Diffusion-weighted Imaging Research Articles

Short- and Long-Term Prognoses After Tissue-Negative Transient Ischemic Attack.

The tissue-based definition of transient ischemic attack, which requires the use of diffusion-weighted imaging (DWI), has limitations in its applicability to clinical practice. This contributes to the limited evidence regarding the risk of subsequent stroke and the associated predictors in the group of patients who are tissue-negative on DWI. Our aim was to assess the early and long-term prognoses of consecutive patients with tissue-negative transient ischemic attacks attended at an emergency department. We performed a prospective cohort study of consecutive patients with neurologist-confirmed transient ischemic attack who were DWI-negative from January 2006 to June 2010. All patients underwent DWI on magnetic resonance imaging (4.0 [SD, 1.8] days) after the index event. The risk and predictors of stroke recurrence (SR) were determined at 1 year and after a median follow-up time of 6.6 (interquartile range, 5.0-9.6) years. A total of 370 patients were included. Previously, 244 patients with positive DWI results and 109 patients without magnetic resonance imaging performed were excluded. ABCD2 score >5 was determined in 95 (26.2%) patients; 15 (4.1%) patients experienced SR at 1 year and 18 (4.9%) beyond 1 year. Predictive models for short- and long-term prognoses were different. Large artery atherosclerosis cause (hazard ratio, 3.7 [95% CI, 1.2-11.0]) was the only predictor of 1-year SR. In contrast, male sex (hazard ratio, 4.17 [95% CI, 1.14-15.23]; P=0.031), speech impairment (hazard ratio, 4.90 [95% CI, 1.05-22.93]; P=0.044), and the presence of chronic microangiopathy expressed as Fazekas score of 3 (hazard ratio, 1.84 [95% CI, 1.15-2.97]; P=0.012) were predictors of long-term SR follow-up. Patients with DWI-negative have a clinically important risk of recurrent vascular events and SR during medium- and long-term follow-ups. These patients warrant optimized secondary prevention to reduce their risk of recurrent vascular events over time. Predictors of SR varied over the follow-up.

Unsupervised clustering analysis-based characterization of spatial profiles of inaccuracy in apparent diffusion coefficient values with varying acquisition plan orientation and diffusion weighting gradient direction – a large multicenter phantom study

This large multicenter study of 37 magnetic resonance imaging scanners aimed at characterizing, for the first time, spatial profiles of inaccuracy (namely, Δ-profiles) in apparent diffusion coefficient (ADC) values with varying acquisition plan orientation and diffusion weighting gradient direction, using a statistical approach exploiting unsupervised clustering analysis. A diffusion-weighted imaging (DWI) protocol (b-value: 0-200-400-600-800-1000 s mm-2) with different combinations of acquisition plan orientation (axial/sagittal/coronal) and diffusion weighting gradient direction (anterior-posterior/left-right/feet-head) was acquired on a standard water phantom. For each acquisition setup, Δ-profiles along the 3 main orthogonal directions were characterized by fitting data with a second order polynomial function (ar2+ br + c). Moreover, for each Δ-profile, the maximum minus minimum of the fitting function (δmax) was calculated. The parametersa,b,c, andδmaxshowed some significant variations between scanner systems by different manufacturers or with different static magnetic field strengths, as well as between different acquisition/estimation setups. Unsupervised clustering analysis showed two evident clusters with significantly different values of parametera(p< 0.0001), which can be grouped by acquisition protocol/Δ-profile direction but not scanner system. The results of ∆-profiles confirm an appreciable inter-scanner variability in ADC measurement and corroborate the importance of guarantying the reliability of ADC estimations in clinical or research studies, considering for each scanner system the specific acquisition sequence in terms of acquisition plan orientation and diffusion weighting gradient direction.

Distortion-free water-fat separated diffusion-weighted imaging using spatiotemporal joint reconstruction.

