Diffusion-weighted Imaging Sequences Research Articles

Enhanced preoperative prediction of breast lesion pathology, prognostic biomarkers, and molecular subtypes using multiple models diffusion-weighted MR imaging

This study aims to comprehensively evaluate the clinical utility of five diffusion models, including conventional mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI), stretched exponential (SEM), and continuous-time random-walk (CTRW), for preoperatively predicting of breast lesion pathology, prognostic biomarkers, and molecular subtypes. We retrospectively analyzed 132 patients with pathologically verified breast lesions (41 benign and 91 malignant) who underwent a full protocol preoperative breast MRI protocol, including a diffusion-weighted imaging (DWI) sequence with nine b values (0 to 2000 s/mm2) on a 3.0T MR scanner. The diffusion parameters from each model—Mono (ADC), IVIM (D, D*, f), DKI (MD, MK), SEM (DDC, α) and CTRW (Dm, α, β)—were quantitatively calculated and compared between benign and malignant breast lesions, as well as across different prognostic biomarker statuses in breast cancer, using Mann–Whitney U-tests. For molecular subtypes comparisons, we employed the Kruskal-Wallis test followed by Bonferroni. All parameters, except IVIM-D*, significantly differentiated benign from malignant lesions. Notably, IVIM-D and DKI-MK values were significantly different between estrogen receptor (ER)-positive and ER-negative tumors. Progesterone receptor (PR)-positive cancers exhibited lower Mono-ADC, IVIM-D, DKI-MD, SEM-DDC, CTRW-Dm, and CTRW-α values, alongside higher DKI-MK value compared to PR-negative cancers (p < 0.05). Significant differences in IVIM-D, IVIM-D*, and DKI-MK values were observed between human epidermal growth factor receptor 2 (HER2)-negative and HER2-positive tumors. Furthermore, higher SEM-α and CTRW-β values, along with lower DKI-MD and SEM-DDC values, were noted in the high Ki-67 expression group compared to the low Ki-67 group (p < 0.05). All five diffusion models proved valuable for breast cancer diagnosis, with the CTRW model exhibiting the highest diagnostic performance, although the difference was not statistically significant. The diffusion parameters derived from these models can effectively assist in distinguishing prognostic factors and molecular subtypes of breast cancer.

Deep Learning Reconstruction Combined With Conventional Acceleration Improves Image Quality of 3 T Brain MRI and Does Not Impact Quantitative Diffusion Metrics.

Deep learning reconstruction of magnetic resonance imaging (MRI) allows to either improve image quality of accelerated sequences or to generate high-resolution data. We evaluated the interaction of conventional acceleration and Deep Resolve Boost (DRB)-based reconstruction techniques of a single-shot echo-planar imaging (ssEPI) diffusion-weighted imaging (DWI) on image quality features in cerebral 3 T brain MRI and compared it with a state-of-the-art DWI sequence. In this prospective study, 24 patients received a standard of care ssEPI DWI and 5 additional adapted ssEPI DWI sequences, 3 of those with DRB reconstruction. Qualitative analysis encompassed rating of image quality, noise, sharpness, and artifacts. Quantitative analysis compared apparent diffusion coefficient (ADC) values region-wise between the different DWI sequences. Intraclass correlations, paired sampled t test, Wilcoxon signed rank test, and weighted Cohen κ were used. Compared with the reference standard, the acquisition time was significantly improved in accelerated DWI from 75 seconds up to 50% (39 seconds; P < 0.001). All tested DRB-reconstructed sequences showed significantly improved image quality, sharpness, and reduced noise (P < 0.001). Highest image quality was observed for the combination of conventional acceleration and DL reconstruction. In singular slices, more artifacts were observed for DRB-reconstructed sequences (P < 0.001). While in general high consistency was found between ADC values, increasing differences in ADC values were noted with increasing acceleration and application of DRB. Falsely pathological ADCs were rarely observed near frontal poles and optic chiasm attributable to susceptibility-related artifacts due to adjacent sinuses. In this comparative study, we found that the combination of conventional acceleration and DRB reconstruction improves image quality and enables faster acquisition of ssEPI DWI. Nevertheless, a tradeoff between increased acceleration with risk of stronger artifacts and high-resolution with longer acquisition time needs to be considered, especially for application in cerebral MRI.

