Resonance Imaging Scans Research Articles

Deep learning-based reconstruction: a reliability assessment in preoperative magnetic resonance imaging for primary rectal cancer.

Deep learning has developed rapidly, and deep learning reconstruction (DLR) methods in magnetic resonance imaging (MRI) are gaining attention for their potential to improve efficacy in clinical work. The preoperative MRI assessment of rectal cancer is crucial for patient management, but the imaging quality is currently limited by a number of factors. DLR could be applied to the preoperative MRI assessment of primary rectal cancer, but research about its specific reliability is limited. Thus, this study aimed to evaluate the reliability of DLR in the preoperative MRI examination of primary rectal cancer. This cross-sectional study was conducted at Ruijin Hospital, Shanghai Jiaotong University School of Medicine from March 2022 to October 2022. Patients with primary rectal cancer underwent routine MRI scans on a 3.0T magnetic resonance scanner (SIGNA Architect, GE Healthcare, USA) with 32-channels flexible coil with conventional reconstruction (ConR) and DLR. The DLR method had three noise reduction levels: DLR-H: 75% noise reduction reconstruction; DLR-M: 50% noise reduction reconstruction; and DLR-L: 25% noise reduction reconstruction. Three components were evaluated: objective image quality; subjective image quality; and diagnostic performance. The objective image quality assessment included the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The subjective image quality assessment involved evaluating five subjective image quality parameters based on a 4-point Likert scale. The diagnostic performance assessment included tumour (T) staging, node (N) staging, as well as the circumferential resection margin and extramural vascular invasion evaluation. The images were evaluated in a blinded manner by two radiologists with different levels of experience. The paired sample Wilcoxon signed-rank test, Kappa test, interclass correlation coefficient, Chi-square test, Friedman test, and weighted kappa coefficients were used for the statistical analysis. In total, 61 patients (mean age: 65±12 years; 38 men) were enrolled in the study. The DLR method improved the SNR and CNR values of the images relative to the ConR method, while the DLR-H produced the greatest improvement (P<0.040). The subjective image quality of the DLR-H images was superior to that of the ConR images (P<0.001), but there was no significant difference between the DLR-H and DLR-M images (P≥0.075). The evaluators showed good agreement in subjective scoring, and in the DLR image scoring, the evaluators have the best consistency in the DLR-H images scoring (kappa =0.921, P<0.001). The diagnostic efficacy of the DLR images was comparable to that of the ConR images in terms of T staging [Reader 1 (R1): P=0.603; Reader 2 (R2): P=0.206] and N staging (R1: P=0.990; R2: P=0.884). The DLR method improved the quality of the images, and had comparable diagnostic efficacy without additional scanning time to that of the ConR method, and thus could be a feasible option for replacing the ConR method in the preoperative MRI examination of primary rectal cancer.

A novel nephrometry scoring system outperforms RENAL score, PADUA score, and SPARE score for feasibility prediction of nephron-sparing surgery in children with bilateral Wilms tumor.

Several nephrometry scores have been proposed and applied in the adult population. We previously established a novel model to predict the feasibility of nephron-sparing surgery (NSS) in pediatric bilateral Wilms tumor (WT) patients. This study aimed to evaluate whether our model had better predictive performance compared to other scores. Data from 58 patients during the period 2008-2019 were retrospectively reviewed. Nephrometry scores, including RENAL [radius (R), exophytic/endophytic (E), nearness to collecting system/sinus (N), anterior/posterior (A), and location relative to polar lines (L)], PADUA (preoperative aspects and dimensions used for an anatomical classification), and SPARE (Simplified PADUA REnal) scores, were calculated. A senior radiologist and a senior urologist independently reviewed computed tomography (CT) or magnetic resonance scans while blinded to clinical outcomes. A urology resident re-evaluated the imaging scans using our RSR [relation with collecting system (R), size (S), remaining renal parenchyma (R)] model. The areas under the curve (AUCs) were compared. Inter-rater reliability of the RSR model was assessed using kappa statistics and intraclass correlation coefficient (ICC). The AUC was 0.982 for the RSR model, which outperformed the other three scores. Decision curve analysis (DCA) showed the same results. Compared to the RENAL score, the RSR model showed significant reclassification ability [0.573 (95% confidence interval: 0.161, 1.204), P=0.03]. Significant overall discrimination ability was found with RSR compared to the other three scores (P<0.001). The ICC value was 0.871 for S, and the kappa values of the two R indicators were 0.806 and 0.777, respectively. A web-based calculator was constructed. The RSR model's predictive performance outperforms the established adult nephrometry scores while offering overall fair interobserver agreement. A web-based calculator could help to make personalized treatment decisions.

Improved MR Detection of Optic Nerve Demyelination With MP2RAGE-FLAWS Compared With T2-Weighted Fat-Saturated Sequences.

