Traditional Magnetic Resonance Imaging Research Articles

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Technical Note: Swing golden angle - A navigator-interleaved golden angle trajectory with eddy current suppression - Application in free-running cardiac MRI.

Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality. This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique. In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images. SwingGA pattern achieves the highest sampling efficiency (mSE>0.925) and the minimum azimuthal angle increment (mAD<1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns. The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI.

Self-supervised anatomical continuity enhancement network for 7T SWI synthesis from 3T SWI

Synthesizing 7T Susceptibility Weighted Imaging (SWI) from 3T SWI could offer significant clinical benefits by combining the high sensitivity of 7T SWI for neurological disorders with the widespread availability of 3T SWI in diagnostic routines. Although methods exist for synthesizing 7T Magnetic Resonance Imaging (MRI), they primarily focus on traditional MRI modalities like T1-weighted imaging, rather than SWI. SWI poses unique challenges, including limited data availability and the invisibility of certain tissues in individual 3T SWI slices. To address these challenges, we propose a Self-supervised Anatomical Continuity Enhancement (SACE) network to synthesize 7T SWI from 3T SWI using plentiful 3T SWI data and limited 3T–7T paired data. The SACE employs two specifically designed pretext tasks to utilize low-level representations from abundant 3T SWI data for assisting 7T SWI synthesis in a downstream task with limited paired data. One pretext task emphasizes input-specific morphology by balancing the elimination of redundant patterns with the preservation of essential morphology, preventing the blurring of synthetic 7T SWI images. The other task improves the synthesis of tissues that are invisible in a single 3T SWI slice by aligning adjacent slices with the current slice and predicting their difference fields. The downstream task innovatively combines clinical knowledge with brain substructure diagrams to selectively enhance clinically relevant features. When evaluated on a dataset comprising 97 cases (5495 slices), the proposed method achieved a Peak Signal-to-Noise Ratio (PSNR) of 23.05 dB and a Structural Similarity Index (SSIM) of 0.688. Due to the absence of specific methods for 7T SWI, our method was compared with existing enhancement techniques for general 7T MRI synthesis, outperforming these techniques in the context of 7T SWI synthesis. Clinical evaluations have shown that our synthetic 7T SWI is clinically effective, demonstrating its potential as a clinical tool.

Tibial and femoral articular cartilage exhibit opposite outcomes for T1ρ and T2* relaxation times in response to acute compressive loading in healthy knees

Abnormal loading is thought to play a key role in the disease progression of cartilage, but our understanding of how cartilage compositional measurements respond to acute compressive loading in-vivo is limited. Ten healthy subjects were scanned at two timepoints (7 ± 3 days apart) with a 3 T magnetic resonance imaging (MRI) scanner. Scanning sessions included T1ρ and T2* acquisitions of each knee in two conditions: unloaded (traditional MRI setup) and loaded in compression at 40 % bodyweight as applied by an MRI-compatible loading device. T1ρ and T2* parameters were quantified for contacting cartilage (tibial and femoral) and non-contacting cartilage (posterior femoral condyle) regions. Significant effects of load were found in contacting regions for both T1ρ and T2*. The effect of load (loaded minus unloaded) in femoral contacting regions ranged from 4.1 to 6.9 ms for T1ρ, and 3.5 to 13.7 ms for T2*, whereas tibial contacting regions ranged from −5.6 to −1.7 ms for T1ρ, and −2.1 to 0.7 ms for T2*. Notably, the responses to load in the femoral and tibial cartilage revealed opposite effects. No significant differences were found in response to load between the two visits. This is the first study that analyzed the effects of acute loading on T1ρ and T2* measurements in human femoral and tibial cartilage separately. The results suggest the effect of acute compressive loading on T1ρ and T2* was: 1) opposite in the femoral and tibial cartilage; 2) larger in contacting regions than in non-contacting regions of the femoral cartilage; and 3) not different visit-to-visit.

Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms.