An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer

ObjectiveTo establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).MethodsThe data of a total of 60 mCRPC patients from two hospitals were retrospectively analyzed and randomized into a training group(n=48) or a validation group(n=12). By extracting features from biparametric MRI, including T2-weighted imaging(T2WI), diffusion-weighted imaging(DWI), and apparent diffusion coefficient(ADC) maps, radiomics features from the training dataset were selected using least absolute shrinkage and selection operator(LASSO) regression. Four predictive models were developed to assess the efficacy of abiraterone in treating patients with mCRPC. The primary outcome variable was the PSA response following AA treatment. The performance of each model was evaluated using the area under the receiver operating characteristic curve(AUC). Univariate and multivariate analyses were performed using Cox regression to identify significant predictors of the efficacy of abiraterone treatment in patients with mCRPC.ResultsThe integrated model was constructed from seven radiomics features extracted from the T2WI, DWI, and ADC sequence images of the training data. This model demonstrated the highest AUC in both the training and validation cohorts, with values of 0.889 (95% CI, 0.764-0.961) and 0.875 (95% CI, 0.564-0.991). The Rad-score served as an independent predictor of the response to abiraterone treatment in patients with mCRPC (HR: 2.21, 95% CI: 1.01-4.44).ConclusionThe biparametric MRI-based radiomics model has the potential to predict the PSA response in patients with mCRPC following abiraterone treatment.Clinical relevance statementThe MRI-based radiomics model could be used to noninvasively identify the AA response in mCRPC patients, which is helpful for early clinical decision-making.

CT and MR Imaging in Colorectal Carcinoma: A Tool for Diagnosis, Staging, Response Evaluation, and Follow-Up

The present review highlights the role of computed tomography (CT), CT colonography (CTC), and magnetic resonance imaging (MRI) in the diagnosis, staging, response evaluation, and follow-up of colorectal cancer. For a CT scan, prior bowel preparation is required. This is done to enhance imaging of the colon with the use of oral or rectal contrast agents. Negative contrast like air or carbon dioxide are helpful in detecting polyps and masses by distending the colon. Virtual colonoscopy offers a lower-radiation alternative for polyp and cancer detection. Intravenous contrast administration with arterial and venous phase CT images is also important in complete staging of a known case of colon cancer and for evaluation of residual/recurrent disease. With respect to MRI, high-resolution T2-weighted images in multiple planes are important, with diffusion-weighted imaging (DWI) sequences being important for restaging. Intravenous contrast is not generally recommended. Contrast-enhanced CT and MRI are used for nodal and distant metastasis staging, with special attention to the pelvic side wall nodes. Positron emission tomography (PET) CT is to be considered for further evaluation if the findings are unclear and recurrence is suspected.

Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review

PurposeThis systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy.MethodsThe review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as “artificial intelligence,” “rectal cancer,” “MRI,” and “pathological complete response.” Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applications. Data on study characteristics, MRI sequences, AI model details, and performance metrics were extracted. Quality assessment was performed using the PROBAST tool.ResultsOut of 512 initial records, 26 studies met the inclusion criteria. Most studies demonstrated promising diagnostic performance, with AUC values for external validation typically exceeding 0.8. The use of T2W and diffusion-weighted imaging (DWI) MRI phases enhanced model accuracy compared to T2W alone. Larger datasets generally correlated with improved model performance. However, heterogeneity in model designs, MRI protocols, and the limited integration of clinical data were noted as challenges.ConclusionAI-enhanced MRI demonstrates significant potential in predicting pCR in rectal cancer, particularly with T2W + DWI sequences and larger datasets. While integrating clinical data remains controversial, standardizing methodologies and expanding datasets will further enhance model robustness and clinical utility.

