OBJECTIVE: The apparent diffusion coefficient (ADC) derived from diffusion-weighted MRI (DWI-MRI) has been proposed as a measure of changes in kidney microstructure, including kidney fibrosis. In advanced kidney disease, the kidneys often become atrophic; however, in the initial phase of type 2 diabetes, there is an increase in renal size. Glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter 2 inhibitors both provide protection against progression of kidney disease in diabetes. However, the mechanisms are incompletely understood. To explore this, we examined the effects of semaglutide, empagliflozin and their combination on renal ADC and total kidney volume (TKV).
METHODS: This was a substudy of a randomised clinical trial on the effects of semaglutide and empagliflozin alone or in combination. Eighty patients with type 2 diabetes and high risk of CVD were randomised into four groups (n=20 in each) receiving either tablet placebo, empagliflozin, a combination of semaglutide and tablet placebo (herein referred to as the \'semaglutide\' group), or the combination of semaglutide and empagliflozin (referred to as the \'combination-therapy\' group). The semaglutide and the combination-therapy group had semaglutide treatment for 16 weeks and then had either tablet placebo or empagliflozin added to the treatment, respectively, for a further 16 weeks; the placebo and empagliflozin groups were treated with the respective monotherapy for 32 weeks. We analysed the effects of treatment on changes in ADC (cortical, medullary and the cortico-medullary difference [ΔADC; medullary ADC subtracted from cortical ADC]), as well as TKV measured by MRI.
RESULTS: Both semaglutide and empagliflozin decreased cortical ADC significantly compared with placebo (semaglutide: -0.20×10-3 mm2/s [95% CI -0.30, -0.10], p<0.001; empagliflozin: -0.15×10-3 mm2/s [95% CI -0.26, -0.04], p=0.01). No significant change was observed in the combination-therapy group (-0.05×10-3 mm2/s [95%CI -0.15, 0.05]; p=0.29 vs placebo). The changes in cortical ADC were not associated with changes in GFR, albuminuria, TKV or markers of inflammation. Further, there were no changes in medullary ADC in any of the groups compared with placebo. Only treatment with semaglutide changed ΔADC significantly from placebo, showing a decrease of -0.13×10-3 mm2/s (95% CI -0.22, -0.04; p=0.01). Compared with placebo, TKV decreased by -3% (95% CI -5%, -0.3%; p=0.04), -3% (95% CI -5%, -0.4%; p=0.02) and -5% (95% CI -8%, -2%; p<0.001) in the semaglutide, empagliflozin and combination-therapy group, respectively. The changes in TKV were associated with changes in GFR, albuminuria and HbA1c.
CONCLUSIONS: In a population with type 2 diabetes and high risk of CVD, semaglutide and empagliflozin significantly reduced cortical ADC compared with placebo, indicating microstructural changes in the kidneys. These changes were not associated with changes in GFR, albuminuria or inflammation. Further, we found a decrease in TKV in all active treatment groups, which was possibly mediated by a reduction in hyperfiltration. Our findings suggest that DWI-MRI may serve as a promising tool for investigating the underlying mechanisms of medical interventions in individuals with type 2 diabetes but may reflect effects not related to fibrosis.
BACKGROUND: European Union Drug Regulating Authorities Clinical Trials Database (EudraCT) 2019-000781-38.

OBJECTIVE: To evaluate the amide proton transfer (APT), tumor blood flow (TBF), and apparent diffusion coefficient (ADC) combined diagnostic value for differentiating intracranial malignant tumors (MTs) from benign tumors (BTs) in young patients, as defined by the 2021 World Health Organization classification of central nervous system tumors.
METHODS: Fifteen patients with intracranial MTs and 10 patients with BTs aged 0-30 years underwent MRI with APT, pseudocontinuous arterial spin labeling (pCASL), and diffusion-weighted imaging. All tumors were evaluated through the use of histogram analysis and the Mann-Whitney U test to compare 10 parameters for each sequence between the groups. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis.
RESULTS: The APT maximum, mean, 10th, 25th, 50th, 75th, and 90th percentiles were significantly higher in MTs than in BTs; the TBF minimum (min) was significantly lower in MTs than in BTs; TBF kurtosis was significantly higher in MTs than in BTs; the ADC min, 10th, and 25th percentiles were significantly lower in MTs than in BTs (all p < 0.05). The APT 50th percentile (0.900), TBF min (0.813), and ADC min (0.900) had the highest area under the curve (AUC) values of the parameters in each sequence. The AUC for the combination of these three parameters was 0.933.
CONCLUSIONS: The combination of APT, TBF, and ADC evaluated through histogram analysis may be useful for differentiating intracranial MTs from BTs in young patients.