Local advanced rectal cancer (LARC) poses significant treatment challenges due to its location and high recurrence rates. Accurate early detection is vital for treatment planning. With magnetic resonance imaging (MRI) being resource-intensive, this study explores using artificial intelligence (AI) to interpret computed tomography (CT) scans as an alternative, providing a quicker, more accessible diagnostic tool for LARC. In this retrospective study, CT images of 1070 T3-4 rectal cancer patients from 2010 to 2022 were analyzed. AI models, trained on 739 cases, were validated using two test sets of 134 and 197 cases. By utilizing techniques such as nonlocal mean filtering, dynamic histogram equalization, and the EfficientNetB0 algorithm, we identified images featuring characteristics of a positive circumferential resection margin (CRM) for the diagnosis of locally advanced rectal cancer (LARC). Importantly, this study employs an innovative approach by using both hard and soft voting systems in the second stage to ascertain the LARC status of cases, thus emphasizing the novelty of the soft voting system for improved case identification accuracy. The local recurrence rates and overall survival of the cases predicted by our model were assessed to underscore its clinical value. The AI model exhibited high accuracy in identifying CRM-positive images, achieving an area under the curve (AUC) of 0.89 in the first test set and 0.86 in the second. In a patient-based analysis, the model reached AUCs of 0.84 and 0.79 using a hard voting system. Employing a soft voting system, the model attained AUCs of 0.93 and 0.88, respectively. Notably, AI-identified LARC cases exhibited a significantly higher five-year local recurrence rate and displayed a trend towards increased mortality across various thresholds. Furthermore, the model's capability to predict adverse clinical outcomes was superior to those of traditional assessments. AI can precisely identify CRM-positive LARC cases from CT images, signaling an increased local recurrence and mortality rate. Our study presents a swifter and more reliable method for detecting LARC compared to traditional CT or MRI techniques.

Iterative Temporal-spatial Transformer-based Cardiac T1 Mapping MRI Reconstruction

The precise reconstruction of accelerated magnetic resonance imaging (MRI) brings about notable advantages, such as enhanced diagnostic precision and decreased examination costs. In contrast, traditional cardiac MRI necessitates repetitive acquisitions across multiple heartbeats, resulting in prolonged acquisition times. Significant strides have been made in accelerating MRI through deep learning-based reconstruction methods. However, these existing methods encounter certain limitations: (1) The intricate nature of heart reconstruction involving multiple complex time-series data poses a challenge in exploring nonlinear dependencies between temporal contexts. (2) Existing research often overlooks weight sharing in iterative frameworks, impeding the effective capturing of non-local information and, consequently, limiting improvements in model performance. In order to improve cardiac MRI reconstruction, we propose a novel temporal-spatial transformer with a strategy in this study. Based on the multi-level encoder and decoder transformer architecture, we conduct multi-level spatiotemporal information feature aggregation over several adjacent views, that create nonlinear dependencies among features and efficiently learn important information among adjacent cardiac temporal frames. Additionally, in order to improve contextual awareness between neighboring views, we add cross-view attention for temporal information fusion. Furthermore, we introduce an iterative strategy for training weights during the reconstruction process, which improves feature fusion in critical locations and reduces the number of computations required to calculate global feature dependencies. Extensive experiments have demonstrated the substantial superiority of this procedure over the most advanced techniques, suggesting that it has broad potential for clinical use.

From strength to precision: A systematic review exploring the clinical utility of 7-Tesla magnetic resonance imaging in abdominal imaging.

