Magnetic Resonance Imaging Acquisition Research Articles

Qualitative and Quantitative Comparison of Respiratory Triggered Reduced Field-of-View (FOV) Versus Full FOV Diffusion Weighted Imaging (DWI) in Pancreatic Pathologies

To investigate the effects of a reduced field-of-view (rFOV) acquisition in diffusion-weighted magnetic resonance imaging of the pancreas. We enrolled 153 patients who underwent routine clinical MRI work-up including respiratory-triggered diffusion-weighted single-shot echo-planar imaging (DWI) with full field-of-view (fFOV, 3 × 3 × 4 mm3 voxel size) and reduced field-of-view (rFOV, 2.5 × 2.5 × 3 mm3 voxel size) for suspected pancreatic pathology. Two experienced radiologists were asked to subjectively rate (Likert Scale 1-4) image quality (overall image quality, lesion conspicuity, anatomical detail, artifacts). In addition, quantitative image parameters were assessed (apparent diffusion coefficient, apparent signal to noise ratio, apparent contrast to noise ratio [CNR]). All subjective metrics of image quality were rated in favor of rFOV DWI images compared to fFOV DWI images with substantial-to-high inter-rater reliability. Calculated ADC values of normal pancreas, pancreatic pathologies and reference tissues revealed no differences between both sequences. Whereas the apparent signal to noise ratio was higher in fFOV images, apparent CNR was higher in rFOV images. rFOV DWI provides higher image quality and apparent CNR values, favorable in the analysis of pancreatic pathologies.

Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning.

Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in clinical magnetic resonance imaging (MRI). Recently, the spatially localized atlas network tiles (SLANT) can effectively segment whole brain, non-contrast T1w MRI with 132 volumetric labels. Transfer learning (TL) is a commonly used domain adaptation tool to update the neural network weights for local factors, yet risks degradation of performance on the original validation/test cohorts. Approach: We explore TL using unlabeled clinical data to address these concerns in the context of adapting SLANT to scanning protocol variations. We optimize whole-brain segmentation on heterogeneous clinical data by leveraging 480 unlabeled pairs of clinically acquired T1w MRI with and without intravenous contrast. We use labels generated on the pre-contrast image to train on the post-contrast image in a five-fold cross-validation framework. We further validated on a withheld test set of 29 paired scans over a different acquisition domain. Results: Using TL, we improve reproducibility across imaging pairs measured by the reproducibility Dice coefficient (rDSC) between the pre- and post-contrast image. We showed an increase over the original SLANT algorithm (rDSC 0.82 versus 0.72) and the FreeSurfer v6.0.1 segmentation pipeline ( ). We demonstrate the impact of this work decreasing the root-mean-squared error of volumetric estimates of the hippocampus between paired images of the same subject by 67%. Conclusion: This work demonstrates a pipeline for unlabeled clinical data to translate algorithms optimized for research data to generalize toward heterogeneous clinical acquisitions.

Semi-Automated and Direct Localization and Labeling of EEG Electrodes Using MR Structural Images for Simultaneous fMRI-EEG.

Electroencephalography (EEG) source reconstruction estimates spatial information from the brain’s electrical activity acquired using EEG. This method requires accurate identification of the EEG electrodes in a three-dimensional (3D) space and involves spatial localization and labeling of EEG electrodes. Here, we propose a new approach to tackle this two-step problem based on the simultaneous acquisition of EEG and magnetic resonance imaging (MRI). For the step of spatial localization of electrodes, we extract the electrode coordinates from the curvature of the protrusions formed in the high-resolution T1-weighted brain scans. In the next step, we assign labels to each electrode based on the distinguishing feature of the electrode’s distance profile in relation to other electrodes. We then compare the subject’s electrode data with template-based models of prelabeled distance profiles of correctly labeled subjects. Based on this approach, we could localize EEG electrodes in 26 head models with over 90% accuracy in the 3D localization of electrodes. Next, we performed electrode labeling of the subjects’ data with progressive improvements in accuracy: with ∼58% accuracy based on a single EEG-template, with ∼71% accuracy based on 3 EEG-templates, and with ∼76% accuracy using 5 EEG-templates. The proposed semi-automated method provides a simple alternative for the rapid localization and labeling of electrodes without the requirement of any additional equipment than what is already used in an EEG-fMRI setup.

