Criteria in Solid Tumors. Subsequently, a radiomics score (rad-score) and a combined model of the rad-score and clinical features for each sequence were generated and compared between the groups. Thirteen (36.1%) patients were considered responders, and the remaining 23 (63.9%) were non-responders. Responders exhibited significantly lower rad-scores than non-responders (P < 0.050 for all sequences). The radiomics models showed good discriminatory ability with an area under the curve (AUC) of 0.696 [95% confidence interval (CI), 0.522–0.870] for the axial T1W-CE-Eq, AUC of 0.839 (95% CI, 0.709–0.970) for the axial T2W with fat suppression, and AUC of 0.836 (95% CI, 0.678–0.995) for the axial T2W without fat suppression. Radiomics models created by pre-treatment MRIs can predict the radiological response to Yttrium- 90 TARE in iCC patients with high accuracy. Combining radiomics with clinical features could increase the power of the test. Large-scale studies of multi-parametric MRIs with internal and external validations are needed to determine the clinical value of radiomics in iCC patients.

Influence of image preprocessing on the segmentation-based reproducibility of radiomic features: in vivo experiments on discretization and resampling parameters

To systematically investigate the impact of image preprocessing parameters on the segmentation-based reproducibility of magnetic resonance imaging (MRI) radiomic features. The MRI scans of 50 patients were included from the multi-institutional Brain Tumor Segmentation 2021 public glioma dataset. Whole tumor volumes were manually segmented by two independent readers, with the participation of eight readers. Radiomic features were extracted from two sequences: T2-weighted (T2) and contrast-enhanced T1-weighted (T1ce). Two methods were considered for discretization: bin count (i.e., relative discretization) and bin width (i.e., absolute discretization). Ten discretization (five for each method) and five resampling parameters were varied while other parameters were fixed. The intraclass correlation coefficient (ICC) was used for reliability analysis based on two commonly used cut-off values (0.75 and 0.90). Image preprocessing parameters had a significant impact on the segmentation-based reproducibility of radiomic features. The bin width method yielded more reproducible features than the bin count method. In discretization experiments using the bin width on both sequences, according to the ICC cut-off values of 0.75 and 0.90, the rate of reproducible features ranged from 70% to 84% and from 35% to 57%, respectively, with an increasing percentage trend as parameter values decreased (from 84 to 5 for T2; 100 to 6 for T1ce). In the resampling experiments, these ranged from 53% to 74% and from 10% to 20%, respectively, with an increasing percentage trend from lower to higher parameter values (physical voxel size; from 1 x 1 x 1 to 2 x 2 x 2 mm3). The segmentation-based reproducibility of radiomic features appears to be substantially influenced by discretization and resampling parameters. Our findings indicate that the bin width method should be used for discretization and lower bin width and higher resampling values should be used to allow more reproducible features.

Sex-based differences in brain morphometry under chronic stress: A pilot MRI study

BackgroundSex-based differences are known to be a significant feature of chronic stress; however, the morphological mechanisms of the brain underlying these differences remain unclear. The present study aimed to use magnetic resonance imaging (MRI) and voxel-based morphometry (VBM) to investigate the effects of sex on gray matter volume (GMV) changes under conditions of chronic stress. MethodsA total of 32 subjects were included for analysis in the present study: 16 participants experiencing chronic stress and 16 healthy controls. T1-weighted (T1WI) images from a 3 T MRI scanner were extracted from the OpenfMRI database. Images were segmented into gray matter using VBM analysis. A two-way analysis of variance (ANOVA) with a 2 × 2 full factorial design was used to evaluate the main and interaction effects of chronic stress and sex on GMV changes, and then post hoc testing was used to verify each simple effect. ResultsTwo-way ANOVA showed a chronic stress × sex interaction effect on GMV. Simple effects analysis indicated that the GMV of the bilateral pre- and post-central gyri, the right cuneus and superior occipital gyrus was decreased in males, whereas that of the bilateral pre- and post-central gyri, the right superior occipital gyrus and the left middle frontal gyrus and orbital middle frontal gyrus was increased in females, under chronic stress. Additionally, in the control group, the GMV of the bilateral pre- and post-central gyri, the right cuneus and superior occipital gyrus was greater in males than females. While in the chronic stress group, the above sex-based differences were no longer significant. ConclusionsThis study preliminarily shows that there are significant differences in gray matter volume changes between males and females under chronic stress. These findings provide a basis for future studies investigating the volumetric mechanisms of sex differences under chronic stress.