Magnetic Resonance Imaging Acquisition Research Articles

7T Magnetic Compatible Multimodality Electrophysiological Signal Recording System

This paper developed a comprehensive magnetic resonance imaging (MRI)-compatible electrophysiological (EP) acquisition system, which can acquire various physiological electrical signals, including electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG) and electrocorticogram (ECoG), and EP recording combined with multimodal stimulation. The system is designed to be compatible with the 7-Tesla (7T) ultra-high field MRI environment, providing convenience for neuroscience and physiological research. To achieve MRI compatibility, the device uses magnetically compatible materials and shielding measures on the hardware and algorithm processing on the software side. Different filtering algorithms are adopted for different signals to suppress all kinds of interference in the MRI environment. The system can allow input signals up to ±0.225 V and channels up to 256. The equipment has been tested and proven to be able to collect a variety of physiological electrical signals effectively. When scanned under the condition of a 7T high-intensity magnetic field, the system does not generate obvious heating and can meet the safety requirements of MRI and EEG acquisition requirements. Moreover, an algorithm is designed and improved to efficiently and automatically remove the gradient artifact (GA) noise generated by MRI, which is a thousand-fold gradient artifact. Overall, this work proposes a complete, portable, MRI-compatible system that can collect a variety of physiological electrical signals and integrate more efficient GA removal algorithms.

Evaluation of Compressed SENSE on Image Quality and Reduction of MRI Acquisition Time: A Clinical Validation Study

To evaluate the effect of compressed SENSE (CS)in clinical settings on scan time reduction and image quality. Ninety-five magnetic resonance imaging (MRI) scans from different anatomical regions were acquired, consisting of a standard protocol sequence (SS) and sequence accelerated with CS. Anonymized paired sequences were randomly displayed and rated by six blinded subspecialty radiologists. Side-by-side evaluation on perceived sharpness, perceived signal-to-noise-ratio (SNR), lesion conspicuity, and artifacts were compared and scored on a five-point Likert scale, and individual image quality was evaluated on a four-point Likert scale. CS reduced overall scan time by 32% while maintaining acceptable MRI quality for all regions. The largest time savings were seen in the spine (mean = 68 seconds, 44% reduction) followed by the brain (mean = 86seconds, 37% reduction). The sequence with maximum time savings was intracranial 3D-time-of-flight magnetic resonance angiography (202seconds, 56% reduction). CS was mildly inferior to SS on perceived sharpness, perceived SNR, and lesion conspicuity (mean scores = 2.32-2.96, P<.001 [1:SS superior; 3: equivalent; 5: CS superior]). CS was equivalent to SS for joint and body scans on overall image quality (CS = 3.02-3.37, SS = 3.04-3.68, P>.05, [1: lowest quality and 4: highest quality]). The overall image quality of CS was slightly less for brain and spine scans (mean CS = 2.79-3.05, mean SS = 3.13-3.43, P=.021) but still diagnostic. Good overall clinical acceptance for CS (88%) was noted with full clinical acceptance for body scans (100%)and high acceptance for other regions (68%-95%). CS significantly reduced MR acquisition time while maintaining acceptable image quality. The implementation of CS may improve departmental workflows and enhance patient care.

Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics

Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides excellent soft-tissue contrast and high-resolution images of the human body, allowing us to understand detailed information on morphology, structural integrity, and physiologic processes. However, MRI exams usually require lengthy acquisition times. Methods such as parallel MRI and Compressive Sensing (CS) have significantly reduced the MRI acquisition time by acquiring less data through undersampling k-space. The state-of-the-art of fast MRI has recently been redefined by integrating Deep Learning (DL) models with these undersampled approaches. This Systematic Literature Review (SLR) comprehensively analyzes deep MRI reconstruction models, emphasizing the key elements of recently proposed methods and highlighting their strengths and weaknesses. This SLR involves searching and selecting relevant studies from various databases, including Web of Science and Scopus, followed by a rigorous screening and data extraction process using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. It focuses on various techniques, such as residual learning, image representation using encoders and decoders, data-consistency layers, unrolled networks, learned activations, attention modules, plug-and-play priors, diffusion models, and Bayesian methods. This SLR also discusses the use of loss functions and training with adversarial networks to enhance deep MRI reconstruction methods. Moreover, we explore various MRI reconstruction applications, including non-Cartesian reconstruction, super-resolution, dynamic MRI, joint learning of reconstruction with coil sensitivity and sampling, quantitative mapping, and MR fingerprinting. This paper also addresses research questions, provides insights for future directions, and emphasizes robust generalization and artifact handling. Therefore, this SLR serves as a valuable resource for advancing fast MRI, guiding research and development efforts of MRI reconstruction for better image quality and faster data acquisition.