Assessment of Gadobutrol Safety in Combination with Ionizing Radiation Using a Preclinical MRI-Guided Radiotherapy Model.

MR-linac technology enhances the precision of therapeutic radiation by clarifying the tumor-normal tissue interface and provides the potential for adaptive treatment planning. Accurate delineation of tumors on diagnostic magnetic resonance imaging (MRI) frequently requires gadolinium-based contrast agents (GBCAs). Despite generally being considered safe, previous literature suggests that GBCAs are capable of contrast-induced acute kidney injury (AKI). It is unclear if the risk for AKI is enhanced when GBCAs are administered concurrently with ionizing radiotherapy. During irradiation, gadolinium may be liberated from its chelator which may induce AKI. The goal of this work was to determine if radiation combined with GBCAs increased the incidence of AKI. Using a preclinical MRI-guided irradiation system, where MRI acquisitions and radiation delivery are performed in rapid succession, tumor-bearing mice with normal kidney function were injected with GBCA and treated with 2, 8 or 18 Gy irradiation. Renal function was assessed on days three and seven postirradiation to assess for AKI. No clinically relevant changes in blood urea nitrogen and creatinine were observed in any combination of GBCA and radiation dose. From these data, we conclude that GBCA in combination with radiation does not increase the risk for AKI in mice. Additional investigation of multiple doses of GBCA administered concurrently with irradiation is warranted to evaluate the risk of chronic kidney injury.

Integration of spatial distortion effects in a 4D computational phantom for simulation studies in extra-cranial MRI-guided radiation therapy: Initial results.

Spatial distortions in magnetic resonance imaging (MRI) are mainly caused by inhomogeneities of the static magnetic field, nonlinearities in the applied gradients, and tissue-specific magnetic susceptibility variations. These factors may significantly alter the geometrical accuracy of the reconstructed MR image, thus questioning the reliability of MRI for guidance in image-guided radiation therapy. In this work, we quantified MRI spatial distortions and created a quantitative model where different sources of distortions can be separated. The generated model was then integrated into a four-dimensional (4D) computational phantom for simulation studies in MRI-guided radiation therapy at extra-cranial sites. A geometrical spatial distortion phantom was designed in four modules embedding laser-cut PMMA grids, providing 3520 landmarks in a field of view of (345×260×480)mm3 . The construction accuracy of the phantom was verified experimentally. Two fast MRI sequences for extra-cranial imaging at 1.5T were investigated, considering axial slices acquired with online distortion correction, in order to mimic practical use in MRI-guided radiotherapy. Distortions were separated into their sources by acquisition of images with gradient polarity reversal and dedicated susceptibility calculations. Such a separation yielded a quantitative spatial distortion model to be used for MR imaging simulations. Finally, the obtained spatial distortion model was embedded into an anthropomorphic 4D computational phantom, providing registered virtual CT/MR images where spatial distortions in MRI acquisition can be simulated. The manufacturing accuracy of the geometrical distortion phantom was quantified to be within 0.2mm in the grid planes and 0.5mm in depth, including thickness variations and bending effects of individual grids. Residual spatial distortions after MRI distortion correction were strongly influenced by the applied correction mode, with larger effects in the trans-axial direction. In the axial plane, gradient nonlinearities caused the main distortions, with values up to 3mm in a 1.5T magnet, whereas static field and susceptibility effects were below 1mm. The integration in the 4D anthropomorphic computational phantom highlighted that deformations can be severe in the region of the thoracic diaphragm, especially when using axial imaging with 2D distortion correction. Adaptation of the phantom based on patient-specific measurements was also verified, aiming at increased realism in the simulation. The implemented framework provides an integrated approach for MRI spatial distortion modeling, where different sources of distortion can be quantified in time-dependent geometries. The computational phantom represents a valuable platform to study motion management strategies in extra-cranial MRI-guided radiotherapy, where the effects of spatial distortions can be modeled on synthetic images in a virtual environment.