Nonenhanced T1-w sequences such as magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) and derived fluid and white matter suppression (FLAWS) have demonstrated high performance for detecting brain parenchymal and cervical spine demyelinating lesions in multiple sclerosis. However, their potential for identifying optic nerve (ON) demyelination remains unexplored. The aim of this study was to evaluate the performance of compressed sensing-accelerated (CS) MP2RAGE-FLAWS imaging for detection of ON demyelination lesions compared with T2-w fat-saturated (FS) TSE imaging in a clinical setting. We conducted a retrospective study of magnetic resonance scans acquired on patients with central nervous system demyelinating disorders between January and December 2022. Inclusion criteria were the acquisition in the same session of a brain CS-MP2RAGE-FLAWS imaging and a combination of axial + coronal T2-w FS orbital sequences. A 4-step radiological analysis-including blinded and consensus readings-assessed ON lesion detection. The reference standard was the final reading session of radiologists using the entire patient file. Sensitivities and specificities of both sequences were computed and compared using McNemar χ2 tests. Thirty-nine patients (mean age: 43 ± 14 years; 25 women) were analyzed, including 34 with multiple sclerosis, 2 with MOGAD (myelin oligodendrocyte glycoprotein antibody-associated disease), 1 with NMOSD (neuromyelitis optica spectrum disorder), and 2 with indeterminate demyelinating disease. Among the 78 ONs analyzed, 64 lesions were detected with CS-MP2RAGE-FLAWS as opposed to 37 with 2D T2-w FS imaging, corresponding to a total of 41 and 27 affected nerves, respectively. CS-MP2RAGE-FLAWS exhibited higher sensitivity for overall detection of ON lesions compared with 2D T2-w FS imaging (97.5% vs 67.5%, P = 0.001) without reducing the specificity. Improved lesion detectability with CS-MP2RAGE-FLAWS was significant compared with 2D T2-w FS in intraorbital and intracanalicular segments (respectively, 92.3% vs 50% and 96.3% vs 66.7%; P < 0.05). There was no difference in sensitivity (P = 0.69) or specificity (P = 0.99) regarding the intracranial segment analysis. CS-MP2RAGE-FLAWS sequence improves ON lesion detection compared with conventional 2D T2-w FS, especially in the intraorbital segment, while simultaneously providing whole-brain and cervical spinal cord imaging at no additional time cost.

Automated Tooth Segmentation in Magnetic Resonance Scans Using Deep Learning.

The main objective was to develop and evaluate an artificial intelligence (AI) model for tooth segmentation in magnetic resonance (MR) scans. MR scans of 20 patients performed with a commercial 64-channel head coil with a T1-weighted 3D-SPACE (Sampling Perfection with Application Optimized Contrasts using different flip angle Evolution) sequence were included. 16 datasets were used for model training and 4 for accuracy evaluation. Two clinicians segmented and annotated the teeth in each dataset. A segmentation model was trained using the nnU-Net framework.The manual reference tooth segmentation and the inferred tooth segmentation were superimposed and compared by computing precision, sensitivity, and Dice-Sørensen coefficient. Surface meshes were extracted from the segmentations, and the distances between points on each mesh and their closest counterparts on the other mesh were computed, of which the mean (average symmetric surface distance, ASSD) and 95th percentile (Hausdorff distance 95%, HD95) were reported. The model achieved an overall precision of 0.867, a sensitivity of 0.926, a Dice-Sørensen coefficient of 0.895, and a 95% Hausdorff distance of 0.91 mm. The model predictions were less accurate for datasets containing dental restorations due to image artefacts. The current study developed an automated method for tooth segmentation in MR scans with moderate to high effectiveness for scans with respectively without artefacts.

Volume and Distribution of Early Knee Effusion After TKA with a PEEK-Based Knee Prosthesis