After approval for clinical use in 2017 early investigations of ultra-high-field abdominal magnetic resonance imaging (MRI) have demonstrated the feasibility as well as diagnostic capabilities of liver, kidney, and prostate MRI at 7-Tesla. However, the elevation of the field strength to 7-Tesla not only brought advantages to abdominal MRI but also presented considerable challenges and drawbacks, primarily stemming from heightened artifacts and limitations in Specific Absorption Rate, etc. Furthermore, evidence in the literature is relatively scarce concerning human studies in comparison to phantom/animal studies which necessitates an investigation into the evidence so far in humans and summarizing all relevant evidence. To offer a comprehensive overview of current literature on clinical abdominal 7T MRI that emphasizes current trends, details relevant challenges, and provides a concise set of potential solutions. This systematic review adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A PubMed search, utilizing Medical Subject Headings terms such as "7-Tesla" and organ-specific terms, was conducted for articles published between January 1, 1985, and July 25, 2023. Eligibility criteria included studies exploring 7T MRI for imaging human abdominal organs, encompassing various study types (in-vivo/ex-vivo, method development, reviews/meta-analyses). Exclusion criteria involved animal studies and those lacking extractable data. Study selection involved initial identification via title/abstract, followed by a full-text review by two researchers, with discrepancies resolved through discussion. Data extraction covered publication details, study design, population, sample size, 7T MRI protocol, image characteristics, endpoints, and conclusions. The systematic review included a total of 21 studies. The distribution of clinical 7T abdominal imaging studies revealed a predominant focus on the prostate (n = 8), followed by the kidney (n = 6) and the hepatobiliary system (n = 5). Studies on these organs, and in the pancreas, demonstrated clear advantages at 7T. However, small bowel studies showed no significant improvements compared to traditional MRI at 1.5T. The majority of studies evaluated originated from Germany (n = 10), followed by the Netherlands (n = 5), the United States (n = 5), Austria (n = 2), the United Kingdom (n = 1), and Italy (n = 1). Further increase of abdominal clinical MRI field strength to 7T demonstrated high imaging potential, yet also limitations mainly due to the inhomogeneous radiofrequency (RF) excitation field relative to lower field strengths. Hence, further optimization of dedicated RF coil elements and pulse sequences are expected to better optimize clinical imaging at high magnetic field strength.

Low-Field, Low-Cost, Point-of-Care Magnetic Resonance Imaging.

Low-field magnetic resonance imaging (MRI) has recently experienced a renaissance that is largely attributable to the numerous technological advancements made in MRI, including optimized pulse sequences, parallel receive and compressed sensing, improved calibrations and reconstruction algorithms, and the adoption of machine learning for image postprocessing. This new attention on low-field MRI originates from a lack of accessibility to traditional MRI and the need for affordable imaging. Low-field MRI provides a viable option due to its lack of reliance on radio-frequency shielding rooms, expensive liquid helium, and cryogen quench pipes. Moreover, its relatively small size and weight allow for easy and affordable installation in most settings. Rather than replacing conventional MRI, low-field MRI will provide new opportunities for imaging both in developing and developed countries. This article discusses the history of low-field MRI, low-field MRI hardware and software, current devices on the market, advantages and disadvantages, and low-field MRI's global potential. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Decay in Entorhinal White Matter/Gray Matter contrast in Alzheimer’s Disease Patients is reduced by 40Hz sensory stimulation

AbstractBackgroundPrevious research has shown that white matter (WM) and gray matter (GM) tissues exhibit regionally selective pathological changes in Alzheimer’s disease (AD). The contrast between WM and GM signal intensities (WM/GM contrast) measured by magnetic resonance imaging (MRI) has previously been proposed as a method for quantifying these changes. It has been shown that WM/GM contrast can provide sensitive, independent, and distinct information about pathologies compared with traditional MRI measures. Studies have also shown, including entorhinal WM/GM contrast to traditional MRI measures resulted in better discrimination between AD and controls. We previously demonstrated that 40Hz auditory‐visual stimulation therapy reduced WM atrophy and myelin loss in AD patients. The present study quantified the effects of the therapy on the WM/GM contrast in the entorhinal region.MethodThis study used neuroimaging data from Overture, a phase I/II randomized, placebo‐controlled feasibility study (NCT03556280) in patients on the AD spectrum. Treatment group received 40Hz gamma sensory stimulation daily at home for six months, whereas placebo group received sham stimulation. Structural MRI was obtained at baseline, month 3 and month 6 visits using 1.5 Tesla scanner. FreeSurfer was used to process T1 MRI including entorhinal parcellation and cortical reconstructions for 38 participants (66% Treatment, 34% Placebo). For each vertex, WM signal intensity (WMI) is sampled at 1mm below, and GM signal intensity (GMI) is sampled at 30% of cortical thickness above the WM/GM border. WM/GM contrast was calculated as 100*(WMI–GMI)/(0.5*(WMI+GMI)) %. Bayesian linear mixed effects modeling was used to model changes in WM/GM contrast in the entorhinal region.ResultFollowing 6 months of treatment, we observed that the treatment group demonstrated 0.06±0.99% decrease and the placebo group demonstrated 1.26±1.04% decrease in WM/GM contrast (p&lt;0.022).ConclusionWe found that six months of 40Hz gamma sensory therapy reduced the decline in WM/GM contrast in the entorhinal region. Since WM/GM contrast is associated with AD severity, and the entorhinal region is of particular interest in the early stages of AD, early intervention of the therapy may result in enhanced tissue preservation and reduced neurological damage.

