BACKGROUND:Reduced field-of-view (rFOV) T2WI improves in-plane spatial resolution. Deep learning-based reconstruction (DLR) has emerged as enhancing image quality. We compared examination time, image quality, and lesion detection rates between pancreaticobiliary rFOV T2WI with and without DLR. METHODS:198 patients who underwent pancreaticobiliary rFOV T2WI were included. The protocol included rFOV T2WI with 2 NEX (rFOV T2WIN2) and 1 NEX (rFOV T2WIN1). DLR was applied to generate corresponding datasets: rFOV T2WIN2-DLR and rFOV T2WIN1-DLR datasets. Three observers evaluated the noise, respiratory motion artifacts (RMA), overall image quality (OIQ), and diagnostic confidence (DC). Two observers measured the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Image quality metrics were compared using either ANOVA or Friedman test. Lesion detection rates were tested using the Cochran's Q test and McNemar test. RESULTS:The noise, RMA, OIQ, and DC scores of rFOV T2WIN1-DLR were notably higher than those of rFOV T2WIN1. The noise, OIQ, and DC scores of rFOV T2WIN2 were notably higher than those of rFOV T2WIN1 (P < .001). The SNR and CNR of rFOV T2WI with DLR were notably higher than those of rFOV T2WI without DLR (P < .05). The rFOV T2WIN1-DLR sequence yielded a higher lesion detection rate (92.5%; 491/531) compared to rFOV T2WIN1 (76.5%; 406/531). CONCLUSIONS:Pancreaticobiliary rFOV T2WI with DLR is feasible and yields superior image quality compared to rFOV T2WI without DLR. A 37.1% to 74.4% reduction in acquisition time is achievable without increasing image noise or compromising overall image quality and lesion detection rate. RELEVANCE STATEMENT:The perfect balance of image quality, scanning time, and lesion detection rate can be achieved by using DLR in pancreaticobiliary rFOV T2WI.

To compare two region-of-interest (ROI) measurement methods based on mDIXON-Quant for distinguishing healthy volunteers (HV), chronic kidney disease (CKD) patients with low (≤ 15

To assess the performance of three-dimensional T2-weighted fast field echo imaging (3D-T2-FFE) in the visualization of the intraparotid facial nerve (IFN) and localization of tumors. Magnetic resonance imaging data from sixty-four patients who underwent 3D-T2-FFE (Time of repetition 8.30 ms, Time of echo 4.10 ms, Voxel 0.65 × 0.65 × 1.00 mm, Field of view 220 × 220 × 65 mm, Matrix 340 × 339 × 130, Number of signal average 2, Flip angle 30°) were retrospectively enrolled. Finally, 64 cases of tumors were included (including 55 benign tumors and 9 malignant tumors). The identification certainty of IFN on 3D-T2-FFE was scored with an arbitrary scale of 0–3. The parotid gland was divided into superior and inferior parts, with the level of the earlobe (approximately at the level of the external auditory canal). The tumor location was categorized as deep or superficial directly on 3D-T2-FFE images and indirectly by the facial nerve line (FNL) and the retromandibular vein line (RMVL). Surgical localization was considered the reference standard. The accuracy, sensitivity, and specificity of each method for localizing parotid lesions were compared using the McNemar test. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for deep lobe lesions in the superior part of the parotid gland using the direct method were 97.8

To evaluate the performance of intravoxel incoherent motion (IVIM) imaging with deep learning reconstruction (DLR) for prediction of lymph-vascular space invasion (LVSI) in cervical cancer (CC). Patients clinically suspected of having CC who underwent uterine MRI were retrospectively collected. IVIM images were collected with b values of 0, 20, 50, 100, 150, 200, 400, 800, 1500 and 2000 s/mm2. Subjective image quality, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), as well as apparent diffusion coefficient (ADC) values calculated at b = 0 and 800 s/mm² (ADC800), ADC values at b = 0 and 2000 s/mm² (ADC2000), and IVIM quantitative parameters were computed and compared between IVIM sequences with and without DLR. Quantitative parameters measured by the two sets of IVIM data were also compared between LVSI-positive and LVSI-negative groups. The study included 55 patients with 31/55 (56.36