Minimizing echo and repetition times in magnetic resonance imaging using a double half-echo k-space acquisition and low-rank reconstruction.

Sampling k-space asymmetrically (ie, partial Fourier sampling) in the readout direction is a common way to reduce the echo time (TE) during magnetic resonance image acquisitions. This technique requires overlap around the center of k-space to provide a calibration region for reconstruction, which limits the minimum fractional echo to ~60% before artifacts are observed. The present study describes a method for reconstructing images from exact half echoes using two separate acquisitions with reversed readout polarity, effectively providing a full line of k-space without additional data around central k-space. This approach can benefit sequences or applications that prioritize short TE, short inter-echo spacing or short repetition time. An example of the latter is demonstrated to reduce banding artifacts in balanced steady-state free precession.

Characterization of a preclinical PET insert in a 7 tesla MRI scanner: beyond NEMA testing

This study evaluates the performance of the Bruker positron emission tomograph (PET) insert combined with a BioSpec 70/30 USR magnetic resonance imaging (MRI) scanner using the manufacturer acceptance protocol and the NEMA NU 4–2008 for small animal PET. The PET insert is made of 3 rings of 8 monolithic LYSO crystals (50 × 50 × 10 mm3) coupled to silicon photomultipliers (SiPM) arrays, conferring an axial and transaxial FOV of 15 cm and 8 cm. The MRI performance was evaluated with and without the insert for the following radiofrequency noise, magnetic field homogeneity and image quality. For the PET performance, we extended the NEMA protocol featuring system sensitivity, count rates, spatial resolution and image quality to homogeneity and accuracy for quantification using several MRI sequences (RARE, FLASH, EPI and UTE). The PET insert does not show any adverse effect on the MRI performances. The MR field homogeneity is well preserved (Diameter Spherical Volume, for 20 mm of 1.98 ± 4.78 without and −0.96 ± 5.16 Hz with the PET insert). The PET insert has no major effect on the radiofrequency field. The signal-to-noise ratio measurements also do not show major differences. Image ghosting is well within the manufacturer specifications (<2.5%) and no RF noise is visible. Maximum sensitivity of the PET insert is 11.0% at the center of the FOV even with simultaneous acquisition of EPI and RARE. PET MLEM resolution is 0.87 mm (FWHM) at 5 mm off-center of the FOV and 0.97 mm at 25 mm radial offset. The peaks for true/noise equivalent count rates are 410/240 and 628/486 kcps for the rat and mouse phantoms, and are reached at 30.34/22.85 and 27.94/22.58 MBq. PET image quality is minimally altered by the different MRI sequences. The Bruker PET insert shows no adverse effect on the MRI performance and demonstrated a high sensitivity, sub-millimeter resolution and good image quality even during simultaneous MRI acquisition.