Background: Early knee effusion is a common phenomenon after total knee arthroplasty (TKA), with potential clinical implications. Unlike traditional alloy knee prostheses, the polyetheretherketone (PEEK) knee system has radiographic transparency on magnetic resonance (MR) scans, which allows analysis of prosthetic knee effusion. We aimed to identify the distribution and volume of knee effusion after TKA with the PEEK prosthesis with use of MR imaging and to analyze whether dynamic changes in effusion were correlated with serum inflammatory marker changes and knee function recovery. Methods: Nine patients with osteoarthritis who were 59 to 74 years old underwent unilateral TKA with the PEEK prosthesis between June 2021 and August 2021. Dynamic early postoperative changes in the volume and distribution of knee effusion were evaluated with use of 3D MR stereoscopic images. Serum inflammatory markers were measured via blood tests, and joint function was evaluated with use of the subjective functional score of the Knee Society Score (KSS) and knee range of motion (ROM). Linear regression analyses were performed to assess for correlations between knee effusion volume and inflammatory markers and between knee effusion volume and joint function. Results: The mean serum inflammatory marker levels increased significantly at 1 week after TKA with the PEEK prosthesis and then gradually decreased with time from 1 to 6 months. The mean total knee effusion volume gradually decreased over time. Concurrently, the mean KSS subjective functional score and mean knee ROM improved with time. Total knee effusion volume was positively correlated with C-reactive protein level (R2 = 0.16; p = 0.007) and negatively correlated with the change in KSS score between the preoperative and postoperative time points (R2 = 0.19; p = 0.003). Using the 1-week total knee effusion volume as a reference, a positive correlation was observed between the reduction in total knee effusion volume and the actual value of the ROM (R2 = 0.36; p = 0.0001) from 3 to 24 months postoperatively. Conclusions: Through 3D MR imaging, the precise distribution and volume of, and dynamic changes in, knee effusion after TKA with the PEEK prosthesis were confirmed and were found to be correlated with inflammation and joint function in the early postoperative period. The results demonstrate the potential clinical benefit of the PEEK-based knee system for future use. Level of Evidence: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Quantitative susceptibility mapping reveals brain iron accumulation in minimal hepatic encephalopathy: associations with neurocognitive changes.

Brain iron deposition is correlated with minimal hepatic encephalopathy (MHE). This study aimed to investigate the pattern of altered iron distribution, using quantitative susceptibility mapping (QSM), and to clarify the relationship between iron deposition and neurocognitive changes in MHE.We enrolled 32 cirrhotic patients without MHE (NHE), 21 cirrhotic patients with MHE, and 24 healthy controls, and used the Psychometric Hepatic Encephalopathy Score (PHES) to assess neurocognitive function. All participants underwent magnetic resonance scans with a gradient-echo sequence reconstructing for QSM. We performed voxel-wise and region-of-interest (ROI)-wise analyses to investigate the QSM difference across three groups and to examine the relationship between susceptibility value and PHES.MHE patients exhibited increased susceptibility value in widespread brain areas (family-wise error (FWE)-corrected P < 0.05), which was located mainly in cognition-related regions (such as the prefrontal lobe, precuneus, inferior parietal lobule, insula, thalamus, and superior longitudinal fasciculus), sensorimotor regions (such as the precentral/postcentral gyrus, superior parietal lobule, and posterior corona radiata), visual regions (such as the occipital cortex and posterior thalamic radiation), and auditory regions (such as the temporal lobe). NHE patients also followed a trend of increasing susceptibility in the scattered brain regions, but which did not reach statistical significance (FWE-corrected P > 0.05). We observed negative correlations between cirrhotic patients' PHES and regional susceptibility values (FWE-corrected P < 0.05).Brain iron accumulation (measured using QSM) contributes to cognitive impairments in MHE patients. QSM could provide new insights into the pathogenesis of MHE and facilitate monitoring disease development.

Absence of cardiac impairment in patients after SARS-CoV-2 infection: a long-term follow-up study

BackgroundConcerns exist that long-term cardiac alterations occur after SARS-CoV-2 infection, particularly in patients who were hospitalized in the acute phase or who remain symptomatic. This study investigates potential long-term functional and morphological alterations after SARS-CoV-2 infection. MethodsThe authors of this study investigated patients after SARS-CoV-2 infection by using a mobile 1.5-T clinical magnetic resonance scanner for cardiac alterations. Cardiac function and dimensions were assessed using a highly efficient cardiac magnetic resonance (CMR) protocol, which included cine sequences, global longitudinal and circumferential strain assessed by fast-Strain-ENCoded imaging (fSENC), and T1 and T2 mapping. We assessed symptoms through a questionnaire. Patients were compared with a control group matched for age, gender, body mass index, and body surface area. ResultsMedian follow-up time was 395 (192-408) days. The final population included 183 participants (age 48.4±14.3 years, 48.1% male). During the acute phase of SARS-CoV-2 infection, 27 patients were hospital-admitted. 42 patients reported persistent symptoms (shortness of breath, chest pain, palpitations, or leg edema), and 63 reported impaired exercise tolerance. Left ventricular functional and morphological parameters were within the normal range. T1- and T2-relaxation times were also within the normal range, indicating that the presence of myocardial edema or fibrosis were unlikely. Persistently symptomatic patients showed a slightly reduced indexed LV-SV. Functional parameters remained normal in patients who were hospitalized for SARS-CoV-2, persistently symptomatic, or with ongoing impaired exercise tolerance. ConclusionIrrespective of ongoing symptoms or severity of prior illness, patients who have recovered from SARS-CoV-2 infection, demonstrate normal functional and morphological cardiac parameters. Long-term cardiac changes due to SARS-CoV-2 infection appear to be rare.