Modern neuroimaging criteria for diagnosing multiple sclerosis in children

The article presents a clinical case of multiple sclerosis (MS) in a 10-year-old female patient, the use of one of the advanced magnetic resonance imaging (MRI) techniques, namely proton magnetic resonance spectroscopy (1H-MRS), in the diagnosis of MS. MRI is an integral part of the diagnostic algorithm for patients with multiple sclerosis according to the McDonald criteria, which were last updated in 2017. In addition, MRI allows assessing the course of the disease, serving as a criterion for confirming the aggravation of the pathological process. Although the McDonald criteria are applied in adults, they are also used to diagnose MS in children, and MS in children has its own particularities. The McDonald criteria include traditional (conventional) MRI sequences: T2-weighted images, T1-weighted images, post-contrast T1-weighted images. With the rapid development of MRI, cutting-edge (advanced) MRI techniques have emerged that allow for a better understanding of changes in the brain matter in MS: brain volumetry, magnetization transfer imaging, cortical imaging, diffusion tensor imaging, 1H-MRS, susceptibility-weighted imaging. In particular, 1H-MRS allows us to assess the degree of white matter integrity by detecting increased resonance of myelin breakdown metabolites. The studies have shown that changes in MS have their own specificity, but there is no consensus on this issue. Currently, there are no agreed guidelines that would include advanced MRI techniques, but we believe they will allow us to better understand the course of MS and to predict the development of the disease. This approach will improve the management of MS in both adults and children and quality of their life.

Comparison of standard T2-weighted turbo spin echo and volumetric interpolated breath-hold examination magnetic resonance imaging sequences in the assessment of articular process dysplasia in Pug dogs with thoracolumbar myelopathy.

A retrospective study to compare the classification, as normal, hypoplastic or aplastic, of thoracic (T10-T13) caudal articular process (CAP) morphology in Pug dogs with a thoracolumbar myelopathy as normal, hypoplastic or aplastic, between T2 weighted Turbo Spin Echo (T2W-TSE), in sagittal and transverse planes, and Volumetric Interpolated Breath-hold Examination (VIBE) Magnetic Resonance Imaging (MRI) sequences, in comparison to Computed Tomography (CT). We hypothesized a stronger agreement for VIBE in comparison to T2W-TSE. Diagnostic accuracy of T2W-TSE was inferior to VIBE for aplastic (60%, 95% CI 0.561-0.639 vs. 78%, 95%CI 0.744-0.815) hypoplastic (44%, 95%CI 0.427-0.452 vs. 62.5%, 95%CI 0.595-0.655) and normal CAP (70%, 95%CI 0.655-0.744 vs. 87%, 95%CI 0.848-0.892). Superior accuracy of classification using VIBE vs. T2W-TSE sequences using the McNemar Chi squared test was significant for aplastic (p = 0.0002) and normal CAP (p = 0.004). VIBE sequences had a sensitivity of 96% and specificity of 75% to detect CAP abnormality and with T2W-TSE imaging sensitivity 81% and specificity of 75%. Three-dimensionally reconstructable VIBE sequences were significantly more accurate than traditional T2W-TSE MRI sequences in classifying CAP morphology, which should reduce the need for CT for pre-operative assessment.