Background:Histopathological grading is a key prognostic marker for hepatocellular carcinoma (HCC). However, the clinical application of deep learning models (DLMs) for predicting HCC grading from medical imaging is limited by their black-box nature. We aimed to develop an interpretable DLM, interpretable HCC grading network (iHCG-Net), to predict HCC grading preoperatively using multi-phase contrast-enhanced magnetic resonance imaging (CEMRI). Methods:This study retrospectively enrolled 370 HCC patients who underwent preoperative CEMRI before curative resection. Based on postoperative pathology, the patients were categorized into high-grade (n=136) and low-grade (n=234) HCC groups. They were then stratified into a training cohort (n=259) and a time-independent validation cohort (n=111). Twenty-three clinical-radiological features were collected for all patients. The iHCG-Net, based on the Concept Bottleneck Model (CBM) framework, first encodes CEMRI images using a DenseNet-121 backbone and then leverages a concept regressor to predict the twenty-three clinical-radiological features for final prediction of HCC histological grade. A feature importance score plot was generated to assess the contribution of each feature to the differential diagnosis. Nine baseline predictive models were developed for comparison. The models were evaluated using receiver operating characteristic (ROC) curve analysis and DeLong's test. Results:iHCG-Net demonstrated strong predictive performance for HCC grading, achieving areas under the receiver operating characteristic curve (AUCs) of 0.893 in the training cohort and 0.802 in the validation cohort. The model significantly outperformed conventional models, including the clinical-radiological model (CM), radiomics models (RMs), and a clinical-radiomic combined model (CRM) (AUCs: 0.675-0.778, 0.617-0.723; P<0.05). Furthermore, iHCG-Net exhibited performance comparable to that of the DLM (AUCs: 0.920, 0.774; P>0.05), while providing inherent interpretability and mitigating the risk of overfitting. Feature importance analysis identified intratumoral arteries as the most influential feature for predicting HCC grading, with an importance score of 0.213. Conclusions:The iHCG-Net can be a promising interpretable artificial intelligence tool for the preoperative prediction of HCC grading.

Early diagnosis of Alzheimer’s disease (AD) remains a significant challenge due to the lack of objective biomarkers. Accurate identification of morphological and metabolic alterations during the different stages of AD is crucial for timely intervention and effective management of the disease. Surface-based morphometry (SBM) and amide proton transfer (APT) imaging are innovative techniques that offer the potential to visualize these changes in the brain. By leveraging these advanced imaging modalities, this study aims to investigate the specific alterations that occur in AD, thereby exploring potential imaging biomarkers that could facilitate early and precise diagnosis of the condition. The identification of such biomarkers is essential for improving the diagnostic accuracy and efficacy of therapeutic strategies for Alzheimer’s disease. In this prospective study, we enrolled 26 patients with AD, thirty-six patients with amnestic mild cognitive impairment (aMCI), and 32 healthy controls (HCs). All participants underwent 3D T1-weighted imaging (T1WI) and 3D-APT imaging. Morphological parameters, including cortical thickness (CTh), sulcal depth (SD), fractal dimension (FD), and gyrification index (GI), were calculated using the SBM algorithm. Magnetization transfer ratio asymmetry (MTRasym) values were computed for 106 brain regions utilizing the vendor’s post-processing workstation. AD Patients exhibited cortical thinning in the frontal, temporal, parietal lobes, and cingulate gyrus, along with a decrease in GI in the left parietal lobe. MCI patients showed a reduction in GI in the left parietal and temporal lobes. Multivariate logistic regression analysis identified widespread increased MTRasym values in AD patients, affecting the frontal, temporal, parietal, occipital, cingulate, basal ganglia regions, and white matter. The right amygdala shows the highest diagnostic performance. Additionally, a negative correlation was observed between MTRasym values and clinical assessments. SBM analysis revealed morphological changes in the brain across different stages of AD, while APT imaging identified metabolic alterations in AD patients. These findings suggest that the combination of SBM and APT imaging may hold potential as a non-invasive diagnostic and monitoring approach for AD.

BACKGROUND:Suicide risk remains high in middle-aged and older adults with major depressive disorder (MDD). Dysregulation of cortisol (CORT) and neuroinflammation may impair glymphatic clearance, but their effects in this population are unclear. METHODS:Diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index, choroid plexus volume (CPV), free water (FW) in white matter, and enlarged perivascular space (EPVS) grading of 20 MDD patients with elevated CORT (high-CORT), 39 MDD with normal CORT levels (normal-CORT), and 29 healthy controls (HCs) were compared. Inflammatory indices included the neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), aggregate index of systemic inflammation (AISI), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR). MRI parameters were also correlated with inflammatory indices and neuropsychological scales. RESULTS:After controlling for age, gender, ACTH and HAMD-sleep disturbance, high-CORT group exhibited the lowest DTI-ALPS index (p < 0.01), significantly higher CPV (p = 0.04) compared to HCs, and tended to be higher BG EPVS grading compared to normal-CORT (p = 0.02). The DTI-ALPS index was negatively correlated with depression severity and HAMD-cognitive impairment (r ranging from -0.437 to -0.300; p ranging from 0.001 to 0.031). Moreover, the DTI-ALPS index was negatively associated with NLR (r = -0.277, p = 0.047). Additionally, CPV was positively correlated with multiple inflammatory indices (r ranging from 0.380 to 0.595, all p ≤ 0.01). CONCLUSION:Significant differences in MRI-based markers of brain metabolic waste clearance were observed among middle-aged and older MDD patients with varying cortisol levels and HCs. These alterations were linked to depressive symptoms, cognitive impairment, and inflammation markers.