Brain Connectivity Correlates of Cognitive Dispersion in a Healthy Middle-Aged Population: Influence of Subjective Cognitive Complaints.

Cognitive dispersion, representing intraindividual fluctuations in cognitive performance, is associated with cognitive decline in advanced age. We sought to elucidate sociodemographic, neuropsychological, and brain connectivity correlates of cognitive dispersion in middle age, and further consider potential influences of the severity of subjective cognitive complaints (SCC). Five hundred and twenty healthy volunteers from the Barcelona Brain Health Initiative (aged 40-66 years; 49.6% females, 453 with magnetic resonance imaging acquisitions) were included and stratified into high and low SCC groups. Two analysis steps were undertaken: (1) for the whole sample and (2) by groups. Generalized linear models and analysis of covariance were implemented to study associations between cognitive dispersion and performance (episodic memory, speed of processing, and executive function), white matter integrity, and resting-state functional connectivity (rs-FC) of the default mode network (DMN) and dorsal attentional networks (DAN). Across-domain dispersion was negatively related to cognitive performance, rs-FC within the DMN, and between the DMN and the DAN, but not to white matter integrity. The rs-FC values were not explained by cognitive performance. When considering groups, the above findings were significant only for those with high SCC. In healthy middle-aged individuals, high cognitive dispersion was related to poorer cognition and DMN dysregulation, being these associations stronger among subjects with high SCC. The present results reinforce the interest in considering dispersion measures within neuropsychological evaluations, as they may be more sensitive to incipient age-related cognitive and functional brain changes than traditional measures of performance.

Radiomic Features Are Predictive of Response in Rectal Cancer Undergoing Therapy.

Rectal cancer is a major mortality cause in the United States (US), and its treatment is based on individual risk factors for recurrence in each patient. In patients with rectal cancer, accurate assessment of response to chemoradiotherapy has increased in importance as the variety of treatment options has grown. In this scenario, a controversial non-operative approach may be considered in some patients for whom complete tumor regression is believed to have occurred. The recommended treatment for locally advanced rectal cancer (LARC, T3-4 ± N+) is total mesorectal excision (TME) after neoadjuvant chemoradiotherapy (nCRT). Magnetic resonance imaging (MRI) has become a standard technique for local staging of rectal cancer (tumor, lymph node, and circumferential resection margin [CRM] staging), in both the US and Europe, and it is getting widely used for restaging purposes. In our study, we aimed to use an MRI radiomic model to identify features linked to the different responses of chemoradiotherapy of rectal cancer before surgery, and whether these features are helpful to understand the effectiveness of the treatments. We retrospectively evaluated adult patients diagnosed with LARC who were subjected to at least 2 MRI examinations in 10-12 weeks at our hospital, before and after nCRT. The MRI acquisition protocol for the 2 exams included T2 sequence and apparent diffusion coefficient (ADC) map. The patients were divided into 2 groups according to the treatment response: complete or good responders (Group 1) and incomplete or poor responders (Group 2). MRI images were segmented, and quantitative features were extracted and compared between the two groups. Features that showed significant differences (SF) were then included in a LASSO regression method to build a radiomic-based predictive model. We included 38 patients (26 males and 12 females), who are classified from T2 and T4 stages in the rectal cancer TNM. After the nCRT, the patients were divided into Group 1 (13 patients), complete or good responders, and Group 2 (25 patients), incomplete or poor responders. Analysis at baseline generated the following significant features for the Mann-Whitney test (out of a total of 107) for each sequence. Also, the analysis at the end of the follow-up yielded a high number of significant features for the Mann-Whitney test (out of a total of 107) for each image. Features selected by the LASSO regression method for each image analyzed; ROC curves relative to each model are represented. We developed an MRI-based radiomic model that is able to differentiate and predict between responders and non-responders who went through nCRT for rectal cancer. This approach might identify early lesions with high surgical potential from lesions potentially resolving after medical treatment.

Fast Compressed Sensing of 3D Radial T1 Mapping with Different Sparse and Low-Rank Models.