OBJECTIVE: To prospectively investigate the influence of the menstrual cycle on the background parenchymal signal (BPS) and apparent diffusion coefficient (ADC) of the breast on diffusion-weighted MRI (DW-MRI) in healthy premenopausal women.
METHODS: Seven healthy premenopausal women (median age, 37 years; range, 33-49 years) with regular menstrual cycles participated in this study. DW-MRI was performed during each of the four phases of the menstrual cycle (four examinations in total). Three radiologists independently assessed the BPS visual grade on images with b-values of 800 sec/mm² (b800), 1200 sec/mm² (b1200), and a synthetic 1500 sec/mm² (sb1500). Additionally, one radiologist conducted a quantitative analysis to measure the BPS volume (%) and ADC values of the BPS (ADCBPS) and fibroglandular tissue (ADCFGT). Changes in the visual grade, BPS volume (%), ADCBPS, and ADCFGT during the menstrual cycle were descriptively analyzed.
RESULTS: The visual grade of BPS in seven women varied from mild to marked on b800 and from minimal to moderate on b1200 and sb1500. As the b-value increased, the visual grade of BPS decreased. On b800 and sb1500, two of the seven volunteers showed the highest visual grade in the early follicular phase (EFP). On b1200, three of the seven volunteers showed the highest visual grades in EFP. The BPS volume (%) on b800 and b1200 showed the highest value in three of the six volunteers with dense breasts in EFP. Three of the seven volunteers showed the lowest ADCBPS in the EFP. Four of the seven volunteers showed the highest ADCBPS in the early luteal phase (ELP) and the lowest ADCFGT in the late follicular phase (LFP).
CONCLUSIONS: Most volunteers did not exhibit specific BPS patterns during their menstrual cycles. However, the highest BPS and lowest ADCBPS were more frequently observed in EFP than in the other menstrual cycle phases, whereas the highest ADCBPS was more common in ELP. The lowest ADCFGT was more frequent in LFP.

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget\'s disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.

OBJECTIVE: Further acceleration of DWI in diagnostic radiology is desired but challenging mainly due to low SNR in high b-value images and associated bias in quantitative ADC values. Deep learning-based reconstruction and denoising may provide a solution to address this challenge.
METHODS: The effects of SNR reduction on ADC bias and variability were investigated using a commercial diffusion phantom and numerical simulations. In the phantom, performance of different reconstruction methods, including conventional parallel (SENSE) imaging, compressed sensing (C-SENSE), and compressed SENSE acceleration with an artificial intelligence deep learning-based technique (C-SENSE AI), was compared at different acceleration factors and flip angles using ROI-based analysis. ADC bias was assessed by Lin\'s Concordance correlation coefficient (CCC) followed by bootstrapping to calculate confidence intervals (CI). ADC random measurement error (RME) was assessed by the mean coefficient of variation (CV¯) and non-parametric statistical tests.
RESULTS: The simulations predicted increasingly negative bias and loss of precision towards lower SNR. These effects were confirmed in phantom measurements of increasing acceleration, for which CCC decreased from 0.947 to 0.279 and CV¯ increased from 0.043 to 0.439, and of decreasing flip angle, for which CCC decreased from 0.990 to 0.063 and CV¯ increased from 0.037 to 0.508. At high acceleration and low flip angle, C-SENSE AI reconstruction yielded best denoised ADC maps. For the lowest investigated flip angle, CCC = {0.630, 0.771 and 0.987} and CV¯={0.508, 0.426 and 0.254} were obtained for {SENSE, C-SENSE, C-SENSE AI}, the improvement by C-SENSE AI being significant as compared to the other methods (CV: p = 0.033 for C-SENSE AI vs. C-SENSE and p < 0.001 for C-SENSE AI vs. SENSE; CCC: non-overlapping CI between reconstruction methods). For the highest investigated acceleration factor, CCC = {0.479,0.926,0.960} and CV¯={0.519,0.119,0.118} were found, confirming the reduction of bias and RME by C-SENSE AI as compared to C-SENSE (by trend) and to SENSE (CV: p < 0.001; CCC: non-overlapping CI).
CONCLUSIONS: ADC bias and random measurement error in DWI at low SNR, typically associated with scan acceleration, can be effectively reduced by deep-learning based C-SENSE AI reconstruction.