PurposeThis study aims to evaluate the efficacy of utilizing automated intertumoral susceptibility signal (ITSS) intensity extraction combined with R2* values derived from enhanced T2*-weighted angiography (ESWAN) in magnetic resonance imaging (MRI) to distinguish between cervical adenocarcinoma (CA) and cervical squamous carcinoma (CSC).MethodsSeventy-eight patients who underwent ESWAN from 2014 to 2019 were stratified into two groups: CA (26 patients) and CSC (52 patients). R2* values of the lesions were measured, and ITSS ratios were automatically calculated using the Anatomy Sketch (AS) software. Independent samples t-tests or Mann-Whitney U-tests were utilized to evaluate disparities in the parameters. Binary logistic regression was conducted to identify independent predictors. The receiver operating characteristic curve was employed to assess diagnostic value, and the Delong test was applied to compare differences in the area under the curve (AUC).ResultsThe CA group exhibited significantly higher values for the ITSSs, ITSSv and R2* value, lower alpha fetoprotein (AFP) and prognostic nutritional index (PNI) (ITSSs: 0.203 ± 0.111; ITSSv:0.206 ± 0.098; R2* value:20.340 ± 5.572Hz; AFP: 1.73(1.33,2.99)ng/ml; PNI:49.150(45.825,51.775)) than that of the CSC group (ITSSs: 0.072 ± 0.019; ITSSv: 0.076 ± 0.030; R2* value: 13.233 ± 4.083Hz; AFP: 2.99(1.88,2.99)ng/ml; PNI: 50.775(48.563,54.050)) (P< 0.05). Among them, ITSSv and R2* value were independent risk predictors. The AUC values for ITSSv, R2* value and the combined model for differentiate between CA and CSC were 0.942, 0.851 and 0.950, respectively. The results of the Delong test indicated that the combined model exhibited superior diagnostic efficacy compared to R2* value (P< 0.05), but no significant difference from ITSSv (P>0.05).ConclusionITSSv and R2* values derived from ESWAN facilitate the quantitative differentiate between CA and CSC. The automated extraction of ITSSv is convenient and reliable, making it a promising candidate for clinical implementation.

Amide proton transfer (APT) imaging indirectly reflects tissue metabolic changes by detecting variations in the concentration of mobile amide protons and tissue pH. Type 2 diabetes mellitus (T2DM) is often accompanied by cognitive dysfunction and diabetic encephalopathy, both of which pose serious threat to human health and quality of life. This study aimed to evaluate the potential of APT imaging as a novel biomarker for detecting cerebral metabolic alterations and to investigate its associations with cognitive impairment in patients with T2DM. This study included 32 T2DM patients, comprising 16 with mild cognitive impairment (MCI) and 16 with normal cognition (NC), and 26 healthy controls. Clinical data and cognitive assessments were collected within one week of MRI acquisition. Imaging markers of cerebral small vessel disease (CSVD) were evaluated using AI-assisted tools. APT values were measured in predefined brain regions, including the hippocampus (hipp), temporal white matter (TWM), temporal gray matter (TGM), occipital white matter (OWM), occipital gray matter (OGM), and cerebral peduncles (CPs) using 3D Slicer software. Group differences were analyzed with one-way ANOVA followed by Bonferroni-corrected post hoc tests (or Kruskal–Wallis test with Bonferroni correction for non-parametric data). Partial correlations (Bonferroni-corrected) assessed the links between APT values and cognitive scores, as well as between APT values and CSVD imaging markers. The APT values of the left temporal white matter (TWM) and the right temporal gray matter (TGM) were significantly different among the three groups. Among them, the APT values of T2DM-MCI group were significantly lower. In T2DM patients, partial correlation analysis showed that the APT values of the left TWM was positively correlated with MMSE attention and calculation score, MoCA attention score, and the number of lacunar infarcts (LI), and negatively correlated with the severity of white matter hyperintensities (WMH). The APT values of right TGM was positively correlated with MoCA total scores, MoCA visuospatial scores and MoCA delayed recall scores. T2DM patients with mild cognitive impairment exhibited significantly lower APT values in the left temporal white matter and right temporal gray matter. These lower APT values were strongly associated with poorer cognitive performance and more severe CSVD. APT imaging may serve as a sensitive, noninvasive biomarker for detecting cerebral metabolic deterioration underlying diabetic cognitive decline.