Knowledge of the relative performance of the well-known sparse and low-rank compressed sensing models with 3D radial quantitative magnetic resonance imaging acquisitions is limited. We use 3D radial T1 relaxation time mapping data to compare the total variation, low-rank, and Huber penalty function approaches to regularization to provide insights into the relative performance of these image reconstruction models. Simulation and ex vivo specimen data were used to determine the best compressed sensing model as measured by normalized root mean squared error and structural similarity index. The large-scale compressed sensing models were solved by combining a GPU implementation of a preconditioned primal-dual proximal splitting algorithm to provide high-quality T1 maps within a feasible computation time. The model combining spatial total variation and locally low-rank regularization yielded the best performance, followed closely by the model combining spatial and contrast dimension total variation. Computation times ranged from 2 to 113 min, with the low-rank approaches taking the most time. The differences between the compressed sensing models are not necessarily large, but the overall performance is heavily dependent on the imaged object.

Dynamic Magnetic Resonance Vascular Fingerprinting During Hypercapnia for Quantitative and Multiparametric Cerebrovascular Reactivity Measures.

Magnetic resonance fingerprinting (MRF) represents a potential paradigm shift in MR image acquisition, reconstruction, and analysis using computational biophysical modelling in parallel to image acquisition. Its flexibility allows for examination of cerebrovascular metrics through MR vascular fingerprinting (MRvF), and this has been extended even further to produce quantitative cerebral blood volume (CBV), microvascular vessel radius, and tissue oxygen saturation (SO2) maps of the whole brain simultaneously every few seconds. This allows for observation of rapid physiological changes like cerebrovascular reactivity (CVR), which is the ability of vessels to dilate in response to a vasoactive stimulus. Here we demonstrated a novel protocol in which a rapid, spin- and gradient-echo pulse sequence allowed for dynamic, and simultaneous acquisition of MRvF and blood oxygen level dependent (BOLD) measures. By combining this with a tailored hypercapnic (5% CO2) breathing paradigm we were able to show how these quantitative CBV, radius, and SO2 parameters changed in response to a stimulus and directly compare those to a colocalized, traditionally used BOLD CVR. We also compared these measures to another traditionally utilized technique in cerebral blood flow CVR from an arterial spin labelling sequence. These imaging, processing, and analysis techniques will allow for further investigation into the magnitude and rate of CVR based on BOLD and MRvF-based metrics and enable investigations to better understand vascular function in healthy aging and cerebrovascular diseases.Clinical Relevance- The development of dynamic magnetic resonance vascular fingerprinting has the potential to enable rapid, quantitative, and multiparametric functional imaging biomarkers of cerebrovascular diseases like vascular cognitive impairment, dementia, and Alzheimer's disease.

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

Background:Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. Purpose:investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. Methods:CT and corresponding T1-weighted MRI with/without contrast, T2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation.A “Baseline” generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. Results:The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE) = 106 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline. Conclusion:DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining.

MRI evaluation of foraminal changes in the cervical spine with assistance of a novel compression device

Standard supine Magnetic Resonance Imaging (MRI) does not acquire images in a position where most patients with intermittent arm radiculopathy have symptoms. The aim of this study was to test the feasibility of a new compression device and to evaluate image quality and foraminal properties during a Spurling test under MRI acquisition. Ten asymptomatic individuals were included in the study (6 men and 4 women; age range 27 to 55 years). First, the subjects were positioned in the cervical compression device in a 3 T MRI scanner, and a volume T2 weighted (T2w) sequence was acquired in a relaxed supine position (3 min). Thereafter, the position and compressive forces on the patient’s neck (provocation position) were changed by maneuvering the device from the control room, with the aim to simulate a Spurling test, causing a mild foraminal compression, followed by a repeated image acquisition (3 min). A radiologist measured the blinded investigations evaluating cervical lordosis (C3–C7), foraminal area on oblique sagittal images and foraminal cross-distance in the axial plane. A total of three levels (C4–C7) were measured on the right side on each individual. Measurements were compared between the compressed and relaxed state. Reliability tests for inter- and intraclass correlation were performed. The device was feasible to use and well tolerated by all investigated individuals. Images of adequate quality was obtained in all patients. A significant increase (mean 9.4, p = 0.013) in the cervical lordosis and a decreased foraminal cross-distance (mean 32%, p < 0.001) was found, during the simulated Spurling test. The area change on oblique sagittal images did not reach a statistically significant change. The reliability tests on the quantitative measures demonstrated excellent intraobserver reliability and moderate to good interobserver reliability. Applying an individualized provocation test on the cervical spine, which simulates a Spurling test, during MRI acquisition was feasible with the novel device and provided images of satisfactory quality. MRI images acquired with and without compression showed changes in cervical lordosis and foraminal cross distance indicating the possibility of detecting changes of the foraminal properties. As a next step, the method is to be tested on symptomatic patients.

