Magnetic Resonance Imaging Methods Research Articles

MR Imaging Techniques for Microenvironment Mapping of the Glioma Tumors: ASystematic Review.

The tumor microenvironment (TME) is a critical regulator of cancer progression, metastasis, and treatment response. Currently, various imaging approaches exist to assess the pathophysiological features of the TME. This systematic review provides an overview of magnetic resonance imaging (MRI) methods used in clinical practice to characterize the pathophysiological features of the gliomas TME. This review involved a systematic comprehensive search of original open-access articles reporting the clinical use of MR imaging in glioma patients of all ages in the PubMed, Scopus, and Web of Science databases between January 2010 and December 2023. We restricted our research to papers published in the English language. A total of 1137 studies were preliminarily identified through electronic database searches. After duplicate studies were removed, 44 studies met the eligibility criteria. The glioma TME was accompanied by alterations in metabolism, pH, vascularity, oxygenation, and extracellular matrix components, including tumor-associated macrophages, and sodium concentration. Multiparametric MRI is capable of noninvasively assessing the pathophysiological features and tumor-supportive niches of the TME, which is in line with its application in personalized medicine.

Diagnostic accuracy of preoperative MRI in assessing macrotrabecular-massive subtype of hepatocellular carcinoma: a systematic review and meta-analysis.

To determine the value of preoperative magnetic resonance imaging (MRI) in predicting macrotrabecular-massive hepatocellular carcinoma (MTM-HCC). A search was conducted on PubMed, Web of Science, Cochrane Library databases, and Embase for studies evaluating the performance of MRI in assessing MTM-HCC. The quality assessment of diagnostic studies (QUADAS-2) tool was used to assess the risk of bias. Diagnostic accuracy measures, including sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR), were pooled. Summary receiver operating characteristic (SROC) curves with the area under the curve (AUC) were generated. Meta-regression analysis was performed to explore potential sources of heterogeneity. A total of ten eligible studies including 2074 lesions in 2053 patients were analyzed. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.65 (0.52, 0.76), 0.88 (0.80, 0.94), 5.6 (3.70, 8.60), 0.40 (0.30, 0.53), 14 (10, 20), and 0.84 (0.81, 0.87), respectively. High heterogeneity was observed (I2 was 78.61% and 90.95% for sensitivity and specificity, respectively) along with a threshold effect (Spearman's correlation coefficient = 0.927, p < 0.001). Meta-regression analysis demonstrated that the MRI method (radiomics or non-radiomics) affected the heterogeneity. MRI has diagnostic value for MTM-HCC due to its higher specificity and moderate sensitivity, but its clinical application remains suboptimal due to significant heterogeneity. Thus, further prospective studies with large sample sizes are needed to confirm these results. Question What is the value of MRI for preoperatively predicting MTM-HCC? Findings Meta-regression analyses revealed that the MRI method (radiomics or non-radiomics) is a significant factor contributing to heterogeneity. Clinical relevance This study demonstrates the high diagnostic accuracy of MRI for early detection of MTM-HCC, which can assist in guiding individualized management.

Prospective Harmonization, Common Data Elements, and Sharing Strategies for Multicenter Pre-Clinical Traumatic Brain Injury Research in the Translational Outcomes Project in Neurotrauma Consortium.

Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science. Here, we present practical approaches for harmonization of TBI research across five centers providing needed vocabulary and structure to achieve centralized data organization and use. This includes data sharing as an essential step that enables validating data between domains, evaluating reproducibility between sites, and performing multimodal analyses. As part of this process, TOP-NT (1) produced a library of TBI-relevant standard operating procedures to coordinate workflow, (2) aligned 481 pre-clinical and clinical common data elements (CDEs), and (3) generated 272 new pre-clinical TBI CDEs. This consortium then (4) connected diverse data types to validate assessments across domains and to allow multivariable TBI phenotyping. Lastly, TOP-NT (5) specified technical quality controls for pre-clinical studies. These harmonization tools can facilitate reproducibility in team science, help distinguish a wide injury spectrum from technical variability, apply quality-controls, and ease higher level data analyses. TOP-NT uses three rat TBI models across four sites. Each site collects primary outcome measures, including magnetic resonance imaging (MRI) protocols and blood biomarkers of neuronal and glial injury, validated by histopathology and behavioral outcomes. Collected data are organized using the 481 TOP-NT pre-clinical CDEs, covering surgical, behavioral, biomarker, MRI, and quantitative histopathological methods. We report data curation steps suited for data storage using the Open Data Commons for TBI as a centralized data repository, allowing unbiased cross-site analysis. This approach leads to introducing a higher level, syndromic understanding of TBI signatures. TOP-NT authors outline a semantic and structural framework suggesting strategies for robust pre-clinical research in multicenter trials to improve translatability for TBI assessments. [Figure: see text].

Factors Affecting the Presence of Renal Cortical Cysts in Kidney Donors.

Simple renal cysts (SRCs) represent the most frequently occurring type of renal cysts, frequently observed in the elderly population. While generally considered benign, SRCs may sometimes be connected to comorbid conditions such as hypertension, aortic diseases, and renal dysfunction. This research aims to investigate the factors influencing the development of SRCs in kidney donors and the associated risks. This retrospective cohort study included 1012 living kidney donors, aged 18-87 years, who underwent renal transplant donor nephrectomy between 2008 and 2023. Data on demographic information, cyst characteristics, comorbidities, and associated risk factors were collected and analyzed using statistical methods, including Binary Logistic Regression Analysis. Renal cysts were identified and classified using computed tomography (CT) and magnetic resonance imaging (MRI) methods. Renal cortical cysts were more frequently observed in males (52.76%) compared to females (47.24%), with a significant difference (P=.031). Donors with renal cortical cysts were significantly older (mean age 54.43 ± 12.17 years) compared to those without cysts (46.26 ± 12.35 years, P < .001). Substantial differences were likewise noted in fasting blood glucose, uric acid, creatinine, HbA1c, and glomerular filtration rate (GFR). The prevalence of aortic atherosclerosis was notably elevated in donors with cysts (47.74%) compared to those without (23.57%, P < .001). Binary logistic regression analysis indicated that older age and being male were significant factors influencing the presence of cortical cysts. The study confirms that SRCs are the most common renal cyst type and are more frequently observed in the elderly population. While generally benign, SRCs may be associated with increased uric acid levels and other comorbidities, suggesting potential impacts on kidney health. Additional studies are required to investigate these associations. The presence of SRCs in kidney donors is significantly associated with male gender, age, uric acid levels, and creatinine levels. These findings should be considered during the evaluation of potential kidney donors, particularly regarding the associated risks and management of SRCs.

Transcranial Cortex-Wide Imaging of Murine Ischemic Perfusion With Large-Field Multifocal Illumination Microscopy.

Ischemic stroke is a common cause of death worldwide and a main cause of morbidity. Presently, laser speckle contrast imaging, x-ray computed tomography, and magnetic resonance imaging are the mainstay for stroke diagnosis and therapeutic monitoring in preclinical studies. These modalities are often limited in terms of their ability to map brain perfusion with sufficient spatial and temporal resolution, thus calling for development of new brain perfusion techniques featuring rapid imaging speed, cost-effectiveness, and ease of use. We report on a new preclinical high-resolution angiography technique for murine ischemic stroke imaging based on large-field high-speed multifocal illumination fluorescence microscopy. We subsequently showcase the proposed method by monitoring therapeutic effects of thrombolysis in stroke (n=6), further performing cross-strain comparison of perfusion dynamics (n=6) and monitoring the therapeutic effects of sensory stimulation-based treatment (n=11). Quantitative readings of hemodynamic and structural changes in cerebral vascular network and pial vessels were attained with 14.4-µm spatial resolution at 80-Hz frame rate fully transcranially. The in vivo perfusion maps accurately delineated the ischemic core and penumbra, further exhibiting a strong correlation (86.1±4.5%) with ex vivo triphenyl tetrazolium chloride staining, significantly higher than for the conventional laser speckle contrast imaging method. Monitoring of therapeutic effects of thrombolysis confirmed that early recanalization could effectively save the penumbra while reducing the infarct area. Cross-strain comparison of perfusion dynamics affirmed that C57BL/6 mice feature a larger penumbra and smaller infarct core as compared with BALB/c mice, which have few or no collaterals. Sensory stimulation-based treatment could effectively enhance blood flow and abolish perfusion deficits in the ischemic core and penumbra regions. A high-speed fluorescence-based angiography method for transcranial stroke imaging in mice is introduced, which is capable of localizing brain perfusion changes and accurately assessing the ischemic penumbra. Compared with the whole-brain x-ray computed tomography and magnetic resonance imaging methods, which are conventionally used for stroke diagnosis and therapeutic monitoring, the new approach is simple and cost-effective, further offering high resolution and speed for in vivo studies. It thus opens new venues for brain perfusion research under various disease conditions such as stroke, neurodegeneration, or epileptic seizures.