Audiovisual Breathing Guidance for Improved Image Quality and Scan Efficiency of T2- and Diffusion-Weighted Liver MRI

Objectives Impaired image quality and long scan times frequently occur in respiratory-triggered sequences in liver magnetic resonance imaging (MRI). We evaluated the impact of an in-bore active breathing guidance (BG) application on image quality and scan time of respiratory-triggered T2-weighted (T2) and diffusion-weighted imaging (DWI) by comparing sequences with standard triggering (T2S and DWIS) and with BG (T2BG and DWIBG). Materials and Methods In this prospective study, random patients with clinical indications for liver MRI underwent 3 T MRI with standard and BG acquisitions. The audiovisual BG application received the respiratory signal from the scanner, and animated breathing instructions were displayed using a mirror and screen behind the MRI bore. Prior to the DWIBG and T2BG acquisition, patients received a short video instruction about MRI with BG. Suitable parameters for desired breathing pattern for T2BG and DWIBG were set individually for each patient based on the patient's physical respiratory ability (ie, 4 seconds breathing followed by 4.5 seconds breath holding). Artifacts, sharpness, lesion conspicuity, and overall image quality were assessed using a Likert scale from 1 (nondiagnostic) to 5 (excellent). Scan time, apparent contrast-to-noise ratio, and apparent signal-to-noise ratio (aSNR) for all sequences were analyzed. Paired t test and Wilcoxon test were used for statistical analysis. Results Thirty-two patients (mean age: 55 ± 13 years, 13 female) were included. T2BG showed less artifacts (4.5 ± 0.7 vs 4.1 ± 0.8, P &lt; 0.001) and better sharpness, lesion conspicuity, and overall image quality (eg, overall image quality 4.6 ± 0.7 vs 4.4 ± 0.7, P = 0.004) compared with T2S. DWIBG demonstrated improved image quality in all categories compared with DWIS (eg, overall image quality 4.5 ± 0.5 vs 4.3 ± 0.5, P = 0.005) and less artifacts (4.1 ± 0.5 vs 3.8 ± 0.7, P = 0.007). Scan times of T2BG (286 ± 23 vs 345 ± 68 seconds, P &lt; 0.001) and DWIBG (160 ± 4 vs 252 ± 70 seconds, P &lt; 0.001) were reduced by 17% and 37%, respectively. aSNR and apparent contrast-to-noise ratio (eg, aSNR: 23.45 ± 11.31 [T2BG] vs 25.84 ± 10.76 [T2S]; P = 0.079) were similar for both sequences for both approaches. Conclusions Active BG for respiratory-triggered liver T2w and DWI sequences led to significant reduction of breathing artifacts, improved image quality, and shorter scan time compared with standard acquisitions.

A novel MRI-based radiomics for preoperative prediction of lymphovascular invasion in rectal cancer.

To develop and validate a clinical-radiomics model for preoperative prediction of lymphovascular invasion (LVI) in rectal cancer. This retrospective study included data from 239 patients with pathologically confirmed rectal adenocarcinoma from two centers, all of whom underwent MRI examinations. Cases from the first center (n = 189) were randomly divided into a training set and an internal validation set at a 7:3 ratio, while cases from the second center (n = 50) constituted the external validation set. The clinical features and MRI imaging characteristics of the patients in the training set were analyzed. Univariate and multivariate logistic regression analyses were used to identify independent risk factors for LVI in rectal cancer, and these risk factors were then used to construct a clinical model. Regions of interest (ROIs) were delineated on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences for feature extraction. After feature reduction and selection, the most strongly correlated features were identified, and their respective regression coefficients were calculated to construct the radiomics model. Finally, a combined clinical-radiomics model was built using a weighted linear combination of features and was visualized as a nomogram. The predictive performance of each model was quantified using receiver operating characteristics (ROC) curves and the area under the curve (AUC) in both training and validation sets, with DeLong analysis being used to compare model performance. Decision curve analysis (DCA) was used to evaluate the clinical utility of each model in the validation sets. In the 239 patients, the combined model outperformed the clinical and radiomics models in predicting LVI in rectal cancer. The combined model showed excellent predictive performance in the training, internal validation, and external validation sets, with AUCs of 0.90 (0.88-0.97), 0.88 (0.78-0.99), and 0.88 (0.78-0.95), respectively. The sensitivity values were 75.9%, 68.8%, and 80.0%, respectively, and the specificity values were 90.3%, 92.7%, and 88.6%. DCA results indicated that the nomogram of the combined model had superior clinical utility compared with the clinical and radiomics models. The clinical-radiomics nomogram serves as a valuable tool for non-invasive preoperative prediction of LVI status in patients with rectal cancer.