Usefulness of additional diffusion MRI acquisition prior to mechanical thrombectomy for acute large vessel occlusion in the early time period at a CT-based stroke center

ObjectiveThis study aimed to investigate whether evaluating the infarction core using additionally acquired diffusion magnetic resonance imaging (MRI) could help improve the assessment of prognosis including complication rates and modify the strategy for mechanical thrombectomy in endovascular procedures at a computed tomography (CT)-based stroke center. MethodsSingle-center data from patients with acute large-vessel occlusion in the anterior circulation who underwent mechanical thrombectomy between May 2018 and January 2021 were analyzed. Diffusion MRI sequences were performed during the preparation period for mechanical thrombectomy after CT angiography. We set the infarction core reference volume on diffusion MRI to 60 cc and divided the patients into two groups: a small infarction core group (less than 60 cc) and a large infarction core group (more than 60 cc). The baseline characteristics, radiological and clinical outcomes of the patients were investigated and compared between the two groups. ResultsThe difference in numbers between the two groups was not significant in the Alberta Stroke Program Early Computed Tomography (ASPECT) score; however, the ASPECT score on diffusion MRI showed a remarkable difference between the two groups. The large infarction core volume group on diffusion MRI had a poor prognosis, with the modified Rankin score at 90 days showing a statistically significant difference (p = 0.011). Complications after the procedure, such as hemorrhagic transformation, that can occur after reperfusion, symptomatic intracerebral hemorrhage, decompressive craniectomy for increased intracranial pressure, and mortality, were significantly more frequent in the large infarction core volume group. ConclusionAt a CT-based stroke center, additionally acquired diffusion MRI without a time delay for reperfusion would improve the assessment of prognosis including complication rate, and could help neurointerventionists determine the extent of recanalization of occluded vessels during mechanical thrombectomy.

Virtual 4DCT generated from 4DMRI in gated particle therapy: phantom validation and application to lung cancer patients

Objective. Respiration negatively affects the outcome of a radiation therapy treatment, with potentially severe effects especially in particle therapy (PT). If compensation strategies are not applied, accuracy cannot be achieved. To support the clinical practice based on 4D computed tomography (CT), 4D magnetic resonance imaging (MRI) acquisitions can be exploited. The purpose of this study was to validate a method for virtual 4DCT generation from 4DMRI data for lung cancers on a porcine lung phantom, and to apply it to lung cancer patients in PT. Approach. Deformable image registration was used to register each respiratory phase of the 4DMRI to a reference phase. Then, a static 3DCT was registered to this reference MR image set, and the virtual 4DCT was generated by warping the registered CT according to previously obtained deformation fields. The method was validated on a physical phantom for which a ground truth 4DCT was available and tested on lung tumor patients, treated with gated PT at end-exhale, by comparing the virtual 4DCT with a re-evaluation 4DCT. The geometric and dosimetric evaluation was performed for both proton and carbon ion treatment plans. Main results. The phantom validation exhibited a geometrical accuracy within the maximum resolution of the MRI and mean dose deviations, with respect to the prescription dose, up to 3.2% for target D 95%, with a mean gamma pass rate of 98%. For patients, the virtual and re-evaluation 4DCTs showed good correspondence, with errors on target D 95% up to 2% within the gating window. For one patient, dose variations up to 10% at end-exhale were observed due to relevant inter-fraction anatomo-pathological changes that occurred between the planning and re-evaluation CTs. Significance. Results obtained on phantom data showed that the virtual 4DCT method was accurate, allowing its application on patient data for testing within a clinical scenario.

Progressive Feedback Residual Attention Network for Cardiac Magnetic Resonance Imaging Super-Resolution.