Prospective Evaluation of Accelerated Brain MRI Using Deep Learning-Based Reconstruction: Simultaneous Application to 2D Spin-Echo and 3D Gradient-Echo Sequences.

To prospectively evaluate the effect of accelerated deep learning-based reconstruction (Accel-DL) on improving brain magnetic resonance imaging (MRI) quality and reducing scan time compared to that in conventional MRI. This study included 150 participants (51 male; mean age 57.3 ± 16.2 years). Each group of 50 participants was scanned using one of three 3T scanners from three different vendors. Conventional and Accel-DL MRI images were obtained from each participant and compared using 2D T1- and T2-weighted and 3D gradient-echo sequences. Accel-DL acquisition was achieved using optimized scan parameters to reduce the scan time, with the acquired images reconstructed using U-Net-based software to transform low-quality, undersampled k-space data into high-quality images. The scan times of Accel-DL and conventional MRI methods were compared. Four neuroradiologists assessed the overall image quality, structural delineation, and artifacts using Likert scale (5- and 3-point scales). Inter-reader agreement was assessed using Fleiss' kappa coefficient. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated, and volumetric quantification of regional structures and white matter hyperintensities (WMHs) was performed. Accel-DL showed a mean scan time reduction of 39.4% (range, 24.2%-51.3%). Accel-DL improved overall image quality (3.78 ± 0.71 vs. 3.36 ± 0.61, P < 0.001), structure delineation (2.47 ± 0.61 vs. 2.35 ± 0.62, P < 0.001), and artifacts (3.73 ± 0.72 vs. 3.71 ± 0.69, P = 0.016). Inter-reader agreement was fair to substantial (κ = 0.34-0.50). SNR and CNR increased in Accel-DL (82.0 ± 23.1 vs. 31.4 ± 10.8, P = 0.02; 12.4 ± 4.1 vs. 4.4 ± 11.2, P = 0.02). Bland-Altman plots revealed no significant differences in the volumetric measurements of 98.2% of the relevant regions, except in the deep gray matter, including the thalamus. Five of the six lesion categories showed no significant differences in WMH segmentation, except for leukocortical lesions (r = 0.64 ± 0.29). Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradient-echo sequences without compromising volumetry, including lesion quantification.

Ex vivo imaging of subacute myocardial infarction with ultra-short echo time 3D quantitative T1- and T1ρ -mapping magnetic resonance imaging in mice.