Monoexponential, biexponential, stretched exponential and diffusion kurtosis models of diffusion-weighted imaging: a quantitative differentiation of solitary pulmonary lesion

BackgroundDiffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.ObjectiveTo investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).MethodForty-four SPL subjects were selected according to the inclusion criteria. All patients underwent conventional and multi‑b DWI sequences. Monoexponential DWI and DKI model were fitted using least square method. Levenberg-Marquardt nonlinear fitting biexponential and stretched exponential DWI. Region of interests (ROIs) were described manually. Parameters between benign and malignant SPLs were compared using independent sample t test or the Mann-Whitney U test. Receiver operating characteristic (ROC) curves analysis was used to investigate the diagnostic performance of different DWI parameters. Correlation between all parameters were evaluated by using Spearman correlation.ResultADC, ADCslow, α, DDC and Dapp values were significantly lower in malignant SPL than in benign SPL (P < 0.001). Kapp was significantly higher in malignant SPL than in benign SPL (P < 0.001). Among all subjects, ADCslow was significantly lower than ADC (P < 0.05), while DDC and Dapp were significantly higher than ADC (P < 0.05). When observing the ROC curves for distinguishing benign and malignant SPL, the AUC values of ADC, ADCslow, DDC, Dapp, and Kapp were 0.904, 0.815, 0.942, 0.93, and 0.815, respectively. The DDC value has the highest area under ROC curve value. DeLong analysis showed no statistically significant difference in the area under ADC, DDC, and Dapp curves. There were strong correlations among ADC, ADCslow, ADCfast, f, α, DDC, Dapp, and Kapp (P < 0.001).ConclusionMulti‑b DWI is a promising method for differentiating benign from malignant SPLs with high diagnostic accuracy. In addition, the DDC derived from stretched‑exponential model is the most promising DWI parameter for the differentiation of benign and malignant SPLs.Trail registrationThis study was a clinical trail study, with study protocol published at ClinicalTrails. Retrospectively registered number ChiCTR2300074258, date of registration 02/08/2023.

Hypoxic brain damage in Methadone misuse: insights from MRI imaging and comparative study.

This study aimed to investigate the potential presence of brain disorders, particularly hypoxia, via magnetic resonance imaging (MRI) in patients misusing methadone, with a comparison to regular opium users and a control group. Conducted as a cross-sectional comparative study at Kamali Hospital in Karaj, Iran, the research included male participants comprising methadone users, opium users, and controls. Inclusion criteria were stringent, focusing on substance use duration and absence of brain structural disorders. MRI scans were performed using a 1.5T MRI scanner. Qualitative MRI assessments and chi-square tests analyzed associations between substance use and hypoxia, while logistic regression examined potential confounding variables. Significant hypoxia was observed in the methadone group (16.7%, 5/24; p = 0.00057), with no cases in the opium or control groups. Logistic regression analysis showed no significant predictors of hypoxia regarding dose and duration of use. MRI findings in methadone users with hypoxia included varied ADC intensities, high signal intensities on T2-weighted and diffusion-weighted imaging (DWI) sequences, and angiogenesis patterns on TOF sequences. The co-use of methadone and alcohol was noted in three of the five hypoxia cases. Methadone misuse, particularly with alcohol, poses a significant risk of hypoxia, detectable via MRI. This study underscores the need for routine MRI monitoring, stricter regulation of non-prescribed methadone, and enhanced public health education to mitigate misuse risks. Future research should expand sample sizes and incorporate advanced imaging techniques to further elucidate methadone's neurological impact.