Diffusion-weighted imaging (DWI) suffers from geometric distortion and chemical shift artifacts due to the commonly used Echo Planar Imaging (EPI) trajectory. Even with fat suppression in DWI, severe B0 and B1 variations can result in residual fat, which becomes both a source of image artifacts and a confounding factor in diffusion-weighted contrast in distinguishing benign and malignant tissues. This work presents a method for acquiring distortion-free diffusion-weighted images using spatiotemporal acquisition and joint reconstruction. Water-fat separation is performed by chemical-shift encoding. Spatiotemporal acquisition is employed to obtain distortion-free images at a series of echo times. Chemical-shift encoding is used for water-fat separation. Reconstruction and separation are performed jointly in the spat-spectral domain. To address the shot-to-shot motion-induced phase in DWI, an Fast Spin Echo (FSE)-based phase navigator is incorporated into the sequence to obtain distortion-free phase information. The proposed method was validated in phantoms and in vivo for the brain, head and neck, and breast. The proposed method enables the acquisition of distortion-free diffusion-weighted images in the presence of B0 field inhomogenieties commonly observed in the body. Water and fat components are separated with no obvious spectral leakage artifacts. The estimated Apparent Diffusion Coefficient (ADC) is comparable to that of multishot DW-EPI. Distortion-free, water-fat separated diffusion-weighted images in body can be obtained through the utilization of spatiotemporal acquisition and joint reconstruction methods.

The fuzzy MAD stroke conjecture, using fuzzy C means to classify multimodal apparent diffusion for ischemic stroke lesion stratification

BackgroundIn conjunction with an epidemiologically determined treatment window, current radiological acute ischemic stroke practice discerns two lesion (stage) types: core (dead tissue, identified by diffusion-weighted imaging (DWI)) and penumbra (tissue region receiving just enough blood flow to be potentially salvageable, identified by the perfusion diffusion mismatch). However, advancements in preclinical and clinical studies have indicated that this approach may be too rigid, warranting a more fine-grained patient-tailored approach. This study aimed to demonstrate the ability to noninvasively provide insights into the current in vivo stroke lesion cascade. MethodsTo elucidate a finer-grained depiction of the acute focal ischemic stroke cascade in vivo, we retrospectively applied our multimodal apparent diffusion (MAD) method to multi-b-value DWI, up to a b-value of 10,000 s/mm2 in 34 patients with acute focal ischemic stroke. Fuzzy C Means was used to cluster the MAD parameters. ResultsWe discerned 18 clusters consistent with normal appearing tissue (NAT) types and 14 potential ischemic lesion (stage) types, providing insights into the variability and aggressiveness of lesion progression and current anomalous stroke-related imaging features. Of the 529 ischemic stroke lesion instances previously identified by two radiologists, 493 (92 %) were autonomously identified; 460 (87 %) were identified as efficaciously or better than the radiologists. ConclusionsThe data analyzed included a small number of clinical patients without follow-up or contemporaneous histology; therefor, the findings and theorizing should be treated as conjecture. Nevertheless, each identified NAT and lesion type is consistent with the known underpinnings of physiological tissues and pathological ischemic stroke lesion (stage) types. Several findings should be considered in current clinical imaging: WM fluid accumulation, BBB compromise conundrum, b1000 identified core may not be dead tissue, and a practical reason for DWI (pseudo) normalization.

Assessing parotid gland function through diffusion weighted MRI during and post-radiotherapy in head and neck cancer patients

ObjectivesThe purpose of this study was to evaluate the usefulness of apparent diffusion coefficient (ADC) variation in irradiated parotid glands during and after 3D conformal radiotherapy (3DCRT). Materials and methodsThis study enrolled 15 head and neck cancer (HNC) patients who were treated with 3DCRT underwent diffusion weighted imaging (DWI) at rest and after gustatory stimulation in three time points as follows: before, during (one day after receiving the mean dose 26 Gy) and 6 months after the end of radiotherapy (RT). Salivary Ejection Fraction (SEF) data was also obtained from salivary gland scintigraphy (SGS) at the same three time points as MRI. Mean ADC at rest and after stimulation (ADCr, ADCs) and SEF were extracted from parotid region for three time points. Finally, SEF and changes of ADC over time at rest and after stimulation and in each time point were compared. ResultsDifference between mean ADCr before RT and during RT was not significant (p = 0.12). The ADCr values were significantly higher after RT than before RT (p = 0.003) and during RT (after dose 26 Gy) (p = 0.001). The difference between ADCr and ADCs in each time point showed that there is significant difference between ADCr and ADCs before RT (p = 0.005). Difference between these parameters after RT was also significant (p = 0.05). Non-significant difference (p = 0.21) between ADCr and ADCs during RT was observed. The result of SEF also was significantly lower after RT than during (p = 0.008) and before RT (p = 0.001). ConclusionMean ADC values could be used as a surrogate marker to characterize the parotid function in different stages of RT.