The aim of this study was to develop an ultra-short echo time 3D magnetic resonance imaging (MRI) method for imaging subacute myocardial infarction (MI) quantitatively and in an accelerated way. Here, we present novel 3D T1- and T1ρ -weighted Multi-Band SWeep Imaging with Fourier Transform and Compressed Sensing (MB-SWIFT-CS) imaging of subacute MI in mice hearts ex vivo. Relaxation time-weighted and under-sampled 3D MB-SWIFT-CS MRI were tested with manganese chloride (MnCl2) phantom and mice MI model. MI was induced in C57BL mice, and the hearts were collected 7 days after MI and then fixated. The hearts were imaged with T1 and adiabatic T1ρ relaxation time-weighted 3D MB-SWIFT-CS MRI, and the contrast-weighted image series were estimated with a locally low-rank regularized subspace constrained reconstruction. The quantitative parameter maps, T1 and T1ρ , were then obtained by performing non-linear least squares signal fitting on the image estimates. For comparison, the hearts were also imaged using 2D fast spin echo-based T2 and T1ρ mapping methods. The relaxation rates varied linearly with the MnCl2 concentration, and the T1 and T1ρ relaxation time values were elevated in the damaged areas. The ischaemic areas could be observed visually in the 3D T1, 3D T1ρ , and 2D MRI maps. The scar tissue formation in the anterior wall of the left ventricle and inflammation in the septum were confirmed by histology, which is in line with the results of MRI. MI with early fibrosis, increased inflammatory activity, and interstitial oedema were determined simultaneously with T1 and T1ρ relaxation time constants within the myocardium by using the 3D MB-SWIFT-CS method, allowing quantitative isotropic 3D assessment of the entire myocardium.

Clean Self-Supervised MRI Reconstruction from Noisy, Sub-Sampled Training Data with Robust SSDU.

Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data.

Prediction of isocitrate dehydrogenase mutation status in WHO grade II glioma by diffusion kurtosis imaging.

Isocitrate dehydrogenase (IDH) mutation status serves as a crucial prognostic indicator for glioma, typically assessed via immunohistochemical analysis post-surgery. Given the invasiveness of this approach, perhaps we can utilise convenient and noninvasive magnetic resonance imaging (MRI) methods to predict IDH mutation status. However, the current landscape lacks a standardised MRI technique for accurately predicting IDH mutations. In this study, we explore the potential of MRI diffusion kurtosis imaging (DKI) in forecasting the IDH mutation status of WHO grade II brain gliomas. Twenty-five patients with WHO grade II gliomas were retrospectively included. Patients underwent routine MRI and DKI scanning before surgery, measuring tumoural solid portion, peritumoral oedema, and normal-appearing white matter (NAWM) DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (Ka), and axial radial kurtosis (Kr). The DKI parameter corrections were made (tumour or oedema parameters values divided by the NAWM value) to obtain the rFA (ratio of FA), rMD (ratio of MD), rMK (ratio of MK), rKA (ratio of KA), and rKr (ratio of Kr) values. Postoperative specimens were made of wax blocks and analysed by Sanger gene sequencing. DKI parameters between the 2 groups were compared by independent sample t-tests. The ROC curve was used to analyse the diagnostic value of each parameter. Twenty-five patients were diagnosed with IDH-mutant (16 cases) and IDH-wild type (9 cases). The rFA and rMK values in the parenchymal region of IDH wild-type tumour were higher than those of IDH mutant, while the rMD values were lower than those of IDH mutant, and the difference between them was statistically significant (p < 0.05). The values of DKI parameters of peritumoral oedema in the 2 groups were not statistically significant. DKI can provide microstructural changes of diseased tissues and provide more imaging information for preoperative non-invasive judgment of IDH mutation status of WHO grade II gliomas. The values of rMK, rFA, and rMD are helpful in the assessment IDH mutation status, benefiting accurate diagnoses and treatment decisions.