Altered structural covariance of the cortex and hippocampal formation in patients with lung cancer after chemotherapy

ObjectiveIn this retrospective study, we aimed to investigate changes in brain morphology and structural topological networks in patients with lung cancer (LC) with or without chemotherapy. MethodsWe retrospectively recruited 191 participants for this cross-sectional study, including 113 patients with LC without chemotherapy (Ch-), 38 patients with LC with chemotherapy (Ch+), and 40 healthy controls (HC) matched for age, sex, and education. The gray matter volume (GMV) and cortical properties were compared among the three groups. We constructed the structural covariant network (SCN) based on cortical thickness, volumes of subcortical structures, and volumes of hippocampal subfields and the amygdala in all participants. The global and nodal topological properties of SCN were compared among groups. In addition, 23 patients with LC (8 Ch+ and 15 Ch-) who received two identical brain magnetic resonance scans were enrolled in the follow-up study. The paired t-test was used to compare group differences in brain morphology and topological properties in the structural network. ResultsThe GMV of the bilateral caudate and thalamus were smaller in the Ch- and Ch+ groups compared to the HC group using threshold-free cluster enhancement and permutation (P < 0.05, 5000 times permutations) for multiple comparison correction. The cortical SCN analysis suggested multiple enhanced nodal properties in several brain areas in Ch+ and Ch- compared to HC, mainly in the temporal gyrus, using permutations test and false discovery rate (FDR) (P < 0.05) corrections. Moreover, an increased sigma was found in the Ch+ compared with HC (P = 0.0238). The reduced nodal degree (P = 0.0002) and betweenness (P = 0.0008) in the right amygdala of Ch+ compared to HC were detected by subcortical SCN analysis. Furthermore, reduced gamma (P = 0.0342) and sigma (P < 0.0001) were found in Ch- compared with HC in the SCN analysis of subfields of the amygdala-hippocampal complex. In the follow-up study, reduced nodal degree (P < 0.0001) was found in the right anterior amygdala, and reduced clustering coefficient and local efficiency were found in patients with LC after the permutation test. ConclusionsOur study showed GMV defects and structural topological property abnormalities related to LC and chemotherapy. Such morphological changes associated with LC and chemotherapy could be used as imaging markers for clinical assessments and pathological indicators.

Alterations of subcortical structure volume in pediatric bipolar disorder patients with manic or depressive first-episode

BackgroundBipolar disorder may begin as depression or mania, which can affect the treatment and prognosis. The physiological and pathological differences among pediatric bipolar disorder (PBD) patients with different onset symptoms are not clear. The aims of the present study were to investigate subcortical structural alterations in PBD patients with first-episode depressive (PBD-FED) and first-episode manic (PBD-FEM).MethodsA total of 59 individuals including 28 PBD-FED, 13 PBD-FEM, and 18 healthy controls (HCs) underwent high-resolution structural magnetic resonance scans. FreeSurfer 7.2 was used to detect changes in subcortical volumes. Simultaneously, thalamic, hippocampal, and amygdala subregion volumes were compared between the three groups.ResultsAnalysis of covariance controlling for age, sex, education, and estimated intracranial volume shows third and fourth ventricle enlargement in patients with PBD. Compared with the PBD-FED and HCs, the PBD-FEM group had reduced gray matter volume in the left thalamus, bilateral hippocampus, and right amygdala. Subsequent subregion analyses showed right cortico-amygdaloid transient, bilateral accessory-basal nucleus, left hippocampal tail, right hippocampal head, and body volume reduction in the PBD-FEM group.ConclusionsThe present findings provided evidence of decreased subcortical structure in PBD-FEM patients, which might present its trait feature.

Predictors of ventricular and atrial feature tracking-derived global longitudinal strain in healthy athletes and volunteers