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.

Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images

Abstract Purpose: This study aimed to investigate the performance of multiparametric magnetic resonance imaging (mpMRI) radiomic feature-based machine learning (ML) models in classifying the Gleason grade group (GG) of prostate cancer. Methods: In this retrospective study, a total of 203 patients with histopathologically confirmed prostate cancer who underwent mpMRI before prostate biopsy were included. After manual segmentation, radiomic features (RFs) were extracted from T2-weighted, apparent diffusion coefficient, and high b-value diffusion-weighted magnetic resonance imaging (DWMRI). Patients were split into training sets and testing sets according to a ratio of 8:2. A pipeline considering combinations of two feature selection (FS) methods and six ML classifiers was developed and evaluated. The performance of models was assessed using the accuracy, sensitivity, precision, F1-measure, and the area under curve (AUC). Results: On high b-value DWMRI-derived features, a combination of FS method recursive feature elimination (RFE) and classifier random forest achieved the highest performance for classification of prostate cancer into five GGs, with 97.0% accuracy, 98.0% sensitivity, 98.0% precision, and 97.0% F1-measure. The method also achieved an average AUC for GG of 98%. Conclusion: Preoperative mpMRI radiomic analysis based on ML, as a noninvasive approach, showed good performance for classification of prostate cancer into five GGs. Advances in Knowledge: Herein, radiomic models based on preoperative mpMRI and ML were developed to classify prostate cancer into 5 GGs. Our study provides evidence that analysis of quantitative RFs extracted from high b-value DWMRI images based on a combination of FS method RFE and classifier random forest can be applied for multiclass grading of prostate cancer with an accuracy of 97.0%.

Evaluating the Prognostic Impact of Apparent Diffusion Coefficient in Definitive Radiotherapy for Gleason Score 7 Prostate Cancer Patients.

To investigate the utility of diffusion-weighted magnetic resonance imaging (DW-MRI) in evaluating Gleason score (GS) 7 tumors before definitive radiotherapy (RT) and to explore its association with clinicopathological factors and treatment outcomes. Clinical data of 266 prostate cancer (PCa) patients with biopsy-confirmed GS 7 who underwent RT were retrospectively analyzed. Pretreatment DW-MRI was utilized to measure apparent diffusion coefficient (ADC) values of primary tumors. Treatment outcomes, including biochemical disease-free survival (bDFS) and prostate cancer-specific survival (PCSS), were assessed. Statistical analyses were conducted to determine the correlation between tumor ADC values, clinicopathological factors, and treatment outcomes. Tumors with a GS of 3 + 4 had significantly higher ADC values than those with a GS of 4 + 3 (0.746 ± 0.150 vs. 0.702 ± 0.157 × 10-³ mm²/s; p < 0.001). Median follow-up time was 8.6 years, and the 7-year rates for bDFS and PCSS were 89.1% and 95.3%, respectively. Lower tumor ADC values were significantly correlated with higher GS and increased risk of disease progression. A primary tumor ADC cutoff value of 0.682 × 10-³ mm²/s was identified for predicting disease progression. Patients with higher ADC values exhibited significantly better 7-year bDFS rates (92.8% vs. 83.2%; p = 0.02). However, GS 4 + 3 tumors independently predicted poorer bDFS and PCSS outcomes. In the multivariable analysis, only GS 4 + 3 tumor was predictive for worse bDFS and PCSS. Tumor ADC values are a reliable biomarker for differentiating between GS 3 + 4 and 4 + 3 tumors in the GS 7 category. Tumors exhibiting lower ADC values have been associated to higher risk factors and an increased likelihood of disease progression, particularly in GS 3 + 4 tumors where GS upgrading could happen.