Assessment of Regional Brain Volume Measurements with Different Brain Extraction and Bias Field Correction Methods in Neonatal MRI

Proper selection and application of preprocessing steps are crucial for obtaining accurate segmentation in brain Magnetic Resonance Imaging (MRI). The aim of this study is to evaluate the impact brain extraction (BE) and bias field correction (BFC) methods have on regional brain volume (RBV) measurements of preterm neonates’ T2w MRI at term-equivalent age (TEA). Five BE methods (Manual, BET2, SWS, HD-BET, SynthStrip) were applied together with two BFC methods (SPM-BFC and N4ITK), before segmenting the neonatal brain into eight tissue classes (cortical grey matter, white matter, cerebral spinal fluid, deep nuclear grey matter, hippocampus, amygdala, cerebellum, and brainstem) using an automated segmentation software (MANTiS). Quantitative assessments were conducted, including the coefficient of variation (CV), coefficient of joint variation (CJV), Dice coefficient (DC), and RBV. HD-BET, together with N4ITK, showed the highest performance (mean ± standard deviation) regarding CV of 0.047 ± 0.005 (white matter) and 0.070 ± 0.005 (grey matter), CJV of 0.662 ± 0.095, DC of 0.942 ± 0.063, and RBV without significant differences (except in the brainstem) from the manual segmentation. Therefore, such combination of methods is recommended for improved skull-stripping accuracy, intensity homogeneity, and reproducibility of RBV of T2w MRI at TEA.

Advantages of using T1-mapping in cardiac magnetic resonance imaging in patients with acromegaly

Aim. To determine the incidence of interstitial myocardial fibrosis (MF) in acromegaly, which leads to the development of cardiac arrhythmias and conduction disorders. Materials and methods. A single-center study was conducted, including 70 patients with acromegaly. All patients underwent standard medical examinations, including hormonal blood tests, electrocardiograms, echocardiography, Holter monitoring electrocardiograms and magnetic resonance imaging (MRI) of the heart with gadolinium contrast, additionally 48 patients underwent T1-mapping, which is an MRI method that allows the detection of diffuse changes by measuring the values of myocardial relaxation time – T1. Results. The study revealed a high incidence of MF – 53 (75.7%) cases (21 women, 32 men). The duration of the disease was found to be critical for myocardial remodeling (p=0.006). Compliance criteria for the T1-mapping technique were determined, with reduced T1-mapping values observed in 85.2% of cases. The ROC analysis (Receiver Operator Characteristic) determined the diagnostic value of post-contrast T1-mapping: AUC (Area Under the Curve)=0.906 (95% confidence interval 0.789–1.000), a sensitivity of 90%, and a specificity of 76.5%, indicating high diagnostic efficiency. According to the Youden index, a cut-off point of 372.5 ms was selected. Conclusion. Myocardial T1-mapping is a novel MRI method that allows to assess the degree of MF without the need for myocardial biopsy. The obtained information is crucial for the diagnosis and prognostic assessment of heart diseases, particularly in cases of hypertrophic cardiomyopathy or infiltrative diseases. The T1-mapping method, which is actively developing, can serve as a marker for early diffuse myocardial fibrosis and help determine the prognosis for patients with acromegalic cardiomyopathy.

Sensitivity of Functional Arterial Spin Labelling in Detecting Cerebral Blood Flow Changes.