Background:Magnetic resonance imaging (MRI) plays a crucial role in the diagnosis of abdominal conditions. A comprehensive assessment, especially of the liver, requires multi-planar T2-weighted sequences. To mitigate the effect of respiratory motion on image quality, the combination of acquisition and reconstruction with motion suppression (ARMS) and respiratory triggering (RT) is commonly employed. While this method maintains image quality, it does so at the expense of longer acquisition times. We evaluated the effectiveness of free-breathing, artificial intelligence-assisted compressed-sensing respiratory-triggered T2-weighted imaging (ACS-RT T2WI) compared to conventional acquisition and reconstruction with motion-suppression respiratory-triggered T2-weighted imaging (ARMS-RT T2WI) in abdominal MRI, assessing both qualitative and quantitative measures of image quality and lesion detection. Methods:In this retrospective study, 334 patients with upper abdominal discomfort were examined on a 3.0T MRI system. Each patient underwent both ARMS-RT T2WI and ACS-RT T2WI. Image quality was analyzed by two independent readers using a five-point Likert scale. The quantitative measurements included the signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), and sharpness. Lesion detection rates and contrast ratios (CRs) were also evaluated for liver, biliary system, and pancreatic lesions. Results:There ACS-RT T2WI protocol had a significantly reduced median scanning time compared to the ARMS-RT T2WI protocol (148.22±38.37 vs. 13.86±1.72 seconds). However, ARMS-RT T2WI had a higher PSNR than ACS-RT T2WI (39.87±2.72 vs. 38.69±3.00, P<0.05). Of the 201 liver lesions, ARMS-RT T2WI detected 193 (96.0%) and ACS-RT T2WI detected 192 (95.5%) (P=0.787). Of the 97 biliary system lesions, ARMS-RT T2WI detected 92 (94.8%) and ACS-RT T2WI detected 94 (96.9%) (P=0.721). Of the 110 pancreatic lesions, ARMS-RT T2WI detected 102 (92.7%) and ACS-RT T2WI detected 104 (94.5%) (P=0.784). The CR analysis showed the superior performance of ACS-RT T2WI in certain lesion types (hemangioma, 0.58±0.11 vs. 0.55±0.12; biliary tumor, 0.47±0.09 vs. 0.38±0.09; pancreatic cystic lesions, 0.59±0.12 vs. 0.48±0.14; pancreatic cancer, 0.48±0.18 vs. 0.43±0.17), but no significant difference was found in others like focal nodular hyperplasia (FNH), hepatapostema, hepatocellular carcinoma (HCC), cholangiocarcinoma, metastatic tumors, and biliary calculus. Conclusions:ACS-RT T2WI ensures clinical reliability with a substantial scan time reduction (>80%). Despite minor losses in detail and SNR reduction, ACS-RT T2WI does not impair lesion detection, marking its efficacy in abdominal imaging.

Background No study has assessed the application of deep learning (DL) networks in combination with more advanced super-resolution (SR) reconstruction for endometrial cancer (EC) MRI and the influence of this methods on myometrial invasion. We aimed to evaluate the performance and clinical value of DL-based SR reconstruction for image quality improvement in under-sampling accelerated EC T2WI. Methods Two sets of T2WI were acquired for 23 volunteers with low (T2 LR ) and high (T2 HR ) resolution, respectively. For 41 EC patients, T2 LR and MultiVane T2WI (T2 MV ) were performed. T2 SR images were reconstructed from T2 LR through DL network. Image quality and diagnostic accuracy were evaluated by radiologists. Results For 23 volunteers, the signal-to-noise ratio and peak signal-to-noise ratio of T2 SR were notably higher than those of T2 LR and T2 HR , and the edge rise distance of T2 SR was lower ( P < 0.005). For 41 EC patients, the image sharpness, capsule delineation, overall image quality, and diagnostic confidence of T2 SR and T2 MV were notably higher than those of T2 LR ( P < 0.005). Compared with T2 MV , the scanning time of T2 SR was reduced by 61% (50s vs. 128s). T2 SR and T2 MV images had similar diagnostic accuracy for myometrial invasion assessed by radiologists. Conclusions DL-based super-resolution reconstruction significantly improved the image quality of uterus T2WI compared to the conventional iterative reconstruction by compressed sensing, saving scanning time and ensuring the accurate evaluation of myometrial invasion of endometrial cancer.