UNASSIGNED: Carpal tunnel syndrome (CTS) is diagnosed based on neurological, electrophysiology, and radiological findings. Due to the technical development of magnetic resonance imaging (MRI), the median nerve is evaluated with several MRI protocols. However, diffusion tensor imaging (DTI) combined with a dual-echo steady-state (DESS) protocol is not frequently used to evaluate the median nerve of CTS. This study aimed to evaluate the median nerve in the carpal tunnel using DTI combined with a DESS protocol.
UNASSIGNED: Five healthy volunteers and seven patients with CTS were enrolled. The patients underwent MRI for CTS pre- and post-operatively. The median nerve was evaluated using a 3-T MRI scanner. The parameters of the DESS protocol were as follows: Repetition time (TR)/echo time (TE) = 10.83/3.32 ms, slice thickness = 0.45 mm, field of view (FoV) = 350 × 253 × 350 mm, and 3D voxel size = 0.5 × 0.5 ×0.4 mm. The parameters of the DTI sequence were as follows: TR/TE = 4000/86 ms, slice thickness = 3 mm, FoV = 160 × 993 × 90 mm, 3D voxel size = 1.2 × 1.2 ×3.0 mm, and b value = 0.1000 s/mm2. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values of the median nerve were statistically analyzed. Statistical significance was set at P< 0.05.
UNASSIGNED: The FA value of healthy volunteers was 0.576 ± 0.058, while those of the patients were 0.357 ± 0.094 and 0.395 ± 0.062 pre-and post-operatively, respectively. Statistically significant differences were identified between the FA values of healthy volunteers and pre-operative/post-operative patients. The ADC values of healthy volunteers and pre-operative patients were 0.931 ± 0.096 and 1.26 ± 0.282 (10-3 mm2/s), respectively (P< 0.05).
UNASSIGNED: This MRI protocol may be useful for evaluating the median nerve in the carpal tunnel.

OBJECTIVE: Cranial nerve involvement (CNI) influences the treatment strategies and prognosis of head and neck tumors. However, its incidence in skull base chordomas and chondrosarcomas remains to be investigated. This study evaluated the imaging features of chordoma and chondrosarcoma, with a focus on the differences in CNI.
METHODS: Forty-two patients (26 and 16 patients with chordomas and chondrosarcomas, respectively) treated at our institution between January 2007 and January 2023 were included in this retrospective study. Imaging features, such as the maximum diameter, tumor location (midline or off-midline), calcification, signal intensity on T2-weighted image, mean apparent diffusion coefficient (ADC) values, contrast enhancement, and CNI, were evaluated and compared using Fisher\'s exact test or the Mann-Whitney U-test. The odds ratio (OR) was calculated to evaluate the association between the histological type and imaging features.
RESULTS: The incidence of CNI in chondrosarcomas was significantly higher than that in chordomas (63% vs. 8%, P < 0.001). An off-midline location was more common in chondrosarcomas than in chordomas (86% vs. 13%; P < 0.001). The mean ADC values of chondrosarcomas were significantly higher than those of chordomas (P < 0.001). Significant associations were identified between chondrosarcomas and CNI (OR = 20.00; P < 0.001), location (OR = 53.70; P < 0.001), and mean ADC values (OR = 1.01; P = 0.002).
CONCLUSIONS: The incidence of CNI and off-midline location in chondrosarcomas was significantly higher than that in chordomas. CNI, tumor location, and the mean ADC can help distinguish between these entities.