EVOLVE: Learning volume-adaptive phases for fast 3D magnetic resonance scan and image reconstruction

Compared with 2D Magnetic Resonance Imaging (MRI), 3D MRI is more powerful for generating high resolution images and visualizing small anatomical structures. However, 3D MRI acquisition is much more time-consuming due to the significantly larger number of phase encoding steps, which is directly proportional to the acquisition time. This paper proposes to select a volume-adaptive small subset of phases to accelerate 3D MRI scans and accurately reconstruct 3D images from the corresponding undersampled 3D k-space data. To avoid the delays caused by computationally expensive yet high-performance volume-adaptive phase selection, we propose a strategy of selecting multiple phases based on sampled slices from the volume during idle time within the repetition time (TR). To enhance the performance of phase selection, we propose a novel three-directional cross-attention phase selection network. Additionally, to improve the reconstruction performance, we introduce a three-directional slice-wise volume reconstruction. To the best of our knowledge, the proposed method, which we called EVOLVE (learning volume-adaptive phases), is the first work that learns volume-adaptive phases for fast 3D MRI. The extensive experimental results on a large-scale 3D MRI dataset at various acceleration factors demonstrate the substantial performance improvement in terms of image reconstruction achieved by using the EVOLVE method for phase selection compared to traditional learning free 3D MRI phase selection methods.

Paper 24: Reliability of Posterior Tibial Slope Measurement on Magnetic Resonance Imaging Versus Computed Tomography

Objectives: Increased posterior tibial slope (PTS) has been linked to higher rates of anterior cruciate ligament (ACL) injury and ACL graft failure post-reconstruction. In recent years, more specific investigations have delineated distinct roles of the medial tibial plateau (MTP) and lateral tibial plateau (LTP) geometries on anteroposterior and rotatory stability of the knee. While the diagnostic workup of patients with ACL insufficiency typically consists of radiographs and magnetic resonance imaging (MRI), computed tomography (CT), which is the gold standard for evaluating the osseous geometry of the proximal tibia, is not routinely obtained. With increased understanding of the roles of the MTP and LTP geometries on tibiofemoral kinematics, accurate sagittal slope measurements of both the MTP and LTP will be crucial for further defining the treatment algorithm for ACL insufficiency. It is currently unknown whether traditional MRI can be used to accurately measure PTS and how well these measurements correlate to those on CT. Therefore, the purpose of this study was to compare PTS measurements of the MTP and LTP on MRI versus CT. Methods: After Institutional Review Board approval was obtained, an institutional picture archiving and communication system imaging database was retrospectively queried for patients that received concurrent MRI and CT imaging of the same knee within a one-year interval. Knees with significant arthrosis (Kellgren-Lawrence grade &gt;2), deformity, proximal tibia fracture, or artifact that obscured visualization of proximal tibia landmarks were excluded. On paired MRI and CT studies, PTS of the MTP and LTP were measured using a previously described, validated method (Hudek et al, Clin Orthop Relat Res, 2009) by two independent raters (Figure 1). Interrater reliability of PTS measurements was assessed using the intraclass correlation coefficient (ICC). Intermethod agreement between MRI and CT measurements was assessed using the ICC and Bland-Altman analysis. Results: A total of 41 knees in 40 patients (mean age, 37.9±13.4 years, 78.0% male) met final inclusion criteria. Interrater reliability was moderate for MRI measurements (ICC = 0.62-0.73) and high for CT measurements (ICC = 0.83-0.86). Mean measurements of PTS at the MTP were 3.3±2.8° (range, -4.7-8.4°) and 3.7±3.1° (range, -3.8-10.4°) on MRI and CT, respectively. Mean measurements of PTS at the LTP were 4.5±3.8° (range, -8.0-10.6°) and 5.2±3.6° (range, -3.6-15.7°) on MRI and CT, respectively. The mean absolute differences in PTS measurements between MRI and CT were 2.6±2.1° and 2.6±2.6° at the MTP and LTP, respectively. Poor intermethod agreement between MRI and CT was observed at the MTP (ICC = 0.31-0.41), while moderate intermethod agreement was observed at the LTP (ICC = 0.61-0.62). Bland-Altman plots demonstrated high variability and minimal bias of PTS measurements on MRI compared to CT (-0.9° [95% Limit of Agreement (LOA) -7.5-5.6°] for MTP; -1.0° [95% LOA -7.2-5.2°] for LTP) (Figure 2). Conclusions: With increased understanding of the roles of the medial and lateral tibial plateau geometries on tibiofemoral stability in the setting of ACL insufficiency, accurate measurements of PTS are necessary to guide treatment decisions. In this study, PTS measurements on MRI demonstrated lower interrater reliability compared to PTS measurements on CT. Poor-to-moderate agreement was observed for measurements of PTS of the MTP and LTP between paired MRI and CT studies, with high variability and minimal bias. Therefore, measurements of medial and lateral PTS may not be reliable on traditional MRI.