AI-Based Evaluation of Prostate MR Imaging at a Modern Low-field 0.55 T Scanner Compared to 3 T in a Screening Cohort

PurposeTo evaluate the effect of lower field strength on quantitative apparent-diffusion-coefficient (ADC) values, contrast of the T2-weighted MR images and the performance of an AI-based segmentation. Materials and Methods25 screening clients (61.6 ± 7.5 years) from a study on a 3T scanner were included and underwent a second examination on a 0.55T scanner. Axial T2 weighted and diffusion-weighted images (DWI) sequences were acquired. An AI-based segmentation was performed. Based on this, the segmentation, volumetry, ADC values and the ratio of central gland (CG) and peripheral zone (PZ) in T2 weighting were compared by using correlations coefficient (Pearson), Bland–Altman plots and a non-inferior test with a paired t-test and a margin of ±20% (lower and upper boundary). ResultsVolumetric assessment (peripheral zone//central gland) showed no significant (p=0.13//0.38) difference between 3T (mean volume: 14.81 (12.53 – 17.09) // 23.07 (15.06 – 31.08) ml) and 0.55T (mean volume of 14.29 (12.03 – 16.54; p = 0.13) // 22.77 (14.41 – 31.12) ml). The deviation of the 0.55 T ADC values from the 3 T values was -10.14% (-16.09% - -4.18%) for the PZ and -4.68% (-10.12% - 0.76%) for the CG. Therefore, all confidence intervals remained within a margin of +/- 20% and thus demonstrated significant non-inferiority. ConclusionBiparametric prostate imaging is feasible at 0.55 T: ADC values vary within a common inter-scanner range compared to a 3T and no difference can be observed regarding contrast ratio between peripheral zone and central gland in T2 weighted images, volumetry and AI-based segmentation compared to 3T.

Development and Validation of Multiparametric MRI-based Interpretable Deep Learning Radiomics Fusion Model for Predicting Lymph Node Metastasis and Prognosis in Rectal Cancer: A Two-center Study

Rationale and ObjectivesTo develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer. Materials and MethodsThis retrospective study involved 286 cancer patients confirmed by histopathology from center 1 (Training set) and 66 patients from center 2 (External test set). Radiomics features were extracted from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences, whereas DL features were obtained using four models: MobileNet-V3-large, Inception-V3, ResNet50, and VGG16. These DL radiomics (DLR) features were then combined to construct a machine learning model. The Shapley additive interpretation (SHAP) tool was utilized to investigate the interpretability of the model. We evaluated and compared the diagnostic performance of senior and junior radiologists, with and without the aid of the optimal DLR model. Kaplan–Meier survival curve was used to analyze the prognosis of patients. ResultsThe DLR model outperforms individual DL models and the radiomics model. The MobileNet-V3-large combination radiomics signature demonstrated the best performance, achieving an AUC of 0.878 on the Training set and 0.752 on the External test set. Compared to the traditional radiomics model, the AUC for the Training set increased by 0.094 and by 0.051 for the External test set. This model facilitated improved diagnostic performance among both junior and senior radiologists. Specifically, the AUC values for junior and senior radiologists increased by 0.162 and 0.232, respectively, on the Training set; and by 0.096 and 0.113, respectively, on the External test set. The DLR model demonstrated strong performance in risk stratification for disease-free survival. ConclusionThe DLR model developed from multiparametric MRI can effectively distinguish cancer LN metastasis and enhance radiologists' diagnostic performance.

Comparison of multi-phase contrast-enhanced T1-weighted volumetric interpolated breath-hold examination and fat-suppressed T2-weighted combined with diffusion-weighted magnetic resonance imaging in anal fistula evaluation.