UNASSIGNED: Central post-stroke pain (CPSP), seen in the aftermath of a stroke, is an underdiagnosed entity but quite a disabling complication. All the postulated theories regarding the pathogenesis of CPSP point to its origin in the central pain pathways. However, this study attempts to demonstrate the role of other contributing areas in the generation of CPSP.
UNASSIGNED: In this single-center tertiary care hospital-based study, 24 patients with both ischemic and hemorrhagic strokes of variable durations were recruited, and Magnetic Resonance Imaging (MRI) imaging with diffusion tensor imaging (DTI) acquisition was done. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of the spinothalamic tract (STT), corticospinal tract (CST), superior thalamic radiation (STR), basal ganglia (BG), and primary somatosensory cortex (SSC) were compared between normal and abnormal sides and also in extrathalamic lesions separately.
UNASSIGNED: Significant differences with lower FA were noted in STT, CST, STR, and SSC and higher ADC values in BG, STR, CST, and SSC on comparison between the normal and lesion sides. On individual sub-analysis, ischemic stroke had significant changes in the FA value of CST and the ADC value of STR and CST, while hemorrhagic stroke had significant changes in the FA and ADC values of STR and SSC, as well as the FA value of STT. In the analysis of the extrathalamic strokes, significance persisted in all the studied parameters except the BG. The CST abnormalities were evident even in patients with clinical motor improvement. On multivariate analysis, visual analogue scale score severity was correlated with thalamic lesions.
UNASSIGNED: Contrary to the belief that STT is solely responsible for CPSP, the role of CST, STR, BG, and SSC as contributing areas is evident from this study and may be more well established if studied in a larger population.

The purpose of our study was to explore and compare the tumor burden of different bone marrow infiltration patterns and evaluate the feasibility of apparent diffusion coefficient (ADC) value to identify patterns in multiple myeloma (MM).
Ninety-three patients with newly diagnosed multiple myeloma and 23 controls had undergone routine magnetic resonance imaging (MRI) and diffusion-weighted MRI (DWI) from January 2019 to November 2020. Five bone marrow (BM) infiltration patterns were allocated according to routine MRI. The laboratory data and ADC values of patterns were analyzed and compared. ROC analysis was used to establish the best diagnostic ADC threshold value for identifying these patterns and distinguishing normal pattern from controls. Besides, the correlation between the ADC values of diffuse pattern and the plasma cells ratio was assessed.
The values of hemoglobin, beta-2 microglobulin (β2-MG), plasma cell, M protein, the percentages of stage, high-risk fluorescence in situ hybridization, and ADC values showed significant difference among patterns. ADCmean at a specific value (368.5×10-6 mm2/s) yielded a maximum specificity (95.5%) and sensitivity (92.0%) in diagnosing MM. A specific value (335.5×10-6mm2/s) yielded a maximum specificity (84.7%) and sensitivity (88.0%) in discriminating visually normal pattern in MM from controls. There was a moderate positive correlation between the plasma cells ratio and ADCs of diffuse infiltration patterns (r = 0.648, P < 0.001).
The bone marrow infiltration patterns in MM patients can indicate the tumor burden and ADC value has the ability to discriminate these patterns objectively.

Distinguishing treatment-related abnormalities (TRA) from tumor progression (TP) in glioblastoma patients is a diagnostic imaging challenge due to the identical morphology of conventional MR imaging sequences. Diffusion-weighted imaging (DWI) and its derived images of the apparent diffusion coefficient (ADC) have been suggested as diagnostic tools for this problem. The aim of this study is to determine the diagnostic accuracy of different cut-off values of the ADC to differentiate between TP and TRA. In total, 76 post-treatment glioblastoma patients with new contrast-enhancing lesions were selected. Lesions were segmented using a T1-weighted, contrast-enhanced scan. The mean ADC values of the segmentations were compared between TRA and TP groups. Diagnostic accuracy was compared by use of the area under the curve (AUC) and the derived sensitivity and specificity values from cutoff points. Although ADC values in TP (mean = 1.32 × 10-3 mm2/s; SD = 0.31 × 10-3 mm2/s) were significantly different compared to TRA (mean = 1.53 × 10-3 mm2/s; SD = 0.28 × 10-3 mm2/s) (p = 0.003), considerable overlap in their distributions exists. The AUC of ADC values to distinguish TP from TRA was 0.71, with a sensitivity and specificity of 65% and 70%, respectively, at an ADC value of 1.47 × 10-3 mm2/s. These findings therefore indicate that ADC maps should not be used in discerning between TP and TRA at a certain timepoint without information on temporal evolution.