Same day comparison of PET/CT and PET/MR in patients with cardiac sarcoidosis

BackgroundInflammatory cardiac disorders, in particular, sarcoidosis, play an important role in left ventricular dysfunction, conduction abnormalities, and arrhythmias. In this study, we compared the imaging characteristics and diagnostic information obtained when patients were imaged sequentially with PET/CT and then with hybrid PET/MRI on the same day following a single 18F-FDG injection. MethodsTen patients with known or suspected sarcoidosis underwent imaging in sequence of (a) 99mTc-MIBI, (b) 18F-FDG with PET/CT, and (c) 18F-FDG with 3T PET/MRI. Images were compared quantitatively by determination of SUVmax and SUV on a voxel by voxel basis, and qualitatively by two experienced observers. ResultsWhen both platforms were compared quantitatively, similar data for the evaluation of enhanced 18F-FDG uptake were obtained. Qualitatively, there were (1) several instances of normal perfusion with delayed enhancement and/or focal 18F-FDG uptake, (2) comparable enhanced 18F-FDG uptake on PET/CT vs. PET/MRI, and (3) diversity in disease patterns with delayed enhancement only, increased 18F-FDG uptake only, or both. ConclusionIn this limited patient study, PET/CT and PET/MR provided similar diagnostic data for 18F-FDG uptake, and the concurrent acquisition of MR images provided further insight into the disease process.

Deep-learned time-signal intensity pattern analysis using an autoencoder captures magnetic resonance perfusion heterogeneity for brain tumor differentiation

Current image processing methods for dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) do not capture complex dynamic information of time-signal intensity curves. We investigated whether an autoencoder-based pattern analysis of DSC MRI captured representative temporal features that improves tissue characterization and tumor diagnosis in a multicenter setting. The autoencoder was applied to the time-signal intensity curves to obtain representative temporal patterns, which were subsequently learned by a convolutional neural network. This network was trained with 216 preoperative DSC MRI acquisitions and validated using external data (n = 43) collected with different DSC acquisition protocols. The autoencoder applied to time-signal intensity curves and clustering obtained nine representative clusters of temporal patterns, which accurately identified tumor and non-tumoral tissues. The dominant clusters of temporal patterns distinguished primary central nervous system lymphoma (PCNSL) from glioblastoma (AUC 0.89) and metastasis from glioblastoma (AUC 0.95). The autoencoder captured DSC time-signal intensity patterns that improved identification of tumoral tissues and differentiation of tumor type and was generalizable across centers.

Evaluation of an accelerated 3D modulated flip-angle technique in refocused imaging with an extended echo-train sequence with compressed sensing for imaging of the knee: comparison with routine 2D MRI sequences

To accelerate the acquisition of high-resolution magnetic resonance imaging (MRI) by using the three-dimensional (3D) matrix sequence with compressed sensing and to compare it with conventional two-dimensional (2D) proton-density (PD) and fast spin-echo (FSE) sequences. 3D matrix, 2D FSE, and PD sequences were acquired from 68 participants using 3 T magnetic resonance imaging (MRI). Two radiologists scored image quality independently on a four-point scale. The structural similarity index (SSIM), and signal- (SNRs) and contrast-to-noise ratios (CNRs) of different anatomical structures of the knee were assessed and compared between sequences using Wilcoxon signed-rank tests and Cohen's kappa. The median acquisition time reduction was 44.5%. There was a substantial to perfect agreement for the rating between the 3D matrix FSE and 2D FSE or PD sequences when evaluating cartilage, subchondral bone, and ligaments (κ=0.783-872, p>0.05). The mean SSIM values between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences was 0.994 and 0.971, respectively, which are acceptable. No significant differences were found in SNR between the 3D matrix FSE and 2D FSE, and between the 3D matrix PD and 2D PD sequences, even though the SNR appeared to be higher on routine 2D sequences. The CNR of subchondral bone-meniscus, subchondral bone-joint fluid, and meniscus-joint fluid did not differentiate significantly between the 3D matrix sequence and routine 2D sequences. 3D matrix reduced the acquisition time in routine clinical knee MRI without the loss in image quality, SNR, and CNR.