Atrial fibrillation (AF) is an increasing medical burden worldwide, and its pathological manifestations are atrial tissue remodeling and low-pressure atrial tissue fibrosis. Due to the inherent defects of medical image data acquisition systems, the acquisition of high-resolution cardiac magnetic resonance imaging (CMRI) faces many problems. In response to these problems, we propose the Progressive Feedback Residual Attention Network (PFRN) for CMRI super-resolution. Specifically, we directly perform feature extraction on low-resolution images, retain feature information to a large extent, and then build multiple independent progressive feedback modules to extract high-frequency details. To accelerate network convergence and improve image reconstruction quality, we implement the MS-SSIM-L1 loss function. Furthermore, we utilize the residual attention stack module to explore the image's internal relevance and extract the low-resolution image's detailed features. Extensive benchmark evaluation shows that PFRN can improve the detailed information of the image SR reconstruction results, and the reconstructed CMRI has a better visual effect.

Left or right ear? A neuroimaging study using combined taVNS/fMRI to understand the interaction between ear stimulation target and lesion location in chronic stroke

BackgroundImplanted vagus nerve stimulation (VNS) and transcutaneous auricular VNS (taVNS) have been primarily administered clinically to the unilateral-left vagus nerve. This left-only convention has proved clinically beneficial in brain disorders. However, in stroke survivors, the presence of a lesion in the brain may complicate VNS-mediated signaling, and it is important to understand the laterality effects of VNS in stroke survivors to optimize the intervention. ObjectiveTo understand whether taVNS delivered to different ear targets relative to the lesion (ipsilesional vs contralesional vs bilateral vs sham) impacts blood oxygenation level dependent (BOLD) signal propagation in stroke survivors. MethodsWe enrolled 20 adults with a prior history of stroke. Each participant underwent a single visit, during which taVNS was delivered concurrently during functional magnetic resonance imaging (fMRI) acquisition. Each participant received three discrete active stimulation conditions (ipsilesional, contralesional, bilateral) and one sham condition in a randomized order. Stimulation-related BOLD signal changes in the active conditions were compared to sham conditions to understand the interaction taVNS and laterality effects. ResultsAll active taVNS conditions deactivated the contralesional default mode network related regions compared to sham, however only ipsilesional taVNS enhanced the activations in the ipsilesional visuomotor and secondary visual cortex. Furthermore, we reveal an interaction in task activations between taVNS and cortical visuomotor areas, where ipsilesional taVNS significantly increased ipsilesional visuomotor activity and decreased contralesional visuomotor activity compared to sham. ConclusionLaterality of taVNS relative to the lesion is a critical factor in optimizing taVNS in a stroke population, with ipsilesional stimulation providing largest direct brain activation and should be explored further when designing taVNS studies in neurorehabilitation.

How does the brain age in individuals with multiple sclerosis? A systematic review.

Multiple Sclerosis (MS) is a complex neurological disorder that involves demyelination, lesions and atrophy in both white and gray matter. Such changes in the central nervous system are diagnostic in MS and has a strong relationship with both physical and cognitive symptoms. As a result, magnetic resonance imaging (MRI) scans as a metric of brain atrophy have emerged as an important outcome measure in MS studies. Recently, research has begun to focus on the contribution of aging to the structural changes in the brain associated with MS; prompting questions about whether there is an amplifying effect of aging superimposed on MS-related brain atrophy. To examine current evidence of how the brain ages in individuals with MS, a systematic review of the literature was performed. Specific questions were focused on how aging affects gray and white matter structure, whether patterns of brain atrophy differ in younger and older cohorts and if there are structural differences in the brain as a function of sex in aging people with MS. This review considered studies that used MRI to examine the effects of aging in adults with MS. Twenty-one studies met eligibility criteria. Findings across these studies revealed that gray matter atrophy was more pronounced in older adults with MS, particularly in subcortical regions such as the thalamus; that the rates of atrophy were similar but varied by region for younger and older cohorts; and that males may experience more brain atrophy than females. Further studies that use multimodal MRI acquisition methods are needed to capture changes in both males and females over time, particularly in middle to older adulthood.