[This corrects the article DOI: 10.3389/fonc.2025.1556311.].

BACKGROUND AND PURPOSE:DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion visualization. Our aim was to evaluate the image quality and diagnostic confidence of deep learning (DL)-based super-resolution reconstruction for brain DWI of infarction stroke. MATERIALS AND METHODS:This retrospective study enrolled 114 consecutive participants who underwent brain DWI. The DWI images were reconstructed with 2 schemes: 1) DL-based super-resolution reconstruction (DWIDL); and 2) conventional compressed sensing reconstruction (DWICS). Qualitative image analysis included overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke of different lesion sizes. Quantitative image quality assessments were performed by measurements of SNR, contrast-to-noise ratio (CNR), ADC, and edge rise distance. Group comparisons were conducted by using a paired t test for normally distributed data and the Wilcoxon test for non-normally distributed data. The overall agreement between readers for qualitative ratings was assessed by using the Cohen κ coefficient. A P value less than .05 was considered statistically significant. RESULTS:A total of 114 DWI examinations constituted the study cohort. For the qualitative assessment, overall image quality, lesion conspicuity, and diagnostic confidence in infarction stroke lesions (lesion size <1.5 cm) improved by DWIDL compared with DWICS (all P < .001). For the quantitative analysis, edge rise distance of DWIDL was reduced compared with that of DWICS (P < .001), and no significant difference in SNR, CNR, and ADC values (all P > .05). CONCLUSIONS:Compared with the conventional compressed sensing reconstruction, the DL-based super-resolution reconstruction demonstrated superior image quality and was feasible for achieving higher diagnostic confidence in infarction stroke.

This study aimed to evaluate changes in gray matter nuclei iron deposition in chronic kidney disease (CKD) patients using the quantitative susceptibility mapping (QSM) threshold method, and analyze the relationship between brain iron levels and cognitive function. A total of fifty-three CKD patients were prospectively recruited, comprising 35 hemodialysis (HD, 57.54 ± 10.42 years, 21 males) and 18 non-hemodialysis (NHD, 55.06 ± 11.47 years, 10 males ), and were compared to 43 healthy controls (HC, 55.67 ± 7.79 years, 18 males). All participants underwent clinical assessments, neuropsychological tests, and QSM scans. The mean magnetic susceptibility value (MSV) and volume of the whole nuclei (MSVM, VM) and high iron region (MSVRII, VRII) were measured. Correlations between QSM data, neuropsychological scores, and clinical variables in HD group were analyzed. Linear regression analysis was performed to explore the effect of iron deposition on cognition and emotional well-being in HD group. A statistically significant P-value was set at 0.05. HD patients exhibited higher MSVM in the right red nucleus (RN) compared to HCs (P = 0.006). Additionally, significant differences in the MSVRII were observed in the left caudate nucleus (CN), bilateral putamen (Put), and right RN among the three groups (all P = 0.027, FDR-corrected). MSVRII of the left Put was positively correlated with creatinine and uric acid levels, while the MSVRII of the right Put was negatively correlated with mean corpuscular hemoglobin and mean corpuscular hemoglobin concentration. Regression analysis revealed that iron deposition in left CN was independently associated with depression, while iron deposition in left Put and right RN were independently positively associated with delayed recall performance. Conversely, iron deposition in bilateral Put and right RN were negatively associated with orientation ability, after controlling for age, sex, years of education and duration of dialysis. Brain iron deposition is often excessive and uneven in CKD patients, particularly those undergoing hemodialysis. Assessing regional high-iron deposition can provide valuable insights into the distribution of iron, which is associated with cognitive dysfunction and emotional disorders.