An information theory model for optimizing quantitative magnetic resonance imaging acquisitions

Acquisition parameter selection is currently performed empirically for many quantitative MRI (qMRI) acquisitions. Tuning parameters for different scan times, tissues, and resolutions requires some amount of trial and error. There is an opportunity to quantitatively optimize these acquisition parameters in order to minimize variability of quantitative maps and post-processing techniques such as synthetic image generation. The objective of this work is to introduce and evaluate a quantitative method for selecting parameters that minimize image variability. An information theory framework was developed for this purpose and applied to a 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) signal model for qMRI. In this framework, mutual information is used to measure the information gained by a measurement as a function of acquisition parameters, quantifying the information content of potential acquisitions and allowing informed parameter selection. The information theory framework was tested on artificial data generated from a representative mathematical phantom, measurements acquired on a qMRI multiparametric imaging standard phantom, and in vivo measurements in a human brain. The phantom measurements showed that higher mutual information calculated by the model correlated with smaller coefficient of variation in the reconstructed parametric maps, and in vivo measurements demonstrated that information-based calibration of acquisition parameters resulted in a decrease in parametric map variability consistent with model predictions.

Multi-shot acquisitions for stimulus-evoked spinal cord BOLD fMRI.

To demonstrate the feasibility of 3D multi-shot magnetic resonance imaging acquisitions for stimulus-evoked blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) in the human spinal cord in vivo. Two fMRI studies were performed at 3T. The first study was a hypercapnic gas challenge where data were acquired from healthy volunteers using a multi-shot 3D fast field echo (FFE) sequence as well as single-shot multi-slice echo-planar imaging (EPI). In the second study, another cohort of healthy volunteers performed an upper extremity motor task while fMRI data were acquired using a 3D multi-shot acquisition. Both 2D-EPI and 3D-FFE were shown to be sensitive to BOLD signal changes in the cervical spinal cord, and had comparable contrast-to-noise ratios in gray matter. FFE exhibited much less signal drop-out and weaker geometric distortions compared to EPI. In the motor paradigm study, the mean number of active voxels was highest in the ventral gray matter horns ipsilateral to the side of the task and at the spinal level associated with innervation of finger extensors. Highly multi-shot acquisition sequences such as 3D-FFE are well suited for stimulus-evoked spinal cord BOLD fMRI.

Ficoll as testing material for diffusion weighted imaging-quality assurance phantoms

PurposeThe aim of this work is to test the use of aqueous solutions of Ficoll®**, a highly branched polymer displaying crowding properties, to build a phantom suitable for Diffusion Weighted Imaging (DWI) in Magnetic Resonance Imaging (MRI). MethodsWe developed a test object made of a cylindrical plastic container with a precise geometrical arrangement suitable for measuring several samples at the same time. The container was designed to host single vials with variable geometry and number, and to fit inside common commercial head coils for MRI scanners.In our experiments, vials were filled with 8 aqueous solutions of Ficoll 70 and Ficoll 400 spanning a range of polymer concentration from 5 to 30% by weight. Vials containing ultra-pure water were also used as reference. Experiments were performed on both 1.5 and 3 T clinical scanners (GE, Philips and Siemens), under the conditions of a standard clinical examination. ResultsThe geometry of the phantom provided reduced imaging artifacts, especially image distortions at magnetic interfaces. We found that the Apparent Diffusion Coefficient (ADC) varied in the range of 0.00125–0.00223 mm2/s and decreased with Ficoll concentration. ADC vs Ficoll concentration exhibited a linear trend. Results were consistent over time and among different MRI clinical scanners, showing an average variability of 3% at 1.5 T and of 7.5% at 3 T. Moreover, no substantial difference was found between Ficoll 70 and 400. By varying Ficoll concentration, ADC can be modulated to approach tissue-mimicking values. Preliminary results for relaxation measurements proved that both T1 and T2 decreased with Ficoll concentration in the ranges 1.3–2.4 s and 150–800 ms respectively. ConclusionsIn this work, we propose a 3D phantom design based on the widespread crowding agent Ficoll, which is suitable for DWI quality assurance purposes in MRI acquisitions. Aqueous Ficoll solutions provide good performance in terms of stability, ease of preparation, and safety.

Dynamic MRI of plantar flexion: A comprehensive repeatability study of electrical stimulation-gated muscle contraction standardized on evoked force.