Deep learning-based segmentation of acute ischemic stroke MRI lesions and recurrence prediction within 1 year after discharge: A multicenter study

ObjectiveTo explore the performance of deep learning-based segmentation of infarcted lesions in the brain MRI of patients with AIS and the recurrence prediction value of radiomics within 1 year after discharge as well as to develop a model incorporating radiomics features and clinical factors to accurately predict AIS recurrence. Materials and MethodsTo generate a segmentation model of MRI lesions in AIS, the deep learning algorithm multiscale residual attention UNet (MRA-UNet) was employed. Furthermore, the risk factors for AIS recurrence within 1 year were explored using logistic regression analysis. In addition, to develop the prediction model for AIS recurrence within 1 year after discharge, four machine learning algorithms, namely, LR, RandomForest, CatBoost, and XGBoost, were employed based on radiomics data, clinical data, and their combined data. ResultsIn the validation set, the MDice and MIou of the MRA-UNet segmentation model were 0.816 and 0.801, respectively. In multivariate logistic regression analysis, age, renal insufficiency, C-reactive protein, triglyceride glucose index, prognostic nutritional index, and infarct volume were identified as the independent risk factors for stroke recurrence. Furthermore, in the validation set, combining radiomics data and clinical data, the AUC was 0.835 (95%CI:0.738, 0.932), 0.834 (95%CI:0.740, 0.928), 0.858 (95%CI:0.770, 0.946), and 0.842 (95%CI:0.752, 0.932) for the LR, RandomForest, CatBoost, and XGBoost models, respectively. ConclusionThe MRA-UNet model can effectively improve the segmentation accuracy of diffusion-weighted images. The model, which was established by combining radiomics features and clinical factors, held some value for predicting ischemic stroke recurrence within 1 year.

Impact of diffusion-weighted imaging on agreement between radiologists and non-radiologists in musculoskeletal tumor imaging using magnetic resonance.

BackgroundDiffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors. AimThis study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist. MethodsThirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet’s AC1 coefficients and interpreted according to Landis and Koch. ResultsThe lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772 to 0.965 among only radiologists indicating substantial to almost perfect agreement. ConclusionThe agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

PI-QUAL version 2 image quality categorisation and inter-reader agreement compared to version 1.

Prostate imaging quality (PI-QUAL) was developed to standardise the evaluation of prostate MRI quality and has recently been updated to version 2. This study aims to assess inter-reader agreement for PI-QUAL v1 and v2 scores and investigates changes in MRI quality score categories. The study retrospectively analysed 350 multiparametric MRI (mpMRI) scans. Two expert uroradiologists independently assessed mpMRI quality using PI-QUAL v1 and v2 guidelines. Biparametric MRI (bpMRI) categorisation based on PI-QUAL v2 included only T2WI and diffusion-weighted imaging (DWI) results. Inter-reader agreement was determined using percentage agreement and kappa, and categorisation comparisons were made using the chi-square test. Substantial inter-reader agreement was observed for the overall PI-QUAL v1 score (κ = 0.64) and moderate agreement for v2 mpMRI (κ = 0.54) and v2 bpMRI scores (κ = 0.57). Inter-reader agreements on individual sequences were similar between v1 and v2 (kappa for individual sequences: T2WI, 0.46 and 0.49; DWI, 0.66 and 0.70; DCE, 0.71 and 0.61). Quality levels shifted from predominantly "optimal" in v1 (65%) down to "acceptable" using v2 (55%); p < 0.001. The addition of DCE increased the proportion of cases with at least "adequate" quality at mpMRI (64%) compared to bpMRI (30%); p < 0.001. This study shows consistent inter-reader agreement between PI-QUAL v1 and v2, encompassing overall and individual sequence categorisation. A notable shift from "optimal" to "acceptable" quality was demonstrated when moving from v1 to v2, with DCE tending improving quality from "inadequate" (bpMRI) to "acceptable" (mpMRI). Question What are the agreement levels of image quality of prostate MRI by using PI-QUAL v1 and v2? Findings Inter-reader agreement based on PI-QUAL v1 and v2 is comparable. Dynamic contrast enhancement (DCE) enables an overall shift from inadequate quality (at bpMRI) to acceptable quality (mpMRI). Clinical relevance The inter-reader agreement on PI-QUAL v1 and v2 is equivalent. PI-QUAL v2 assesses prostate bpMRI as well as mpMRI quality. Transitioning from inadequate to acceptable between v2-bpMRI and v2-mpMRI highlights the role of DCE as an "image quality safety net."

Decreased Water Diffusivity Along the Perivascular Space in Older Adults With Poor Sleep Quality.