Aims/Background Arterial spin labelling (ASL) is a non-invasive magnetic resonance imaging (MRI) method. ASL techniques can quantitatively measure cerebral perfusion by fitting a kinetic model to the difference between labelled images (tag images) and ones which are acquired without labelling (control images). ASL functional MRI (fMRI) provides quantitative perfusion maps by using arterial water as an endogenous tracer instead of depending on vascular blood oxygenation level.This study aimed to assess the number of pulsed ASL blocks that were needed to provide accurate and reliable regional estimates of cerebral blood flow (CBF) changes when participants engaged in visually guided saccade and fixation task; evaluate the localization to cortical control saccade versus fixation; investigate the relationship between the sensitivity of ASL fMRI and the number of blocks; and compare the sensitivity of blood oxygen level-dependent (BOLD) fMRI and ASL fMRI. Methods The experiment was a block-design paradigm consisting of two conditions: fixation and saccade. No response other than the eye movements of the participants was recorded during the scans. ASL and BOLD fMRI scans were conducted on all participants during the same session. The fMRI study consisted of two functional experiments: a CBF contrast was provided using the ASL sequence, and an optimized BOLD contrast was provided using the BOLD sequence. Results From group analysis in all divided blocks of ASL sessions (4, 6, 8...... 14, 16, 18......26, 28, 30), ASL yielded significant activation clusters in the visual cortex of the bilateral hemisphere from block 4. There was no false activation from block 4. No activation cluster was found by reversing analysis of block 2. Robust and consistent activation in the visual cortex was observed in each of the 14 divided blocks group analysis, and no activation was found in the eye field of the brain. The sensitivity of 4-block was found to be better than that of 8-block. More significant activation clusters of the visual cortex were found in BOLD than in ASL. No activation cluster of parietal eye field (PEF), frontal eye field (FEF) and supplementary eye field (SEF) was detected in ASL. The voxel size of the activation cluster increased with the increasing number of blocks, and the percent signal change in the activation cluster decreased with the escalating block number. The voxel size was positively correlated with the number of blocks (correlation coefficient = 0.98, p < 0.0001), and the percent signal change negatively correlated with the number of blocks (correlation coefficient = -0.90, p < 0.0001). Conclusion The 4-block pulsed functional ASL (fASL) presents accurate and reliable activation, with minimal time-on-task effect and little adverse impact of time, in participants engaging in visually guided saccade and fixation tasks. Despite having lower sensitivity than BOLD fMRI, ASL can determine accurate activation location. Although the time-on-task effects affect the observation for the sensitivity of ASL over task time, it is suggested that ASL fMRI may provide a powerful method for pinpointing the time-on-task effect over a long period of time.

Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study.

Modic changes (MCs) classification system is the most widely used method in magnetic resonance imaging (MRI) for characterizing subchondral vertebral marrow changes. However, it shows a high degree of sensitivity to variations in MRI because of its semiquantitative nature. In 2021, the authors of this classification system further proposed a quantitative and reliable MC grading method. However, automated tools to grade MCs are lacking. This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. In order to verify performance, we tested CNNs' generalization performance, the performance of CNN with that of junior doctors, and the consistency of junior doctors after AI assistance. A retrospective analysis of 139 patients' MRIs with MCs was conducted and annotated by a spine surgeon. Of the 139 patients, MRIs from 109 patients were acquired using Philips scanners from June 2020 to June 2021, constituting Dataset 1. The remaining 30 patients had MRIs obtained from both Philips and United Imaging scanners from June 2022 to March 2023, forming Dataset 2. YOLOv8 and YOLOv5 were developed in PyCharm using the Python language and based on the PyTorch deep learning framework, data enhancement and transfer learning were applied to enhance model generalization. The model's performance was compared with precision, recall, F1 score, and mAP50. It also tested generalizability and compared it with the junior doctor's performance on the second data set (Dataset 2). Post hoc, the junior doctor graded Dataset 2 with CNN assistance. In addition, the region of interest was displayed using the class activation mapping heat map. On the unseen test set, the YOLOv8 and YOLOv5 models achieved precision of 81.60% and 61.59%, recall of 80.90% and 67.16%, mAP50 of 84.40% and 68.88%, and F1 of 0.81 and 0.60 respectively. On Dataset 2, YOLOv8 and junior doctor achieved precision of 95.1% and 72.5%, recall of 68.3% and 60.6%. In the AI-assisted experiment, agreement between the junior doctor and the senior spine surgeon significantly improved from Cohen's kappa of 0.368-0.681. YOLOv8 in detecting and grading MCs was significantly superior to that of YOLOv5. The performance of YOLOv8 is superior to that of junior doctors, and it can enhance the capabilities of junior doctors and improve the reliability of diagnoses.

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.

Prognosis of asymptomatic endolymphatic hydrops in healthy volunteers: A five-year cohort study.