Abstract Background Feature tracking (FT) strain analysis provides insights into the mechanics of the heart and carries essential prognostic and diagnostic information without prolonging image acquisition during cardiac magnetic resonance (CMR) scans. However, data regarding CMR-derived FT strain in athletes is scarce, especially in the atria. We explored the determinants of FT global longitudinal strain (GLS) values concerning all four cardiac chambers in healthy competitive athletes and less active volunteers. Methods Long- and short-axis cine sequences were analyzed with Medis Suite v4.0 (Leiden, The Netherlands) to assess FT strain. After excluding individuals with preexisting pathologies and images with quality issues, subjects were classified by sport types following the 2020 ESC Sports Cardiology Guidelines. Multiple linear regression models were constructed to assess the impact of age, sex (male=0, female=1), weekly training hours, and sports category (power coded as 0, mixed as 1, endurance as 2) on GLS values. Atrial strain values are positive, and ventricular ones are negative. Intercepts/constants are denoted as "baseline" for easier comprehension. Results Native CMR scans acquired at a tertiary cardiac centre of 596 healthy Caucasian subjects (411 athletes: 145 females, mean age 21±6, range 14-36 years; and 185 volunteers: 86 females, mean age 24±5, range 13-35 years) were included. Athletes had a high exercise load versus the controls who performed no or maximum recreational physical activity (19±7 vs. 3±2 hours/week, p&amp;lt;.001). When assessing athletes (weekly training≥6h), RA GLS (baseline: 34%, p&amp;lt;.001) is increased in females (β=2.0) but is expected to decrease with every one-hour increase in training hours (β=-0.2). LA GLS is notably affected by sex alone (baseline: 37%, β=3.6, p&amp;lt;.001), suggesting markedly higher values for females. RV GLS (baseline: -30%, p&amp;lt;.001) is associated with both age and training hours (β=0.1 for each), making it less negative. Conversely, LV GLS (baseline: -25%, p&amp;lt;.0001) is significantly modulated by all studied factors, with sports category (β=0.5), age, and training hours (β=0.1 each) increasing values and sex decreasing them (β=-0.9). In less active volunteers (weekly training&amp;lt;6h), sex (β=4.7) is the only significant factor for RA GLS (baseline: 33%, p=.04), and sports category (β=2.7, the group included 98 recreational athletes with low training loads) for LA GLS (baseline: 38%, p=.04). No variables were retained in the model for RV GLS, but sex (β=-1.4) remained a predictor in the LV GLS model (baseline: -21%, p=.002). Conclusion GLS shows significant variations influenced by age, sex, sports category, and training hours across all four heart chambers in athletes. These effects appear mostly inconclusive among less active individuals. This data calls for further advanced analyses of athlete's hearts to refine differentiation methods between healthy adaptation and cardiac pathologies.Left atrial FT analysisModel outputs for GLS predictions

Severe aortic stenosis is associated with left atrial prothrombotic flow changes that persists despite successful aortic valve replacement

Abstract Background Severe aortic stenosis (AS) is associated with ischaemic stroke independently of atrial fibrillation (AF) or other causes. Even after aortic valve replacement (AVR), patients remain affected by up to 15% five-year rate of incident ischaemic strokes independently of AVR-related complications and with knowledge gaps in the underlying pathophysiology. Purpose To assess whether severe AS leads to prothrombotic left atrial (LA) flow changes with reduction of LA velocities and vorticity which do not normalise after AVR. Methods Seventy-seven participants without history of AF were recruited in this study: N=40 with severe AS (60% male; age 73 ±10 years, BMI 28 ±5 Kg/m2, median CHA2DS2VASc = 3) and N=37 controls with similar clinical risk profile (51% male; age 71 ±6 years, BMI 28 ±5 Kg/m2, median CHA2DS2VASc = 3). All participants underwent a cardiovascular magnetic resonance (CMR) scan to quantify left ventricular (LV) volumes, mass, and ejection fraction (LVEF), LV global longitudinal strain (GLS) as a sensitive marker of longitudinal dysfunction, LA maximum volume and emptying fraction (LAEF), LA blood flow peak velocity and vorticity by 4-dimensional flow CMR. Out of the 40 patients with severe AS, N=23 (58%) underwent a successful AVR and were followed-up with a repeat CMR scan at approximately 6 months after surgery. Results When compared to matched controls, patients with severe AS displayed significantly higher LV mass, smaller LA and LV volumes (i.e. concentric LV hypertrophy), significantly reduced GLS but higher (more hyperdynamic) LVEF (all p&amp;lt;0.05), as expected. LAEF (i.e. global LA function) was not significantly different from controls but despite this, severe AS was associated with slightly lower LA peak flow velocity (mean 0.25 vs. 0.28 m/sec respectively) and lower LA flow vorticity (mean 16.6 vs. 21.1 radians) compared to controls (both p&amp;lt;0.05). Successful AVR led to complete regression of LV hypertrophy (p&amp;lt;0.05) with LV mass that was no longer different from controls (p&amp;gt;0.05). Nevertheless, post-AVR patients displayed a persistent adverse morpho-functional phenotype characterised by persistently smaller LA and LV volumes, persistently hyperdynamic LV function with reduced GLS (all p&amp;lt;0.05), as well as adverse LA flow changes with significantly lower velocity (mean 0.24 vs 0.28 m/sec, p=0.003) and vorticity when compared to controls (17.1 vs 21.1 radians, p&amp;lt;0.001). Conclusions Patients with severe AS display adverse LA physiology and structure (prothrombotic LA flow characteristics in smaller LA size) and LV changes (smaller size with reduced longitudinal function and hyperdynamic function), which persist after successful AVR despite normalisation of the LV hypertrophy.Figure 1