Quantification of skeletal muscle contraction in Magnetic Resonance Imaging (MRI) is a non-invasive method for studying muscle motion and deformation. The aim of this study was to evaluate the repeatability of quantitative measures such as strain, based on single slice dynamic MRI synchronized with neuromuscular electrical stimulation (NMES) and standardized to a similar relative force level across various individuals. Unilateral electrical stimulation of the triceps surae muscles was applied in eight volunteers during single-slice, three-directional phase contrast MRI acquisition at a 3T MRI scanner. To assess repeatability, the same process was executed on two different days by standardizing the stimulation aiming at evoking a fixed percentage of their maximal voluntary force in the same position. Except from the force, the effect of using the current as reference was evaluated on day two as a secondary acquisition. Finally, the presence of fatigue induced by NMES was assessed (on day one) by examining the difference between consecutive measurements. Strain maps were derived from the acquired slice at every time point; distribution of strain in the muscle and peak strain over the muscle of interest were evaluated for repeatability. It was found that fatigue did not have an appreciable effect on the results. The stimulation settings based on evoked force produced more repeatable results with respect to using the current as the only reference, with an intraclass correlation coefficient between different days of 0.95 for the former versus 0.88 for the latter. In conclusion, for repeatable strain imaging it is advisable to record the force output of the evoked contraction and use that for the standardization of the NMES setup rather than the current.

NeuroSmog: Determining the impact of air pollution on the developing brain - outline of the project protocol

Background: Recent studies have linked exposure to airborne particulate matter (PM) to neurodevelopmental outcomes but the findings are mixed and mechanisms are unclear. We aim to determine the impact of PM on the developing brain of schoolchildren in Poland, a European country characterized by very high levels of air pollution. Methods: During 2020-2021, 800 children aged 10 to 13 years will be recruited in a case-control study. Attention Deficit Hyperactivity Disorder (ADHD) cases will be sampled from psychological centres and clinics. Population-based controls will be sampled from schools. The study area will include 20 towns in southern Poland characterized by different PM levels. Behavioural testing will be done using a battery of tests, including the inhibition measuring Go-NoGo task and the Attention Network Task. Magnetic Resonance Imaging (MRI) acquisition will be conducted in line with the Human Connectome Project Adolescent Brain Cognitive Development protocols (resting-state functional MRI, Diffusion Tensor Imaging (DTI), T1/T2 structural MRI, one functional MRI task - Conditioned Approach Response Inhibition Task (CARIT)). Estimates of PM and other air pollutants will be modelled using a combination of land use regression, transport models, satellite observations and monitoring data. These will be assigned to prenatal and early postnatal residential addresses of children. Associations of PM with ADHD-related symptoms, intelligence quotient (IQ), attention, emotional and social function and the role of changes in specific brain regions will be assessed by mediation models, adjusted for confounders. Results and Discussion: This comprehensive four-year study will provide novel, in-depth understanding of the neurodevelopmental effects of PM.

Immediate effects of a single session of physical exercise on cognition and cerebral blood flow: A randomized controlled study of older adults

BackgroundRegular physical activity is beneficial for cognitive performance in older age. A single bout of aerobic physical exercise can transiently improve cognitive performance. Researchers have advanced improvements in cerebral circulation as a mediator of long-term effects of aerobic physical exercise on cognition, but the immediate effects of exercise on cognition and cerebral perfusion are not well characterized and the effects in older adults are largely unknown. MethodsForty-nine older adults were randomized to a 30-min aerobic exercise at moderate intensity or relaxation. Groups were matched on age and cardiovascular fitness (VO2 max). Average Grey Matter Blood Flow (GMBF), measured by a pulsed arterial-spin labeling (pASL) magnetic resonance imaging (MRI) acquisition, and working memory performance, measured by figurative n-back tasks with increasing loads were assessed before and 7 min after exercising/resting. ResultsAccuracy on the n-back task increased from before to after exercising/resting regardless of the type of activity. GMBF decreased after exercise, relative to the control (resting) group. In the exercise group, higher n-back performance after exercise was associated with lower GMBF in the right hippocampus, left medial frontal cortex and right orbitofrontal cortex, and higher cardiovascular fitness was associated with lower GMBF. ConclusionThe decrease of GMBF reported in younger adults shortly after exercise also occurs in older adults and relates to cardiovascular fitness, potentially supporting the link between cardiovascular fitness and cerebrovascular reactivity in older age.