Detection of fetal brain abnormalities using data augmentation and convolutional neural network in internet of things

The all-embracing morphological changes in fetal brain development in the whole time pregnancy are visually seen in acquisition of Magnetic Resonance Imaging (MRI) techniques. In the way of exploring fetal brain development, Convolutional Neural Network (CNN) technique brings out automatic segmentation and classification. Monitoring fetal brain development is crucial to prevent brain abnormality. With the automation and resource optimization provided by IOT technology in healthcare applications, the national healthcare industries can improve the quality of care while lowering costs. The remarkable progresses in image recognition tasks are undergone via CNN to recognize complex patterns in image data. It faces the standstill challenges in observing the fetal brain development inclusive of 16–39 weeks gestation images and explores the optimized performance evaluation for quantitative assessment. The goal of this research is to improve access to brain development information by utilizing modern medical detection method as well as embedding IoT (internet of things). The brain malformations are considered as a problem in recent days, to overcome this problem the Detection of Fetal Brain Abnormalities (DFBA) using Data Augmentation is proposed in this paper and the IOT technology is helps to identify the detection in easy way. The proposed DFBA perform in three stages image pre-processing, model build and performance evaluation. The input dataset comprises 875 MRI scan images taken from the Harvard medical school with the training set as 80%, testing set as 10%, and validation set as 10%. Once the data is retrieved, the preprocessing techniques are carried out using Normalization and Reshaping for better progress and result. In sequence to pre-processing, Data augmentation is applied to increase the size of the dataset for CNN to be processed for classification. The efficiency of the proposed DFBA approach has been determined using the evaluation metrics such as Recall, F1-score, Accuracy, Precision, Confusion matrix and Support. The proposed method peculiarly tests set accuracy as 83% are obtained which is better than the existing classifiers.

Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality

Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations. Therefore, the purpose of this study was to investigate the impact on image quality and diagnostic confidence of an innovative deep learning-based accelerated FLAIR (FLAIRDLR) sequence of the brain compared to conventional (standard) FLAIR (FLAIRS) imaging. Seventy consecutive patients with staging cerebral MRIs were retrospectively enrolled in this single-center study. The FLAIRDLR was conducted using the same MRI acquisition parameters as the FLAIRS sequence, except for a higher acceleration factor for parallel imaging (from 2 to 4), which resulted in a shorter acquisition time of 1:39minute instead of 2:40minutes (-38%). Two specialized neuroradiologists evaluated the imaging datasets using a Likert scale that ranged from 1 to 4, with 4 indicating the best score for the following parameters: sharpness, lesion demarcation, artifacts, overall image quality, and diagnostic confidence. Additionally, the image preference of the readers and the interreader agreement were assessed. The average age of the patients was 63±11years. FLAIRDLR exhibited significantly less image noise than FLAIRS, with P-values of<.001 and<.05, respectively. The sharpness of the images and the ability to detect lesions were rated higher in FLAIRDLR, with a median score of 4 compared to a median score of 3 in FLAIRS (P-values of<.001 for both readers). In terms of overall image quality, FLAIRDLR was rated superior to FLAIRS, with a median score of 4 vs 3 (P-values of<.001 for both readers). Both readers preferred FLAIRDLR in 68/70 cases. The feasibility of deep learning FLAIR brain imaging was shown with additional 38% reduction in examination time compared to standard FLAIR imaging. Furthermore, this technique has shown improvement in image quality, noise reduction, and lesion demarcation.

Progression of white matter signal abnormalities in predementia frontotemporal dementia mutation carriers

AbstractBackgroundWhite matter signal abnormalities (WMSA) are frequently observed on magnetic resonance imaging (MRI) scans of people with frontotemporal dementia (FTD). We hypothesized that carriers of mutations in progranulin (GRN+), chromosome 9 open reading frame 72 (C9orf72+) and microtubule associated protein tau (MAPT+) have increased rates of progression of WMSA compared to non‐carrier individuals prior to onset of dementia, and that different mutations may have different rates of WMSA accumulation.MethodParticipants were recruited through the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Study. We selected participants with Clinical Dementia Rating scale (CDR) scores of 0 and 0.5. If a participant developed FTD during the follow‐up, we only analyzed observations collected before the conversion. WMSA were defined as hyperintensities on FLAIR MRI. Total WMSA volumes were normalized to the respective intracranial volumes. We used a linear mixed effects model, with random intercepts and slopes, to compare the baseline burden and the longitudinal progression of total WMSA volumes between mutation carriers and controls. The model was adjusted for baseline age, sex, education, baseline Montreal Cognitive Assessment (MoCA) scores, and MRI acquisition sites.ResultWe analyzed MRI data from 199 participants who had at least two MRI visits (46 C9orf72+, 17 GRN+, 35 MAPT+, 101 non‐carrier controls; up to five years of follow‐up). We did not find significant group differences in baseline demographic features, except that MAPT+ was younger than non‐carrier controls (average age: C9orf72+ = 47, GRN+ = 56, MAPT+ = 40, controls = 47). Baseline WMSA volumes were not significantly different between mutation carriers and controls. Longitudinally, the rates of WMSA volume increase in C9orf72+ and MAPT+ were comparable to controls; whereas, GRN+ had higher rates of WMSA volume increases compared to controls (p = 0.01).ConclusionWMSA may accumulate at higher rates in GRN+ prior to the onset of FTD, which is consistent with the frequent presence of white matter diseases observed in symptomatic FTD patients with GRN mutations. Future work is warranted to investigate the association between white matter structural changes and cognitive/psychiatric changes in FTD mutation carriers.