Objective:To observe the hemodynamics of intracranial arteries and veins in patients with cerebral small vessel disease (CSVD) with cognitive impairment (CI), and to explore the association between these flow features and white matter hyperintensities (WMH). Materials and methods:A total of 53 patients with CSVD were included in the study, comprising 30 patients with CI (CI group) and 23 patients with non-CI (NCI group); Meanwhile, 25 age-matched cognitively healthy volunteers were recruited. WMH burden was evaluated using a 2D axial T2-FLAIR sequence. A 4D flow MRI was employed to measure intracranial hemodynamic features, including cross-sectional area, flow rate, blood flow velocity, wall shear stress (WSS), pulsatility index, and resistive index in the internal carotid artery (ICA), middle cerebral artery, basilar artery (BA), transverse sinus (TS), straight sinus (SS), and superior sagittal sinus (SSS). CSF-Q flow, a 2D PC MRI sequence, was performed to calculate the CSF fluid dynamics in the midbrain aqueduct. Results:The CSVD with CI population reported a statistically significant decrease in flow rate, blood flow velocity, and WSS, as well as an increase in PI, RI, CSF flow quantity, and velocity compared to age-matched cognitively healthy control participants. There was a moderately positive correlation between MMSE, MoCA score and flow rate, flow velocity, and WSS (r = 0.226-0.544, all P < 0.05), and a moderately negative correlation between MMSE, MoCA score and PI, RI (r = -0.230 to -0.406, all P < 0.05). Multiple linear regression indicated that, the flow rate and mean velocity in venous sinuses (β = -0.472 to -0.381, all P < 0.05) and the WSS in arterial segments (β = -0.771 to -0.441, all P < 0.05) had independently negative association with WMH burden; Meanwhile, a significant positive relationship was found between PI in arterial segments and specific-distributed WMH (PVWMH and S-CC WMH) (β = 0.239 to 0.356, all P < 0.05). Conclusion:The intracranial hemodynamics were associated with CI and WMH in patients with CSVD. 4D flow MRI can be used as a non-invasive method to assess cerebrovascular hemodynamics and helps to identify patients who may benefit from interventions to improve the functions of the cerebral circulatory system and provides a potential new path for clinical treatment.

BackgroundHypoxia inducible factor (HIF-1α) is a major transcriptional factor regulating gene expression under hypoxic conditions. HIF-1α expression was closely correlated with the oxygenation status of tumor and could serve as an important biomarker for tumor hypoxia, aggressiveness, or radiation resistance. High expression of HIF-1α contributes to high aggressiveness or poor prognosis of endometrial cancer.PurposeThis study aimed to investigate correlations between multimodal MRI parameters (derived from amide proton transfer weighted imaging [APTw], conventional diffusion weighted imaging [DWI], intravoxel incoherent motion [IVIM] imaging and diffusion kurtosis imaging [DKI]) and HIF-1α expression, and to determine whether multimodal MRI can be used for quantitative evaluation of HIF-1α expression.Study typeRetrospective.PopulationA total of 94 patients with EC were examined with 32 cases finally included in the high HIF-1α expression group and 40 cases included in the low expression group according to the exclusion and inclusion criteria.Field Strength/Sequence3.0T/APTw, DWI, IVIM, and DKIAssessmentThe asymmetry of magnetization transfer rate (MTRasym), apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo diffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean diffusivity (MD) were calculated from multimodal MRI and compared between HIF-1α high expression and HIF-1α low expression groups.Statistical TestMann–Whitney U-test; Chi-square test or Fisher exact test; logistic regression analysis; Area under the receiver operating characteristic (ROC) curve (AUC); The Delong test; Pearson or Spearman correlation coefficients. The significance threshold was set at P < 0.05.ResultMTRasym, ADC, D, D*, MK and MD values were significantly higher in high HIF-1α expression than in low HIF-1α expression groups, whereas f value was significantly lower in high HIF-1α expression than in low HIF-1α expression groups. The AUC of HIF-1 α expression evaluated by MTRasym, ADC, D, D*, f, MD, MK and their combination were 0.894 (0.740, 0.973), 0.746 (0.568, 0.879), 0.716 (0.528, 0.904), 0.920 (0.772, 0.984), 0.756 (0.578, 0.886), and 0.973 (0.851-1.000), respectively. Multivariate analysis revealed that only f, MK, and MD values were independent predictors for evaluating HIF-1α expression in EC.ConclusionAPTw combined with multi-model diffusion imaging can quantitatively evaluate the expression of HIF-1α in EC, and the combination of multiple quantitative parameters can improve the evaluation efficiency.