Anal fistula is a common anorectal disorder that significantly diminishes the quality of life for affected patients. Accurate preoperative evaluation of the fistula's traits is essential for customizing surgical strategies, improving patient outcomes, and reducing the likelihood of the disease returning. This study aimed to evaluate the diagnostic accuracy of multi-phase contrast-enhanced fat-suppressed T1-weighted imaging using three-dimensional gradient echo sequence volumetric interpolated breath-hold examination (CE-FS-T1-3D-VIBE) and fat-suppressed T2-weighted imaging combined with diffusion-weighted imaging (FS-T2WI-DWI) sequence in delineating the characteristics of anal fistulas. A case-control study of 168 patients with anal fistula was conducted through the picture archiving and communication systems (PACS; diagnostic imaging workstation). Imaging evaluations were performed using both multi-phase CE-FS-T1-3D-VIBE and FS-T2WI-DWI imaging on a Siemens 3.0T magnetic resonance imaging system (Skyra 3.0T superconducting type). The efficacy of each imaging modality in depicting the clarity, number, and positioning of the internal openings, as well as the identification of primary and secondary fistulas and abscesses, was independently evaluated in a blinded manner by two senior diagnostic radiologists, each with over a decade of experience. Statistical analyses were performed using χ2 test. Comparative analysis of the FS-T2WI-DWI and multi-phase CE-FS-T1-3D-VIBE sequences for diagnosing internal and primary fistula tract clarity demonstrated a significant superiority of the multi-phase CE-FS-T1-3D-VIBE sequence in delineating internal clarity (P=0.013) and primary fistula tract clarity (P<0.001). The multi-phase CE-FS-T1-3D-VIBE sequence demonstrated superior accuracy over the FS-T2WI-DWI sequence in depicting localization of internal openings [86.31% vs. 77.38%, 95% confidence interval (CI): 0.307-0.959, P=0.034], as well as delineation of the secondary tracts (88.69% vs. 80.95%, 95% CI: 0.293-1.001, P=0.048). Despite the increased accuracy of the multi-phase CE-FS-T1-3D-VIBE sequence, no difference was observed between the two imaging techniques regarding the accuracy in determining the number of internal openings, the quantity of primary fistulas, and the classification of anal fistulas. This study elucidates that the multi-phase CE-FS-T1-3D-VIBE imaging sequence potentially represents a more effective noninvasive alternative for the precise evaluation of the positioning and clarity of the internal opening, as well as the delineation of primary and secondary fistula tracts in anal fistula patients, compared to the FS-T2WI-DWI sequence. This enhanced diagnostic capability underscores the utility of multi-phase CE-FS-T1-3D-VIBE in improving the clinical management of anal fistulas.

Elucidating Microstructural Alterations in Neurodevelopmental Disorders: Application of Advanced Diffusion-Weighted Imaging in Children With Rasopathies.

Neurodevelopmental disorders (NDDs) can severely impact functioning yet effective treatments are limited. Greater insight into the neurobiology underlying NDDs is critical to the development of successful treatments. Using a genetics-first approach, we investigated the potential of advanced diffusion-weighted imaging (DWI) techniques to characterize the neural microstructure unique to neurofibromatosis type 1 (NF1) and Noonan syndrome (NS). In this prospective study, children with NF1, NS, and typical developing (TD) were scanned using a multi-shell DWI sequence optimized for neurite orientation density and dispersion imaging (NODDI) and diffusion kurtosis imaging (DKI). Region-of-interest and tract-based analysis were conducted on subcortical regions and white matter tracts. Analysis of covariance, principal components, and linear discriminant analysis compared between three groups. 88 participants (Mage = 9.36, SDage = 2.61; 44 male) were included: 31 NS, 25 NF1, and 32 TD. Subcortical regions differed between NF1 and NS, particularly in the thalamus where the neurite density index (NDI; estimated difference 0.044 [95% CI: -0.034, 0.053], d = 2.36), orientation dispersion index (ODI; estimate 0.018 [95% CI: 0.010, 0.026], d = 1.39), and mean kurtosis (MK; estimate 0.049 [95% CI: 0.025, 0.072], d = 1.39) were lower in NF1 compared with NS (all p < 0.0001). Reduced NDI was found in NF1 and NS compared with TD in all 39 white matter tracts investigated (p < 0.0001). Reduced MK was found in a majority of the tracts in NF1 and NS relative to TD, while fewer differences in ODI were observed. The middle cerebellar peduncle showed lower NDI (estimate 0.038 [95% CI: 0.021, 0.056], p < 0.0001) and MK (estimate 0.057 [95% CI: 0.026, 0.089], p < 0.0001) in NF1 compared to NS. Multivariate analyses distinguished between groups using NDI, ODI, and MK measures. Principal components analysis confirmed that the clinical groups differ most from TD in white matter tract-based NDI and MK, whereas ODI values appear similar across the groups. The subcortical regions showed several differences between NF1 and NS, to the extent that a linear discriminant analysis could classify participants with NF1 with an accuracy rate of 97%. Differences in neural microstructure were detected between NF1 and NS, particularly in subcortical regions and the middle cerebellar peduncle, in line with pre-clinical evidence. Advanced DWI techniques detected subtle alterations not found in prior work using conventional diffusion tensor imaging.