This study aimed to clarify the prognosis of asymptomatic endolymphatic hydrops (EH) in healthy volunteers via five-year follow-ups with inner ear magnetic resonance imaging (MRI). Inner ear MRI was performed on 115 participants recruited as controls in a previous study on Meniere's disease. The endolymphatic space was visualized using Naganawa's method of contrast-enhanced MRI with intravenous gadolinium injection and evaluated using Nakashima's method of 2D imaging analysis. Cochlear or vestibular EH was present in 7.0% of participants (n = 8), with all cases being unilateral (laterality), moderate (severity), and asymptomatic (onset). Only cochlear-localized EH, only vestibular-localized EH, and both EH were present in 1.7% (n = 2) (C group), 4.3% (n = 5) (V group), and 0.9% (n = 1) (CV group) of participants, respectively. Conducting inner ear MRI after 5 years showed that EH had almost disappeared in two participants in the C and V groups (4/8, 50.0%). EH was still present in three participants in the V group and one in the CV group (4/8, 50.0%). One participant in the V group and another in the CV group presented with residual inner ear EH and developed typical symptomatic Meniere's disease (2/8, 25.0%). Approximately 7% of healthy participants showed asymptomatic EH. Therefore, EH is not the definitive marker for making a diagnosis of Meniere's disease or the suitable predictor for the development of Meniere's disease. Among these participants, 25% maintained EH and subsequently developed typical Meniere's disease within the next 5 years. Schellong-positive participants maintained persistent EH in the inner ear, and participants with higher scores on the self-rating depression scale developed Meniere's symptoms after 5 years. 2a.

Muscle fat infiltration: a narrative review of the magnetic resonance (MR)-based evaluation methods and their clinical applications.

Muscle fat infiltration (MFI) can be seen in several pathologies of various origin including neuromuscular, endocrinological, or musculoskeletal etiologies. Its evaluation is possible through medical imaging, especially MRI, using qualitative or quantitative methods. This paper is an overview of the techniques used to evaluate MFI in different pathologies. The literature search was performed on the PubMed database for articles published in English between 2010 and end of January 2023. The keywords used were: (Fat infiltration of the muscle) AND magnetic resonance imaging (MRI). In total, 159 articles were included. Qualitative methods of MFI evaluation represented 20% of the studies, with the Goutallier classification being the most used, especially in the setting of rotator cuff tears. Quantitative methods were used in most studies (80%), with the Dixon technique being the most adopted. The evaluation of MFI was used for the assessment of endocrinological, neuromuscular and musculoskeletal disorders. Spinal pathologies, including low back pain and spinal deformities, were the most represented (44% of the studies). This review shows the importance of automated MFI calculation using MRI to monitor the progression of diseases, assess the effect of treatments, or predict post-surgical complications such as in the setting of adult spinal deformity. This review also showed the importance of automation of MFI assessment, particularly using the Dixon technique, to make its use easier in clinical settings and open MFI assessment applications to other diseases.

A lightweight adaptive spatial channel attention efficient net B3 based generative adversarial network approach for MR image reconstruction from under sampled data.

Magnetic Resonance Imaging (MRI) stands out as a notable non-invasive method for medical imaging assessments, widely employed in early medical diagnoses due to its exceptional resolution in portraying soft tissue structures. However, the MRI method faces challenges with its inherently slow acquisition process, stemming from the sequential sampling in k-space and limitations in traversal speed due to physiological and hardware constraints. Compressed Sensing in MRI (CS-MRI) accelerates image acquisition by utilizing greatly under-sampled k-space information. Despite its advantages, conventional CS-MRI encounters issues such as sluggish iterations and artefacts at higher acceleration factors. Recent advancements integrate deep learning models into CS-MRI, inspired by successes in various computer vision domains. It has drawn significant attention from the MRI community because of its great potential for image reconstruction from undersampled k-space data in fast MRI. This paper proposes a lightweight Adaptive Spatial-Channel Attention EfficientNet B3-based Generative Adversarial Network (ASCA-EffNet GAN) for fast, high-quality MR image reconstruction from greatly under-sampled k-space information in CS-MRI. The proposed GAN employs a U-net generator with ASCA-based EfficientNet B3 for encoder blocks and a ResNet decoder. The discriminator is a binary classifier with ASCA-based EfficientNet B3, a fully connected layer and a sigmoid layer. The EfficientNet B3 utilizes a compound scaling strategy that achieves a balance amongst model depth, width, and resolution, resulting in optimal performance with a reduced number of parameters. Furthermore, the adaptive attention mechanisms in the proposed ASCA-EffNet GAN effectively capture spatial and channel-wise features, contributing to detailed anatomical structure reconstruction. Experimental evaluations on the dataset demonstrate ASCA-EffNet GAN's superior performance across various metrics, surpassing conventional reconstruction methods. Hence, ASCA-EffNet GAN showcases remarkable reconstruction capabilities even under high under-sampling rates, making it suitable for clinical applications.