BackgroundChronic kidney disease (CKD) is associated with increased, and early cardiovascular disease risk. Changes in hemodynamics within the left ventricle (LV) respond to cardiac remodeling. The LV hemodynamics in nondialysis CKD patients are not clearly understood.PurposeTo use four‐dimensional blood flow MRI (4D flow MRI) to explore changes in LV kinetic energy (KE) and the relationship between LV KE and LV remodeling in CKD patients.Study TypeRetrospective.Population98 predialysis CKD patients (Stage 3: n = 21, stage 4: n = 21, and stage 5: n = 56) and 16 age‐ and sex‐matched healthy controls.Field Strength/Sequence3.0 T/balanced steady‐state free precession (SSFP) cine sequence, 4D flow MRI with a fast field echo sequence, T1 mapping with a modified Look–Locker SSFP sequence, and T2 mapping with a gradient recalled and spin echo sequence.AssessmentDemographic characteristics (age, sex, height, weight, blood pressure, heart rate, aortic regurgitation, and mitral regurgitation) and laboratory data (eGFR, Creatinine, hemoglobin, ferritin, transferrin saturation, potassium, and carbon dioxide bonding capacity) were extracted from patient records. Myocardial T1, T2, LV ejection fraction, end diastolic volume (EDV), end systolic volume, LV flow components (direct flow, delayed ejection, retained inflow, and residual volume) and KE parameters (peak systolic, systolic, diastolic, peak E‐wave, peak A‐wave, E/A ratio, and global) were assessed. The KE parameters were normalized to EDV (KEiEDV). Parameters were compared between disease stage in CKD patients, and between CKD patients and healthy controls.Statistical TestsDifferences in clinical and imaging parameters between groups were compared using one‐way ANOVA, Kruskal Walls and Mann–Whitney U tests, chi‐square test, and Fisher's exact test. Pearson or Spearman's correlation coefficients and multiple linear regression analysis were used to compare the correlation between LV KE and other clinical and functional parameters. A P‐value of <0.05 was considered significant.ResultsCompared with healthy controls, peak systolic (24.76 ± 5.40 μJ/mL vs. 31.86 ± 13.18 μJ/mL), systolic (11.62 ± 2.29 μJ/mL vs. 15.27 ± 5.10 μJ/mL), diastolic (7.95 ± 1.92 μJ/mL vs. 13.33 ± 5.15 μJ/mL), peak A‐wave (15.95 ± 4.86 μJ/mL vs. 31.98 ± 14.51 μJ/mL), and global KEiEDV (9.40 ± 1.64 μJ/mL vs. 14.02 ± 4.14 μJ/mL) were significantly increased and the KEiEDV E/A ratio (1.16 ± 0.67 vs. 0.69 ± 0.53) was significantly decreased in CKD patients. As the CKD stage progressed, both diastolic KEiEDV (10.45 ± 4.30 μJ/mL vs. 12.28 ± 4.85 μJ/mL vs. 14.80 ± 5.06 μJ/mL) and peak E‐wave KEiEDV (15.30 ± 7.06 μJ/mL vs. 14.69 ± 8.20 μJ/mL vs. 19.33 ± 8.29 μJ/mL) increased significantly. In multiple regression analysis, global KEiEDV (β* = 0.505; β* = 0.328), and proportion of direct flow (β* = −0.376; β* = −0.410) demonstrated an independent association with T1 and T2 times.Data Conclusion4D flow MRI‐derived LV KE parameters show altered LV adaptations in CKD patients and correlate independently with T1 and T2 mapping that may represent myocardial fibrosis and edema.Level of Evidence4.Technical EfficacyStage 3.

Background:The link between glymphatic system function in the brain and alterations in white-matter microstructure among individuals with major depressive disorder (MDD) remains unclear. This study aimed to examine the assessment of glymphatic system function in patients with MDD using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index and to evaluate its association with cerebral-white-matter abnormalities and neuropsychological scores. Methods:From February 2023 to November 2023, this cross-sectional study recruited 35 patients with MDD from the Psychosomatic Diseases Department of the First Affiliated Hospital of Dalian Medical University. In this time period, 23 healthy controls (HCs) were enlisted from the community and matched with the MDD cohort in terms of years of education, gender, and age. All participants underwent magnetic resonance imaging, depression, anxiety, and cognitive assessments. The tract-based spatial statistics (TBSS) analyzed DTI parameters and identified significant clusters. Automated fiber quantification (AFQ) was used to automatically identify fiber bundles with statistical differences. Mann-Whitney tests or two-sample t-tests were used for comparisons. Interobserver consistency of the DTI-ALPS measurements was evaluated using the interclass correlation coefficient (ICC). Partial correlation analyses and linear regression analyses were used to examine relationships. A comparison of the DTI-ALPS index was made between the two groups. Correlations among diffusion characteristics, neuropsychological scores, and the DTI-ALPS index were analyzed. Results:Compared to HCs, patients with MDD exhibited a lower DTI-ALPS score (P=0.001). According to using linear regression analysis, the ALPS index was found to be an independent predictor of the Hamilton Depression Rating Scale [B=-25.32; P=0.001; 95% confidence interval (CI): -40.35 to -11.55], Hamilton Anxiety Rating Scale (B=-33.48; P=0.003; 95% CI: -55.38 to -11.24), and Montreal Cognitive Assessment total score (B=8.59; P=0.008; 95% CI: 2.38 to 14.79). According to the TBSS analysis, there were clusters of increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) in patients with MDD as compared to HCs (all P values <0.05). A lower DTI-ALPS score was correlated with higher AD (r=-0.592; P<0.001), MD (cluster 1: r=-0.567, P=0.001; cluster 2: r=-0.581, P<0.001), and RD (r=-0.491; P=0.004) values. AFQ analysis identified the significantly different diffusion indicators in the left cingulum bundle (CB_L), left inferior longitudinal fasciculus (ILF_L), and left uncinate fasciculus (UF_L) between the two groups (all false discovery rate P values <0.05). DTI-ALPS score was negatively correlated with the AD value of CB_L (r=-0.304; P=0.024), ILF_L (r=-0.35; P=0.008), and UF_L (r=-0.354; P=0.008) in AFQ tract-level analysis. In point-wise analysis, the MD value of CB_L at nodes 33 to 36 was negatively correlated with DTI-ALPS score (r ranging from -0.504 to -0.535; P<0.01). Conclusions:Our results indicated a decrease in DTI-ALPS index score in patients with MDD. DTI-ALPS score was associated with depression, anxiety, declined cognitive ability, and white-matter microstructural abnormalities and may thus be a promising biomarker for the partial evaluation of glymphatic system function in patients with MDD.