Multiparametric Magnetic Resonance Investigations on Acute and Long-Term Kidney Injury.

Acute kidney injury (AKI) is a frequent complication of critical illness and carries a significant risk of short- and long-term mortality. The prediction of the progression of AKI to long-term injury has been difficult for renal disease treatment. Radiologists are keen for the early detection of transition from AKI to long-term kidney injury, which would help in the preventive measures. The lack of established methods for early detection of long-term kidney injury underscores the pressing needs of advanced imaging technology that reveals microscopic tissue alterations during the progression of AKI. Fueled by recent advances in data acquisition and post-processing methods of magnetic resonance imaging (MRI), multiparametric MRI is showing great potential as a diagnostic tool for many kidney diseases. Multiparametric MRI studies offer a precious opportunity for real-time noninvasive monitoring of pathological development and progression of AKI to long-term injury. It provides insight into renal vasculature and function (arterial spin labeling, intravoxel incoherent motion), tissue oxygenation (blood oxygen level-dependent), tissue injury and fibrosis (diffusion tensor imaging, diffusion kurtosis imaging, T1 and T2 mapping, quantitative susceptibility mapping). The multiparametric MRI approach is highly promising but the longitudinal investigation on the transition of AKI to irreversible long-term impairment is largely ignored. Further optimization and implementation of renal MR methods in clinical practice will enhance our comprehension of not only AKI but chronic kidney diseases. Novel imaging biomarkers for microscopic renal tissue alterations could be discovered and benefit the preventative interventions. This review explores recent MRI applications on acute and long-term kidney injury while addressing lingering challenges, with emphasis on the potential value of the development of multiparametric MRI for renal imaging on clinical systems. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Aortic Hemodynamics with Accelerated Dual-Venc 4D Flow MRI in Type B Aortic Dissection

The aim of this study is to investigate the applicability of the dual-venc (DV) 4D flow magnetic resonance imaging (MRI) to quantify the complex flow patterns in type B aortic dissection (TBAD). One GRAPPA-accelerated single-venc (SV) and one compressed-sensing (CS) accelerated DV 4D flow MRI sequences are used to scan all subjects, including twelve chronic TBAD patients and two volunteers. The scans are performed twice for the reproducibility assessment of the scan protocols. Voxelwise quantitative flow parameters including kinetic energy (KE), peak velocity (PV), forward and reverse flows (FF, RF) and stasis are calculated. High-venc (HV) data from the DV acquisition are separately analyzed. The scan time reduction by the CS-accelerated DV 4D flow MRI acquisition is 46.4% compared with the SV acquisition. The DV velocity-to-noise ratio (VNR) is higher compared with HV (p = 0.000). No true lumen (TL) parameter shows a significant difference among the acquisition types (p &gt; 0.05). The false lumen (FL) RF is higher in SV compared with the DV acquisition (p = 0.009). The KE is higher (p = 0.038) and stasis is lower (p = 0.01) in HV compared with SV acquisition. All FL parameters except stasis are higher and stasis is lower in HV compared with DV acquisition (p &lt; 0.05). Positive Pearson correlations among the acquisition types in TL and high agreements between the two scans for all acquisition types are observed except HV RF in the FL, which demonstrates a moderate agreement. The CS-accelerated DV 4D flow MRI may have utility in the clinical daily routine with shortened scan times and improved velocity measurements while providing high VNR in TBAD. The observed hemodynamic flow trends are similar between GRAPPA-accelerated SV and CS-accelerated DV 4D flow MRI acquisitions; however, parameters are more impacted by CS-accelerated HV protocol in FL, which may be secondary to the CS regularization effects.