Nonlinear parameter estimation with physics-constrained spectral–spatial priors for highly accelerated chemical exchange saturation transfer MRI

Objective.To develop a nonlinear, model-based parameter estimation method directly from incomplete measurements ink - wspace for robust spectral analysis in highly accelerated chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI).Approach. A CEST-specific, separable nonlinear model, which describes spectral decomposition using multi-pool Lorentzian functions (conventional magnetization transfer (MT), direct saturation of water signals (DS), amide, amine, and nuclear Overhauser effect) derived from the steady-state Bloch McConnel equation, is incorporated into a measurement model in CEST MRI. Furthermore, signal drop in saturation transfer experiments is formulated by an additional, separable nonlinear spectral prior indicating that the symmetric z-spectra synthesized using conventional MT and DS always remain higher or equal to the whole z-spectra with all pools. Given the above considerations, linear and nonlinear parameters in the proposed method are estimated in an alternating fashion directly from highly incomplete measurements ink - wspace by solving a constrained optimization problem with the physics-constrained spectral priors while imposing additional sparsity priors on spatial parameter maps.Main results.Compared with conventional methods, the proposed method yields clearer delineation of tumor-specific CEST maps without apparent artifact and noise.Significance.We successfully demonstrated the feasibility of the proposed method for CEST MRI with highly incomplete measurements thus enabling high-resolution whole brain CEST MRI in clinically reasonable imaging time.

An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure. Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network. Therefore, this study aims to find suitable tensor processing methods and deep learning models to achieve better reconstruction results and a smaller network size. We propose a novel unrolling network method that enhances the reconstruction quality and reduces the parameter redundancy by introducing time correlation modeling into MRI reconstruction with low-rank core matrix and convolutional long short-term memory (ConvLSTM) unit. We conduct extensive experiments on AMRG Cardiac MRI dataset to evaluate our proposed approach. The results demonstrate that compared to other state-of-the-art approaches, our approach achieves higher peak signal-to-noise ratios and structural similarity indices at different accelerator factors with significantly fewer parameters. The improved reconstruction performance demonstrates that our proposed time correlation modeling is simple and effective for accelerating MRI reconstruction. We hope our approach can serve as a reference for future research in dynamic MRI reconstruction.

Contribution of Magnetic Resonance Imaging Studies to the Understanding of Cerebral Malaria Pathogenesis

Cerebral malaria (CM), the most lethal clinical syndrome of Plasmodium falciparum infection, mostly affects children under 5 in sub-Saharan Africa. CM is characterized by seizures and impaired consciousness that lead to death in 15–20% of cases if treated quickly, but it is completely fatal when untreated. Brain magnetic resonance imaging (MRI) is an invaluable source of information on the pathophysiology of brain damage, but, due to limited access to scanners in endemic regions, only until very recently have case reports of CM patients studied with advanced MRI methods been published. The murine model of experimental cerebral malaria (ECM) shares many common features with the human disease and has been extensively used to study the pathogenic mechanisms of the neurological syndrome. In vivo MRI studies on this model, the first of which was published in 2005, have contributed to a better understanding of brain lesion formation in CM and identified disease markers that were confirmed by MRI studies published from 2013 onwards in pediatric patients from endemic areas. In this review, we recapitulate the main findings and critically discuss the contributions of MRI studies in the ECM model to the understanding of human CM.