This study included 52 Japanese older adults with Pittsburgh Sleep Quality Index (PSQI) scores > 5 and 52 healthy controls (HCs) with PSQI score ≤ 5. Diffusion-weighted imaging (DWI) and 3D T1-weighted imaging were acquired using 3T magnetic resonance imaging. The diffusion tensor image analysis along the perivascular space (DTI-ALPS) index was calculated using preprocessed DWI. The choroid plexus volume (CPV) was calculated using FreeSurfer 6.0. The mean ALPS index and CPV were compared between the older adults with poor sleep quality (PSQ) and HCs using a general linear model, adjusted for covariates including age, sex, years of education, total intracranial volume, systolic blood pressure, hemoglobin A1c, and white matter lesion volume. We also conducted a partial correlation analysis between the mean ALPS index and CPV, Montreal Cognitive Assessment (MoCA), and PSQI scores, adjusting for all the mentioned covariates. The PSQ group had a significantly lower mean ALPS index than HCs. The mean ALPS index in the PSQ group was negatively correlated with CPV and positively correlated with the MoCA score. Therefore, older adults with PSQ may experience dysfunction in the excretory pathway of the perivascular space around the medullary veins. This impairment may be associated with an increase in CPV and cognitive dysfunction.

Retina and optic nerve diffusion restriction in acute central retinal artery occlusion: A case-control study.

To determine the frequency and accuracy of diffusion restriction (DR) of the retina and/or optic nerve (ON) detection on standard brain magnetic resonance diffusion-weighted imaging (DWI-MRI) in patients presenting with acute non-arteritic central retinal artery occlusion (CRAO). This is a retrospective case-control study that includes all consecutive patients presenting to our tertiary academic center from 2013-2021 with acute non-arteritic CRAO (cases) or acute ischemic stroke syndrome (controls, age and gender-matched) that had brain MRI performed within 14 days from symptom onset. Two neurology residents (junior and senior), a vascular neurologist, and two neuroradiologists, blinded to the site of CRAO, independently reviewed the brain MRIs to assess for the presence of retina and ON DR. The consensus agreement between the two neuroradiologists was used to perform sensitivity and specificity analyses and calculate inter-rater reliability (prevalence-adjusted bias-adjusted kappa coefficient). A total of 128 patients with acute non-arteritic CRAO (mean (SD) age 69 (14) years; 50% female; median time from CRAO to DW-MRI 2 days (IQR 1-5)) and 128 age and gender-matched controls with acute cerebral ischemia were included. After the neuroradiologist consensus, DR was correctly identified in the retina or ON in 51/128 (39.8%) CRAO cases, retina alone 27.3%, ON alone 24.2%, and both retina and ON 11.7%, with almost perfect neuroradiologists' inter-rater reliability for retina (K = 0.91) and ON (K = 0.83). Among controls, the retina DR was identified in 1/128 (0.8%) and ON DR in 5/128 (3.9%). The sensitivity, specificity, positive predictive value, and negative predictive value were 28.1%, 99.2%, 97.3%, and 58.0% for retina DR, and 27.3%, 96.1%, 87.5%, and 56.9% for ON DR. Though experienced neuroradiologists identified retina and ON DR with excellent inter-rater reliability, these are infrequent findings in real world CRAO practice, with excellent specificity but limited sensitivity. Prospective studies with larger cohort of patients, optimization of standardized orbit DWI-MRI protocols are needed to facilitate a more accurate and reliable identification of retina and ON DR in acute CRAO.

Multiparametric Magnetic Resonance Imaging Assessment of Primary Tumors for Predicting Axillary Tumor Burden in Women with Invasive Breast Cancer.