Dapagliflozin reduces epicardial adipose tissue in patients with heart failure with reduced ejection fraction and type 2 diabetes

Abstract Introduction Epicardial adipose tissue (EAT) accumulation is associated with adverse outcome in heart failure with preserved ejection fraction, however its role in HF with reduced ejection fraction (HFrEF) is less clear. The relationship between EAT thickness and their impact on patients who have diabetes and heart failure remains unclear. SGLT2 inhibitors such as dapagliflozin improve outcomes in heart failure and diabetes, however their mechanism of benefit is not well understood. The aim of our study was to assess whether dapagliflozin reduced EAT in patients with type 2 diabetes and HFrEF. Methods We performed an analysis of individuals with type 2 diabetes and HFrEF who were randomised to receive either dapagliflozin or a placebo for 12 months as part of the REFORM trial. Participants had cardiac magnetic resonance (CMR) scans at baseline and at 12 months. Epicardial fat was measured utilizing 4-chamber cine images to quantify adipose tissue between the myocardium and the visceral layer of the pericardium. This assessment was carried out using the single end-diastolic image, with measurements conducted by evaluators blinded to the randomisation status of participants. Results 47 participants completed the stud. At baseline, not only EAT thickness and BMI level (placebo: 33, dapagliflozin 32, p=0.49) but also heart rate (74.39 bpm vs. 77.61 bpm, p=0.35), systolic blood pressure (132.75 mmHg vs. 135.00 mmHg, p=0.63), diastolic blood pressure (71.39 mmHg vs. 74.04 mmHg, p=0.33) and age (67 vs. 66, p=0.79) were comparable between the two groups. 23 randomised to placebo and 24 to dapagliflozin. Baseline EAT was similar in both groups (placebo: 11.54 cm², dapagliflozin 11.74 cm², p=0.414). After 12 months of treatment dapagliflozin caused a significant reduction in EAT compared to placebo (1.21 cm² vs. 0.09 cm², p=0.007). Conclusion Dapagliflozin significantly reduced epicardial adipose tissue compared to placebo in patients with type 2 diabetes and HFrEF. The reduction in EAT may play a role in the beneficial effects of dapagliflozin in heart failure patients.4 Ch CMRBox plot

Long-term trends in total administered radiation dose from brain [18F]FDG-PET in children with drug-resistant epilepsy.

To assess the trends in administered 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) doses, computed tomography (CT) radiation doses, and image quality over the last 15years in children with drug-resistant epilepsy (DRE) undergoing hybrid positron emission tomography (PET) brain scans. We retrospectively analyzed data from children with DRE who had [18F]FDG-PET/CT or magnetic resonance scans for presurgical evaluation between 2005 and 2021. We evaluated changes in injected [18F]FDG doses, administered activity per body weight, CT dose index volume (CTDIvol), and dose length product (DLP). PET image quality was assessed visually by four trained raters. Conversely, CT image quality was measured using region-of-interest analysis, normalized by signal-to-noise (SNR) and contrast-to-noise ratio (CNR). We included 55 children (30 male, mean age: 9 ± 6years) who underwent 61 [18F]FDG-PET scans (71% as PET/CT). Annually, the injected [18F]FDG dose decreased by ~ 1% (95% CI: 0.92%-0.98%, p < 0.001), with no significant changes in administered activity per body weight (p = 0.51). CTDIvol and DLP decreased annually by 16% (95% CI: 9%-23%) and 15% (95% CI: 8%-21%, both p < 0.001), respectively. PET image quality improved by 9% year-over-year (95% CI: 6%-13%, p < 0.001), while CT-associated SNR and CNR decreased annually by 7% (95% CI: 3%-11%, p = 0.001) and 6% (95% CI: 2%-10%, p = 0.008), respectively. Our findings indicate stability in [18F]FDG administered activity per body weight alongside improvements in PET image quality. Conversely, CT-associated radiation doses reduced. These results reaffirm [18F]FDG-PET as an increasingly safer and higher-resolution auxiliary imaging modality for children with DRE. These improvements, driven by technological advancements, may enhance the diagnostic precision and patient outcomes in pediatric epilepsy surgery.

Fitness, Gray Matter Volume, and Executive Function in Cognitively Normal Older Adults: Cross-Sectional Findings From the AGUEDA Trial.