Poster 141: Does the Traditional TT-TG Distance Accurately Reflect the Relative Position of the Tibial Tubercle to the Trochlear Groove? A Three-Dimensional Analysis

Objectives: The tibial tubercle to trochlear groove (TT-TG) distance often guides surgical decision- making for patellar instability. Traditional TT-TG measurements are limited by interrater reliability and lack of consideration for variable anatomy throughout flexion. The purpose of this study was to compare the traditional TT-TG measurement to three-dimensional measures generated from automated trochlear groove detection with sequential radial slices on magnetic resonance imaging (MRI). Methods: Twenty-four knees (4 healthy controls, 20 patellar instability patients) were segmented into three-dimensional distal femur and tibia models. A custom-designed program was employed to detect the trochlear groove through its course based on the shortest distance to the origin in a polar (radial) axis defined by the distal femoral condyles. The program also identified the surface projection of the tibial tubercle center of mass. The medial-lateral distance between the trochlear groove and tibial tubercle was calculated for the first 45 degrees of the detected groove to generate radial TT-TG (rTT-TG) values. The mean and maximum rTT-TG values were compared to the traditional TT-TG MRI measurement performed by one grader. Results: Mean rTT-TG values were significantly greater than traditional measurements (21.8 mm versus 17.9 mm, p=.003) with moderate correlation between measurements (Pearson r = 0.59, p=.002). In Dejour C and D dysplasia, the mean rTT-TG measurements trended towards being greater than traditional measurements (C: 24.3 mm versus 16.6 mm; D: 28.5 mm versus 21.8 mm; p=0.057 for both comparisons). Dejour C dysplasia demonstrated a significantly greater maximum rTT-TG than the traditional measurement (27.9 mm versus 16.6 mm, p=0.029), and Dejour D dysplasia demonstrated a similar trend (30.2 mm versus 21.8 mm; p=0.057). Conclusions: Traditional TT-TG distances may underestimate the true medial-lateral offset between trochlear groove and tibial tubercle, particularly in patients with high- grade dysplasia. This represents a potential alternative to the current standard measurement techniques which may improve measurement reliability by automation. Our study suggests that utilizing 3D radial TT-TG values via automated measurement may more accurately portray the variable anatomy of the trochlear entrance and offer a standardized alternative to a frequently used measurement to overcome reader reliability.

Abbreviated whole‐body MRI for follow‐up in pediatric lymphoma: results of a multicenter prospective study

Introduction: The evaluation of treatment response in pediatric lymphoma is critical for making informed treatment decisions and improving patient outcomes. Positron emission tomography/computed tomography (PET/CT) and traditional whole-body magnetic resonance imaging (MRI) are the most commonly used imaging modalities for assessing treatment response in lymphoma. However, PET/CT exposes patients to ionizing radiation, and its high cost and limited availability may limit its use for routine follow-up. Traditional whole-body MRI is a noninvasive alternative, but it has some limitations, including prolonged acquisition time and the need for sedation, which can be burdensome for pediatric patients. Abbreviated whole-body MRI (AWBMRI) has emerged as a promising imaging modality for monitoring treatment response in pediatric lymphoma. AWBMRI requires less time and is typically performed without sedation, making it a potentially more feasible and patient-friendly alternative to traditional MRI for routine follow-up. Methods: A multicenter prospective study was conducted, enrolling 103 pediatric patients aged 0 to 18 years with pathologically confirmed lymphoma. All patients underwent both PET/CT and abbreviated whole-body MRI at baseline and during follow-up. Image quality of abbreviated whole-body MRI was evaluated using a 5-point Likert scale, and interobserver agreement was assessed. Sensitivity, specificity, and accuracy of abbreviated whole-body MRI for detecting residual or recurrent disease were calculated, with PET/CT used as the reference standard. Results: Image quality of abbreviated whole-body MRI was rated as good or excellent in 97% of cases, with high interobserver agreement. Abbreviated whole-body MRI demonstrated a sensitivity of 90%, specificity of 96%, and accuracy of 94% for detecting residual or recurrent disease, with excellent agreement between abbreviated whole-body MRI and PET/CT findings. Keyword: Other No conflicts of interests pertinent to the abstract.