0.23-Tesla MRI to differentiate between ischaemic and haemorrhagic strokes within 24 hours of onset: a combined experimental–clinical study

Background and purposeThe low-field MRI is a promising tool to accurately diagnose strokes. We here report our study on the accuracy of a 0.23-Tesla (0.23-T) MRI using the haematoma enhanced...

Comparative analysis of image quality and diagnostic performance among SS-EPI, MS-EPI, and rFOV DWI in bladder cancer.

To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC). This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance. Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types. Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.

MRI Tomoelastography to Assess the Combined Status of Vessels Encapsulating Tumor Clusters and Microvascular Invasion in Hepatocellular Carcinoma.

Integrating vessels encapsulating tumor clusters (VETC) and microvascular invasion (MVI) (VM hereafter) is potentially useful in risk stratification of hepatocellular carcinoma (HCC). However, noninvasive assessment methods for VM are lacking. To investigate the diagnostic performance of tomoelastography in assessing the VM status in HCC. Retrospective. One hundred sixty-eight patients with surgically confirmed HCC consisting of 115 training and 53 validation cohorts, divided into negative-VM and positive-VM groups with mild or severe-VMs. Of them, 127 patients completed the follow-up (median: 26.1 months). 3D multifrequency tomoelastography with a single-shot spin-echo echo-planar imaging sequence, and liver MRI including T1-weighted in-phase and opposed-phase gradient echo (GRE), T2-weighted turbo spin echo, diffusion-weighted imaging and dynamic contrast-enhanced T1-weighted GRE sequences at 3.0 T. Shear wave speed (c) and phase angle of the shear modulus (φ) were calculated on tomoelastograms. Imaging features were visually analyzed and clinical features were collected. Conventional models used clinical and imaging features while nomograms combined tomoelastography, clinical and imaging features. Univariable and multivariable logistic regression analyses, nomogram, area under the receiver operating characteristic curve (AUC), DeLong test, Kaplan-Meier analysis and log-rank test. P < 0.05 was considered statistically significant. Tumor-to-liver parenchyma ratio of c (cr) and tumor c were independent risk factors for positive-VM and severe-VM, respectively. In validation cohort, the nomograms including cr and tumor c performed significantly better than the conventional models for diagnosing positive-VM (0.84 [95% CI: 0.72-0.93] vs. 0.77 [95% CI: 0.64-0.88]) and severe-VM (0.86 [95% CI: 0.68-0.96] vs. 0.75 [95% CI: 0.55-0.89]). Patients with estimated positive-VM (9.3 months)/severe-VM (9.2 months) based on nomograms had shorter median recurrence-free survival than those with estimated negative-VM (>20.0 months)/mild-VM (18.0 months) in validation cohort. Tomoelastography based-nomograms showed good performance for noninvasively assessing VM status in patients with HCC. 3 TECHNICAL EFFICACY: Stage 2.

Effects of Gadolinium Chelate Administration Timing on T2-weighted and Diffusion-weighted Abdominal MRI Examination: A Prospective Study.