To assess the association between multiparametric MRI features of primary tumors and axillary lymph node tumor burden in women with invasive breast cancer. In this retrospective study, women diagnosed with invasive breast cancer who underwent 3T multiparametric MRI, including diffusion-weighted imaging (DWI) from 2019-2020, were evaluated. Two radiologists reviewed T2-weighted images (T2WI) for peritumoral edema and intratumoral necrosis, and measured apparent diffusion coefficient (ADC) values by manually placing regions of interest within breast tumors. We also analyzed quantitative kinetic features of breast cancer using computer-aided diagnosis (CAD) and clinical-pathologic characteristics. Uni- and multivariable logistic regression analyses were conducted to identify predictors of a high axillary nodal burden (≥3 positive nodes). In total, 301 women (mean age, 54.13 years) were evaluated. Forty-three (14.3%) had a high axillary nodal burden by surgical pathology. Multivariate analysis revealed that factors significantly associated with high axillary nodal burden included peritumoral edema (OR:7.970; p<0.001), lower tumor ADCmax (≤ 1.098 × 10-3 mm2/s) (OR:6.978; p<0.001), larger tumor size (>2cm) (OR:2.986; p=0.046), lobular histology (OR:12.620; p<0.001), and the presence of lymphovascular invasion (OR:3.622; p=0.003). CAD-derived kinetic features did not show an association with axillary nodal burden. In subgroup analysis of 238 patients with early clinically node-negative breast cancer, both peritumoral edema (OR:7.831; p=0.002) and lower tumor ADCmax (≤ 1.098 × 10-3 mm2/s) (OR:8.002; p=0.002) remained significant predictors. Our results suggest that peritumoral edema as viewed in T2WI and the ADCmax value of breast cancer in DWI are valuable for predicting axillary nodal burden in women with invasive breast cancer. Multiparametric MRI features of a primary tumor are useful for predicting axillary nodal burden in patients with invasive breast cancer.

Intravoxel incoherent motion imaging in stroke infarct core and penumbra is related to long-term clinical outcome

Intravoxel incoherent motion (IVIM) imaging, a contrast agent-free magnetic resonance imaging technique, enables the evaluation of microvascular perfusion abnormalities in acute stroke. Prior research reported reduced IVIM values within the infarct core in acute stroke. However, findings concerning IVIM characteristics in the penumbra have been mixed and the relationship between IVIM and clinical outcomes remains unknown. We employed a longitudinal multimodal imaging approach for ischemic stroke patients (n analyzed=24; pre-/post-treatment and 90-day post-stroke assessments) including IVIM, diffusion-weighted, and contrast-enhanced perfusion-weighted imaging. We evaluated IVIM in relevant stroke areas after endovascular treatment. Reduced post-treatment IVIM perfusion fraction in infarct core and recanalized penumbra was associated with poorer functional recovery at 90-days post-stroke (NIH Stroke Scale [NIHSS]; r=-0.64 and r=-0.69). Including IVIM perfusion fraction increased the explained variance of NIHSS from 42% up to 83% compared to well-known prognostic factors core volume and patient age. Additionally, IVIM perfusion fraction was reduced in the core and recanalized penumbra compared to contralateral healthy tissue, suggesting impaired microvascular reperfusion after endovascular treatment. In conclusion, IVIM characteristics of the infarct core and recanalized penumbra are strong prognostic factors for long-term outcome in stroke patients and IVIM shows promise for characterizing microvascular perfusion in relevant stroke areas.

Long-Term Risk of Clinically Significant Prostate Cancer in Biopsy-Negative Patients With Baseline Biparametric Prostate MRI.

The long-term prevalence of clinically significant prostate cancer (csPCa) in patients with initial negative prostate biopsy is unknown. To investigate the rate of csPCa of men with initial negative biopsy. Retrospective analysis of prospectively collected data. A total of 197 men (mean age 63 years [SD ±6.98, range 29-79]) without csPCa on initial biopsy and available baseline biparametric prostate MRI (bpMRI). 3.0 T, turbo spin-echo T2-weighted (axial and sagittal) and three sets of diffusion-weighted imaging using single-shot spin-echo planar imaging (5 b-values 0-500 seconds/mm2; 2 b-values 0 and 1500 seconds/mm2, and 2 b-values 0 and 2000 seconds/mm2). BpMRI was read using Prostate Imaging Reporting Data System (PI-RADS) v2.1. Systematic or targeted biopsy results served as reference standard. Continuous variables were compared using Kruskal-Wallis rank sum test. Categorical variables were compared using either Fisher's exact test or Pearson's chi-square test. Uni- and multivariate regression odds ratios (95% confidence interval) were used to study factors affecting csPCa being diagnosed during follow-up. Time to diagnosis of csPCa is calculated using the Kaplan-Meier method. Of 197 men, 74 (38%), 57 (29%), and 66 (34%) presented with PI-RADS 1-2, 3, and 4-5 findings in the baseline bpMRI. During the median follow-up of 52 months, 8.1%, 5.3%, and 18.2% of these men were diagnosed with csPCa, respectively. Baseline PI-RADS finding was the only factor that associated with csPCa found during the follow-up. Baseline bpMRI with PI-RADS scores 1-3 and initial biopsies negative of csPCa had low rate of csPCa during follow-up, which supports more conservative follow-up for them but further research with longer follow-up is warranted. 3 TECHNICAL EFFICACY: Stage 2.