Quantitative Imaging of Patients Undergoing Radiotherapy for Primary Gliomas Using A 1.5 Tesla MR Linac

The introduction of a 1.5 Tesla MR-Linac (MRL) allows for the acquisition of diagnostic quality MR images, either as ancillary studies during treatment on a conventional Linac (cLinac) or seamlessly during treatment on the MRL. We present the first in human experience imaging and treating primary brain tumors using a 1.5 Tesla MRL. Between February of 2019 and January of 2020 a total of 4 patients with gliomas have been treated using the MRL, with daily quantitative MR (qMR) and 3 patients had weekly acquisition of qMR images while undergoing treatment on a cLinac in a prospective imaging trial (NCT03500081). A detailed log during each treatment was obtained for the duration and changes in treatment time. Quantitative MR sequences (IVIM, T1 and T2 mapping) were acquired for patients treated on the MRL while adaptive planning was performed and during beam delivery. Patients treated on the MRL had daily acquisition of qMR sequences. Patients treated on cLinac were scheduled for weekly qMR imaging. qMR sequences obtained are in Table 1. Independent, FDA approved, commercially available software was used for qMRI processing and radiomic feature extraction. Six patients with WHO Grade IV tumors were treated with 60 Gy, while one WHO Grade II tumor of the brainstem was treated to 59.4Gy. The median patient age was 60. Median follow up is 4.7 months, four patients have progressed. Patients treated on the MRL had a median table time of 28 minutes including daily adaptation and optimization of IMRT plan, physician review, treatment and all qMR imaging. Weekly imaging sessions for patients treated on cLinac ranged from 15-33 minutes. Analysis of the qMR data demonstrated variable % change in median values within the Gross Target Volume (Table 1). ADC, T1, and T2 parameters ranged from (-0.2 – 25%), (-1 – 36%), and (3-22%) within the GTV, respectively. Changes in quantitative T1 (14-36%) and T2 (15-22%) within the GTV over the course of treatment were observed in the recurring patients. We present a first in human experience of imaging and treating primary gliomas with a 1.5 Tesla MR. It was feasible to treat patients on the MRL and obtain qMR imaging without additional table time. It was also feasible to integrate weekly imaging into a standard clinical work flow without increasing time. While entirely exploratory and hypothesis generating, highly unique changes in qMRI parameters were seen in those patients who developed progression. This presents an unparalleled opportunity for collection, validation, and future interventional trial integration of MR biomarkers of radiation response in primary glioma. Table 1: % Change Median Value for GTV.Tabled 1Abstract 2641; TablePatientMachineIVIMADCDSLOWDFASTFMAPT1MapT2Map1MRL7-0.25-0.2-10-132-17252415-258153-21141311-536154151812313314225conventional2761167-7-3 Open table in a new tab

Predicting Clinical Outcome in Acute Ischemic Stroke Using Parallel Multi-parametric Feature Embedded Siamese Network.