Magnetic Resonance Imaging (MRI) data acquisition includes several sequences that might be optimized to reduce the scan time. This study aimed to investigate the impact of gadolinium chelate administration timing on scan duration and image quality in Diffusion-weighted Imaging (DWI) and T2-weighted Imaging (T2WI) during abdominal MRI examinations. A prospective study was conducted from October 2018 to May 2020. Study participants were assigned into a conventional group, undergoing MRI with DWI and T2WI sequences pre and post-gadolinium injection, or an optimized group, receiving MRI with DWI and T2WI sequences after gadolinium injection. Quantitative image quality, measured by the Signal-to-noise Ratio (SNR), Contrast-to-noise Ratio (CNR), and Apparent Diffusion Coefficient (ADC), was analyzed. Kappa statistics were employed for the inter-observer agreement on liver lesion detection. Our study has included 341 patients, with 168 and 173 in the conventional and optimized groups, respectively. Mean scan durations were 1,304 (±143) and 1,015 (±129) s for the conventional and optimized groups, respectively (p<0.05). For the liver, spleen, and pancreas, SNR and ADC remained statistically unchanged in post-enhanced DWI and T2WI (p>0.05). Significant decreases in the SNR and ADC of the kidney were observed in post-contrast DWI and T2WI (p<0.05). Hepatic lesion detectability did not show significant differences between pre and post-contrast DWI and T2WI images (p>0.05). DWI and T2WI sequences assessed post-gadolinium administration exhibited shortened scan time without compromising the image quality for liver, spleen, and pancreas evaluations. However, these sequences should be examined before gadolinium administration when assessing the kidneys.

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.

To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI. The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value = 50-1500 s/mm2). Three readers evaluated the vCE images with regard to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p < 0.05). Non-significant effects (p > 0.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training. Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research.

Ultra-Low-Field Portable MRI and Extracorporeal Membrane Oxygenation: Preclinical Safety Testing.

Conventional MRI is incompatible with extracorporeal membrane oxygenation (ECMO) cannulas and pumps. Ultra-low-field portable MRI (ULF-pMRI) with 0.064 Tesla may provide a solution, but its safety and compatibility is unknown. ULF-pMRI does not cause significant displacement and heating of ECMO cannulas and does not affect ECMO pump function. ECMO cannulas in various sizes were tested ex vivo using phantom models to assess displacement force and heating according to the American Society for Testing and Materials criteria. ECMO pump function was assessed by pump flow and power consumption. In vivo studies involved five female domestic pigs (20-42 kg) undergoing different ECMO configurations (peripheral and central cannulation) and types of cannulas with an imaging protocol consisting of T2-weighted, T1-weighted, FLuid-Attenuated Inversion Recovery, and diffusion-weighted imaging sequences. Phantom models demonstrated that ECMO cannulas, both single lumen with various sizes (15-24-Fr) and double lumen cannula, had average displacement force less than gravitational force within 5 gauss safety line of ULF-pMRI and temperature changes less than 1°C over 15 minutes of scanning and ECMO pump maintained stable flow and power consumption immediately outside of the 5 gauss line. All pig models showed no visible motion due to displacement force or heating of the cannulas. ECMO flow and the animals' hemodynamic status maintained stability, with no changes greater than 10%, respectively. ULF-pMRI is safe and feasible for use with standard ECMO configurations, supporting its clinical application as a neuroimaging modality in ECMO patients.

A controlled study of MR DWI, FS-PDW, and CE-T1W imaging for the evaluation of the internal opening of anal fistulas.

Anal fistula (AF) is an abnormal tunnel under the skin connecting the anal canal in the colon to the skin of buttocks. Fat-suppressed (FS) proton density-weighted (PDW) imaging is mainly used for the diagnosis of diseases involving bones and joints. Until now, its value in the diagnosis of anal fistula has been rarely reported. To compare three magnetic resonance imaging (MRI) sequences - diffusion-weighted imaging (DWI), FS-PDW), and contrast-enhanced (CE) T1-weighted (T1W) imaging - for the diagnostic value of the internal opening of AF. MRI scans of 132 patients suspected of having AF between December 2021 and April 2023 were retrospectively analyzed. In total, 65 patients who underwent preoperative MRI and were treated surgically were included. The lesion conspicuity and accuracy for featuring AF were calculated by evaluating the three imaging datasets DWI, FS-PDW, and CE-T1W imaging, with surgical findings serving as the reference standard for the presence of fistulas. The statistical analysis included the application of the chi-square test and Kruskal-Wallis test. In 65 patients with AF, 87 internal openings of AF were confirmed. In terms of the diagnostic accuracy of the internal openings, both FS-PDW and CE-T1W imaging sequences were significantly better than DWI sequences, and the difference was statistically significant (P < 0.05). The FS-PDW imaging sequence showed comparable diagnostic performance of the internal opening of AF to CE-T1W imaging, which can provide an important diagnostic basis for clinical procedures.