Navigating the challenges of neuro-oncology in Africa: addressing diagnostic and treatment barriers in the region: a correspondence

Navigating the challenges of neuro-oncology in Africa: addressing diagnostic and treatment barriers in the region: a correspondence

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T 2 mapping and comparable results to conventional methods were obtained in the human brain. Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Nanoparticle-Based Activatable MRI Probes for Disease Imaging and Monitoring

Traditional diagnosis relies on identifying anatomical abnormality, which offers a stage for various anatomical imaging techniques, such as X-ray computed tomography (CT), ultrasonic imaging, and magnetic resonance imaging (MRI). The good capacity of providing anatomical details, especially for soft tissues, popularizes the clinical use of MRI. However, as the understanding of various diseases reaches the molecular level, it is gradually accepted that molecular anomaly often precedes anatomical abnormality. Therefore, molecular imaging, which is aimed at gathering various molecular information in organisms via imaging, starts to gain momentum. Unfortunately, traditional MRI is not capable of molecular imaging. As a result, there is an urgent demand for probes that enable MRI to “see” molecules. A promising design strategy for these probes is to elicit a signal change triggered by the presence of molecular targets, i.e. activation. Benefiting from the rapid development of nanotechnology, a number of nanoparticle-based activatable MRI probes have been developed for molecular imaging. This review summarizes recent advances of activatable MRI nanoprobes for imaging pathological characteristics of cancer, inflammation, and neurodegenerative diseases, with a focus on the design strategies and applications of these probes. In addition, the prospects and challenges of activatable MRI nanoprobes are also discussed.

The emerging potential of quantitative MRI biomarkers for the early prediction of brain metastasis response after stereotactic radiosurgery: a scoping review.

At present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS). We systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans. We identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans 2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE ) were confirmed to be indicative of the therapeutic effect of SRS for BMs. Quantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer

BackgroundIt has been believed that traditional handcrafted radiomic features extracted from magnetic resonance imaging (MRI) of tumors are normally shallow and low-ordered. Recent advancement in deep learning technology shows that the high-order deep radiomic features extracted automatically from tumor images can capture tumor heterogeneity in a more efficient way. We hypothesize that MRI-based deep radiomic phenotypes have significant associations with molecular profiles of breast cancer tumors. We aim to identify deep radiomic features (DRFs) from MRI, evaluate their significance in predicting breast cancer (BC) clinical characteristics and explore their associations with multi-level genomic factors.MethodsA denoising autoencoder was built to retrospectively extract 4,096 DRFs from 110 BC patients’ MRI. Visualization and clustering were applied to these DRFs. Linear Mixed Effect models were used to test their associations with multi-level genomic features (GFs) (risk genes, gene signatures, and biological pathway activities) extracted from the same patients’ mRNA expression profile. A Least Absolute Shrinkage and Selection Operator model was used to identify the most predictive DRFs for each clinical characteristic (tumor size (T), lymph node metastasis (N), estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status).ResultsThirty-six conventional radiomic features (CRFs) for 87 of the 110 BC patients provided by a previous study were used for comparison. More than 1,000 DRFs were associated with the risk genes, gene signatures, and biological pathways activities (adjusted P-value < 0.05). DRFs produced better performance in predicting T, N, ER, PR, and HER2 status (AUC > 0.9) using DRFs. These DRFs showed significant powers of stratifying patients, linking to relevant biological and clinical characteristics. As a contrast, only eight risk genes were associated with CRFs. The RFs performed worse in predicting clinical characteristics than DRFs.ConclusionsThe deep learning-based auto MRI features perform better in predicting BC clinical characteristics, which are more significantly associated with GFs than traditional semi-auto MRI features. Our radiogenomic approach for identifying MRI-based imaging signatures may pave potential pathways for the discovery of genetic mechanisms regulating specific tumor phenotypes and may enable a more rapid innovation of novel imaging modalities, hence accelerating their translation to personalized medicine.