Stroke is the second leading cause of death and disability worldwide, with ischemic stroke as the most common type. The preferred diagnostic procedure at the acute stage is the acquisition of multi-parametric magnetic resonance imaging (MRI). This type of imaging not only detects and locates the stroke lesion, but also provides the blood flow dynamics that helps clinicians in assessing the risks and benefits of reperfusion therapies. However, evaluating the outcome of these risky therapies beforehand is a complicated task due to the variability of lesion location, size, shape, and cerebral hemodynamics involved. Though the fully automated model for predicting treatment outcomes using multi-parametric imaging would be highly valuable in clinical settings, MRI datasets acquired at the acute stage are mostly scarce and suffer high class imbalance. In this paper, parallel multi-parametric feature embedded siamese network (PMFE-SN) is proposed that can learn with few samples and can handle skewness in multi-parametric MRI data. Moreover, five suitable evaluation metrics that are insensitive to imbalance are defined for this problem. The results show that PMFE-SN not only outperforms other state-of-the-art techniques in all these metrics but also can predict the class with a small number of samples, as well as the class with high number of samples. An accuracy of 0.67 on leave one cross out testing has been achieved with only two samples (minority class) for training and accuracy of 0.61 with the highest number of samples (majority class). In comparison, state-of-the-art using hand crafted features has 0 accuracy for minority class and 0.33 accuracy for majority class.

Changes in resting-state functional brain connectivity associated with head impacts over one men's semi-professional soccer season.

Soccer, as a contact sport, exposes players to repetitive head impacts, especially through heading the ball. The question of a long-term brain cumulative effect remains. Our objective was to determine whether exposure to head impacts over one soccer season was associated with changes in functional brain connectivity at rest, using magnetic resonance imaging (MRI). In this prospective cohort study, 10 semi-professional men soccer players, aged 18-25years, and 20 age-matched men athletes without a concussion history and who do not practice any contact sport were recruited in Bordeaux (France). Exposure to head impacts per soccer player during competitive games over one season was measured using video analysis. Resting-state functional magnetic resonance imaging data were acquired for both groups at two times, before and after the season. With a seed-based analysis, resting-state networks that have been intimately associated with aspects of cognitive functioning were investigated. The results showed a mean head impacts of 42 (±33) per soccer player over the season, mainly intentional head-to-ball impacts and no concussion. No head impact was found among the other athletes. The number of head impacts between the two MRI acquisitions before and after the season was associated with increased connectivity within the default mode network and the cortico-cerebellar network. In conclusion, our findings suggest that the brain functioning changes over one soccer season in association with exposure to repetitive head impacts.

Quantitative Magnetic Resonance Imaging Assessment of the Quadriceps Changes during an Extreme Mountain Ultramarathon.

Extreme ultra-endurance races are growing in popularity, but their effects on skeletal muscles remain mostly unexplored. This longitudinal study explores physiological changes in mountain ultramarathon athletes' quadriceps using quantitative magnetic resonance imaging (MRI) coupled with serological biomarkers. The study aimed to monitor the longitudinal effect of the race and recovery and to identify local inflammatory and metabolic muscle responses by codetection of biological markers. An automatic image processing framework was designed to extract imaging-based biomarkers from quantitative MRI acquisitions of the upper legs of 20 finishers at three time points. The longitudinal effect of the race was demonstrated by analyzing the image markers with dedicated biostatistical analysis. Our framework allows for a reliable calculation of statistical data not only inside the whole quadriceps volume but also within each individual muscle head. Local changes in MRI parameters extracted from quantitative maps were described and found to be significantly correlated with principal serological biomarkers of interest. A decrease in the PDFF after the race and a stable paramagnetic susceptibility value were found. Pairwise post hoc tests suggested that the recovery process differs among the muscle heads. This longitudinal study conducted during a prolonged and extreme mechanical stress showed that quantitative MRI-based markers of inflammation and metabolic response can detect local changes related to the prolonged exercise, with differentiated involvement of each head of the quadriceps muscle as expected in such eccentric load. Consistent and efficient extraction of the local biomarkers enables to highlight the interplay/interactions between blood and MRI biomarkers. This work indeed proposes an automatized analytic framework to tackle the time-consuming and mentally exhausting segmentation task of muscle heads in large multi-time-point cohorts.