The aim of the study is to investigate the association of cardiorespiratory fitness (CRF) and muscular strength indicators with gray matter volume (GMV) and to study whether fitness-related regions of GMV are associated to executive function (EF) in cognitively normal older adults. Ninety-one cognitively normal older adults (71.69 ± 3.91 years; 57.14% females) participated in this study from the AGUEDA trial. CRF was measured by a 2-km walking test and a 6-min walking test. Muscular strength was measured by handgrip, biceps curl, squats, and isokinetic strength tests. T1-weigthed images were obtained through a magnetic resonance scan. GMV was determined by voxel-based morphometric analysis. Standardized EF tests were performed. CRF did not show any positive association with GMV. Handgrip strength was positively associated with GMV (p < 0.001) in nine regions (β from 0.6 to 0.8 and k from 106 to 1927) and knee extension strength in three regions (β from 0.4 to 0.5 and k from 76 to 2776). Squats strength was negatively associated with GMV (p < 0.001) in two regions (β = -0.3, k = 1102 and k = 152) and the 2-km walking test in one region (β = -0.4, k = 99). Only handgrip strength-related GMV was associated with cognitive flexibility (p = 0.039, β = 0.215) and spatial working memory (p < 0.03, β 0.247-0.317), but not with EF score (p > 0.05). Muscular strength, but no CRF, may be positively related to GMV in cortical and subcortical regions, with implications for specific cognitive domains rather than the overall EF score. Specifically, handgrip strength was the indicator most associated with higher GMV, while squats strength and CRF were negatively related to GMV. ClinicalTrials.gov identifier: NCT05186090.

Accuracy, repeatability, and reproducibility of water-fat magnetic resonance imaging in a phantom and healthy volunteer

Bone marrow (BM) damage due to chemoradiotherapy can increase BM fat in cervical cancer patients. Water-fat magnetic resonance (MR) scans were performed on a phantom and a healthy female volunteer to validate proton density fat fraction accuracy, reproducibility, and repeatability across different vendors, field strengths, and protocols. Phantom measurements showed a high accuracy, high repeatability, and excellent reproducibility. Volunteer measurements had an excellent intra- and interreader reliability, good repeatability, and moderate to good reproducibility. Water-fat MRI show potential for quantification of longitudinal vertebral BM fat changes. Further studies are needed to validate and extend these findings for broader clinical applicability.

TUMbRAIN: A transformer with a unified mobile residual attention inverted network for diagnosing brain tumors from magnetic resonance scans

Diagnosing tumors in Magnetic Resonance Imaging (MRI) brain scans is challenging and can lead to errors, even for radiologists. Deep learning, mainly through deep convolutional neural networks, has assisted in automating the diagnosis of these scans. However, there is still room for improvement. Researchers have shown that transformer models hold promise but often remain underutilized due to their need for large amounts of data and complexity compared to traditional neural networks. This paper introduces a new hybrid model called TUMbRAIN (Transformer with a Unified Mobile Residual Attention Inverted Network), which combines a lightweight transformer with a deep convolutional neural network to address these issues. TUMbRAIN incorporates innovative components designed for this purpose, such as the expanded inverted residual block and the unified self-attention mechanism. The results demonstrate that TUMbRAIN outperforms many existing state-of-the-art neural network models, achieving an impressive overall accuracy of 97.94 % with only 1.04 million parameters. These results suggest that hybrid transformer models like TUMbRAIN could significantly advance the automated diagnosis of brain tumors from MRI scans. The study also offers new insights into effectively integrating transformers into traditional neural network architectures, resulting in a cost-effective and accurate deep learning solution for medical imaging. By incorporating these advanced components, TUMbRAIN enhances support for radiological practice through improved diagnostic accuracy and efficiency.

Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics

BackgroundThis study aims to utilize the deep learning method of VB-Net to locate and segment the trigeminal nerve, and employ radiomics methods to distinguish between CTN patients and healthy individuals.MethodsA total of 165 CTN patients and 175 healthy controls, matched for gender and age, were recruited. All subjects underwent magnetic resonance scans. VB-Net was used to locate and segment the bilateral trigeminal nerve of all subjects, followed by the application of radiomics methods for feature extraction, dimensionality reduction, feature selection, model construction, and model evaluation.ResultsOn the test set for trigeminal nerve segmentation, our segmentation parameters are as follows: the mean Dice Similarity Coefficient (mDCS) is 0.74, the Average Symmetric Surface Distance (ASSD) is 0.64 mm, and the Hausdorff Distance (HD) is 3.34 mm, which are within the acceptable range. Analysis of CTN patients and healthy controls identified 12 features with larger weights, and there was a statistically significant difference in Rad_score between the two groups (p < 0.05). The Area Under the Curve (AUC) values for the three models (Gradient Boosting Decision Tree, Gaussian Process, and Random Forest) are 0.90, 0.87, and 0.86, respectively. After testing with DeLong and McNemar methods, these three models all exhibit good performance in distinguishing CTN from normal individuals.ConclusionsRadiomics can aid in the clinical diagnosis of CTN, and it is a more objective approach. It serves as a reliable neurobiological indicator for the clinical diagnosis of CTN and the assessment of changes in the trigeminal nerve in patients with CTN.