Relationship Between DWI-Based Acute Ischemic Stroke Volume, Location and Severity of Dysphagia

Background/Objectives: The impact of stroke location and volume on the development of post-stroke dysphagia is not fully understood. The aim of this study is to evaluate the relationship between acute ischemic lesions and the severity of dysphagia. Methods: Brain MRIs were obtained with a 1.5 Tesla MRI system (Magnetom Avanto B13, Siemens, Erlangen, Germany). The brain MRI protocol included axial echo planar diffusion-weighted imaging (DWI). The acute ischemic volume was obtained using DWI by drawing regions of interest (ROIs). The diagnosis and assessment of the severity of dysphagia was carried out by a multidisciplinary team and included the Dysphagia Outcome and Severity Scale (DOSS), the Penetration–Aspiration Scale (PAS), and the Pooling score (P-score). The threshold for statistical significance was set at 5%. Results: Among all the patients enrolled (n = 64), 28 (43.8%) were males and 36 (56.2%) were females, with a mean age of 78.8 years. Thirty-three (51.6%) of them had mild dysphagia and thirty-one (48.4%) had moderate–severe dysphagia. The total ischemic volume was negatively correlated with the DOSS (r = −0.441, p = 0.0003) and positively with the P-score (rs = 0.3054, p = 0.0328). Conclusions: There are significant associations between the severity of dysphagia and the quantitative DWI-based data of the acute ischemic volume and anatomical location.

The value of amide proton transfer imaging in predicting parametrial invasion and lymph-vascular space invasion of cervical cancer

ObjectiveTo explore the value of amide proton transfer (APT) imaging in assessing parametrial invasion (PMI) and lymph-vascular space invasion (LVSI) of cervical cancer. Materials and methodsWe retrospectively analyzed the clinical and imaging data of cervical cancer patients diagnosed pathologically at our hospital from January 2021 to June 2024. All patients underwent routine magnetic resonance imaging (MRI), diffusion-weighted imaging (DWI), and APT imaging before treatment. Apparent diffusion coefficient (ADC) and APT values were measured. Based on the pathological results, patients were categorized into LVSI (+) and LVSI (−) groups, and PMI (+) and PMI (−) groups. Independent sample t-tests were used to compare the ADC and APT values between these groups. Receiver operating characteristic (ROC) curves were used to assess the sensitivity, specificity, and area under the curve (AUC) of ADC, APT, and ADC + APT in predicting PMI and LVSI. The Delong test was employed to compare the diagnostic performance among these measures. ResultsA total of 83 patients were included, with 56 in the LVSI (−) group, 27 in the LVSI (+) group, 35 in the PMI (−) group, and 16 in the PMI (+) group. The ADC values for the LVSI (+) and PMI (+) groups were significantly lower than those for the LVSI (−) and PMI (−) groups (P < 0.01). The APT values for the LVSI (+) and PMI (+) groups were significantly higher than those for the LVSI (−) and PMI (−) groups (P < 0.01). The AUC values for ADC, APT, and the combination of ADC + APT in predicting LVSI were 0.839, 0.788, and 0.880, respectively, and in predicting PMI were 0.770, 0.764, and 0.796, respectively. There were no statistically significant differences in the diagnostic performance of ADC, APT, and ADC + APT in predicting PMI. However, the diagnostic performance of ADC + APT in predicting LVSI was significantly better than that of ADC and APT alone (P < 0.01). ConclusionAPT imaging can predict LVSI and PMI status in cervical cancer before surgery. When combined with ADC, its diagnostic accuracy for predicting LVSI is higher than that of APT or ADC alone. This suggests a novel approach for assessing LVSI in cervical cancer.

Introducing µGUIDE for quantitative imaging via generalized uncertainty-driven inference using deep learning

This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.