BackgroundsType 2 Diabetes Mellitus (T2DM) has become a significant global public health issue, characterized by a rising prevalence and associated deficits across multiple organ systems. Our study aims to utilize the DTI-ALPS technique to assess the change of ALPS index in T2DM patients, and to explore whether such changes are correlated with cognition level and diffusion parameters.MethodsThe study involved 41 patients with T2DM (mean age, 60.49 ± 8.88 years) and 27 healthy controls (mean age, 58.00 ± 7.63 years). All subjects underwent MRI examination, cognitive assessment, and laboratory tests. Tract-based spatial statistics (TBSS) was used to evaluate white matter changes. GLM was performed to check the DTI-ALPS index difference between T2DM and HC groups. Spearman correlation analysis and partial correlation analysis were used to analyze the correlation between the DTI-ALPS index and diffusion properties & cognitive scores.ResultsThe results show that the ALPS index was lower in T2DM patients. MoCA score was significantly correlated with the ALPS index. Patients with T2DM had a significant increase in both mean diffusivity (MD) and radial diffusivity (RD) and decrease in fractional anisotropy (FA) compared to the HC group.ConclusionThe results suggest that the ALPS index is decreased in T2DM patients and associates with cognitive level.

OBJECTIVES:To find the optimal acceleration factor (AF) of the compressed SENSE (CS) technique for uterine isotropic high-resolution 3D T2-weighted imaging (3D-ISO-T2WI). METHODS:A total of 91 female volunteers from the First Affiliated Hospital of Dalian Medical University, Chu Hsien-I Memorial Hospital and Tianjin Institute of Endocrinology, and The Fourth Hospital of Harbin were recruited. A total of 44 volunteers received uterus sagittal 3D-ISO-T2WI scans on 3.0T MRI device with different CS AFs (including SENSE3, CS3, CS4, CS5, CS6, and CS7), 51 received 3D-ISO-T2WI scans with different degrees of fat suppression (none, light, moderate, and severe), while 4 volunteers received both series of scans. Image quality was subjectively evaluated with a 3-point scoring system. Junction zone signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and myometrial SNR were also calculated. Intraclass correlation coefficients were used to analyse the consistency of the measurement results by 2 observers. Analysis of variance test or Friedman rank sum test was used to compare the differences in subjective scores, SNR, and CNR under different AFs/different degrees of fat suppression. RESULTS:Images by AFs of CS3, CS4, and CS5 had the highest SNR and CNR. Among them, CS5 had the shortest scan time. CS5 also had one of the highest subjective scores. There was no significant difference in SNR and CNR among images acquired with different degrees of fat suppression. Also, images with moderate fat suppression had the highest subjective scores. CONCLUSION:The CS5 combined with moderate fat suppression is recommended for routine female pelvic 3D-ISO-T2WI scan. ADVANCES IN KNOWLEDGE:The CS5 has the highest image quality and has the shortest scan time, which is the best AF. Moderate fat suppression has the highest subjective scores. The CS5 and moderate fat suppression are the best combination for a female pelvic 3D-ISO-T2WI scan.