Full-Wave Simulation of a Helmholtz Radiofrequency Coil for Magnetic Resonance Applications

Magnetic resonance imaging (MRI) is a non-invasive diagnostic technique able to provide information about the anatomical, structural, and functional properties of different organs. A magnetic resonance (MR) scanner employs radiofrequency (RF) coils to generate a magnetic field to excite the nuclei in the sample (transmit coil) and pick up the signals emitted by the nuclei (receive coil). To avoid trial-and-error approaches and optimize the RF coil performance for a given application, accurate design and simulation processes must be performed. We describe the full-wave simulation of a Helmholtz coil for high-field MRI performed with the finite-difference time-domain (FDTD) method, investigating magnetic field pattern differences between loaded and unloaded conditions. Moreover, the self-inductance of the single loops constituting the Helmholtz coil was estimated, as well as the frequency splitting between loops due to inductive coupling and the sample-induced resistance. The result accuracy was verified with data acquired with a Helmholtz prototype for small phantom experiments with a 3T MR clinical scanner. Finally, the magnetic field variations and coil detuning after the insertion of the RF shield were evaluated.

Clinical Significance of Myocardial Injury in Patients Hospitalized for COVID-19: A Prospective, Multicenter, Cohort Study

BackgroundHospitalized COVID-19 patients with troponin elevation have a higher prevalence of cardiac abnormalities than control individuals. However, the progression and impact of myocardial injury on COVID-19 survivors remain unclear. ObjectivesThis study sought to evaluate myocardial injury in COVID-19 survivors with troponin elevation with baseline and follow-up imaging and to assess medium-term outcomes. MethodsThis was a prospective, longitudinal cohort study in 25 United Kingdom centers (June 2020 to March 2021). Hospitalized COVID-19 patients with myocardial injury underwent cardiac magnetic resonance (CMR) scans within 28 days and 6 months postdischarge. Outcomes were tracked for 12 months, with quality of life surveys (EuroQol-5 Dimension and 36-Item Short Form surveys) taken at discharge and 6 months. ResultsOf 342 participants (median age: 61.3 years; 71.1% male) with baseline CMR, 338 had a 12-month follow-up, 235 had a 6-month CMR, and 215 has baseline and follow-up quality of life surveys. Of 338 participants, within 12 months, 1.2% died; 1.8% had new myocardial infarction, acute coronary syndrome, or coronary revascularization; 0.8% had new myopericarditis; and 3.3% had other cardiovascular events requiring hospitalization. At 6 months, there was a minor improvement in left ventricular ejection fraction (1.8% ± 1.0%; P < 0.001), stable right ventricular ejection fraction (0.4% ± 0.8%; P = 0.50), no change in myocardial scar pattern or volume (P = 0.26), and no imaging evidence of continued myocardial inflammation. All pericardial effusions (26 of 26) resolved, and most pneumonitis resolved (95 of 101). EuroQol-5 Dimension scores indicated an overall improvement in quality of life (P < 0.001). ConclusionsMyocardial injury in severe hospitalized COVID-19 survivors is nonprogressive. Medium-term outcomes show a low incidence of major adverse cardiovascular events and improved quality of life. (COVID-19 Effects on the Heart; ISRCTN58667920)

Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights.

Prenatal alcohol exposure (PAE) refers to the exposure of the developing fetus due to alcohol consumption during pregnancy and can have life-long consequences for learning, behavior, and health. Understanding the impact of PAE on the developing brain manifests challenges due to its complex structural and functional attributes, which can be addressed by leveraging machine learning (ML) and deep learning (DL) approaches. While most ML and DL models have been tailored for adult-centric problems, this work focuses on applying DL to detect PAE in the pediatric population. This study integrates the pre-trained simple fully convolutional network (SFCN) as a transfer learning approach for extracting features and a newly trained classifier to distinguish between unexposed and PAE participants based on T1-weighted structural brain magnetic resonance (MR) scans of individuals aged 2-8 years. Among several varying dataset sizes and augmentation strategy during training, the classifier secured the highest sensitivity of 88.47% with 85.04% average accuracy on testing data when considering a balanced dataset with augmentation for both classes. Moreover, we also preliminarily performed explainability analysis using the Grad-CAM method, highlighting various brain regions such as corpus callosum, cerebellum, pons, and white matter as the most important features in the model's decision-making process. Despite the challenges of constructing DL models for pediatric populations due to the brain's rapid development, motion artifacts, and insufficient data, this work highlights the potential of transfer learning in situations where data is limited. Furthermore, this study underscores the importance of preserving a balanced dataset for fair classification and clarifying the rationale behind the model's prediction using explainability analysis.