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Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.

Skeletal muscle is the most abundant tissue in the body and serves various physiological functions including the generation of movement and support. Whole body motor function requires adequate quantity, geometry, and distribution of muscle. This raises the question: how do muscles scale with subject size in order to achieve similar function across humans? While much of the current knowledge of human muscle architecture is based on cadaver dissection, modern medical imaging avoids limitations of old age, poor health, and limited subject pool, allowing for muscle architecture data to be obtained in vivo from healthy subjects ranging in size. The purpose of this study was to use novel fast-acquisition MRI to quantify volumes and lengths of 35 major lower limb muscles in 24 young, healthy subjects and to determine if muscle size correlates with bone geometry and subject parameters of mass and height. It was found that total lower limb muscle volume scales with mass (R2=0.85) and with the height–mass product (R2=0.92). Furthermore, individual muscle volumes scale with total muscle volume (median R2=0.66), with the height–mass product (median R2=0.61), and with mass (median R2=0.52). Muscle volume scales with bone volume (R2=0.75), and muscle length relative to bone length is conserved (median s.d.=2.1% of limb length). These relationships allow for an arbitrary subject's individual muscle volumes to be estimated from mass or mass and height while muscle lengths may be estimated from limb length. The dataset presented here can further be used as a normative standard to compare populations with musculoskeletal pathologies.

Transcranial focused ultrasound with the InSightec Exablate system uses thermal ablation for the treatment of movement and mood disorders and blood brain barrier disruption for tumor therapy. The system uses computed tomography (CT) images to calculate phase corrections that account for aberrations caused by the human skull. This work investigates whether magnetic resonance (MR) images can be used as an alternative to CT images to calculate phase corrections. Phase corrections were calculated using the gold standard hydrophone method and the standard of care InSightec ray tracing method. MR binary image mask, MR-simulated-CT (MRsimCT), and CT images of three ex vivo human skulls were supplied as inputs to the InSightec ray tracing method. The degassed ex vivo human skulls were sonicated with a 670 kHz hemispherical phased array transducer (InSightec Exablate 4000). 3D raster scans of the beam profiles were acquired using a hydrophone mounted on a 3-axis positioner system. Focal spots were evaluated using six metrics: pressure at the target, peak pressure, intensity at the target, peak intensity, positioning error, and focal spot volume. Targets at the geometric focus and 5 mm lateral to the geometric focus were investigated. There was no statistical difference between any of the metrics at either target using either MRsimCT or CT for phase aberration correction. As opposed to the MRsimCT, the use of CT images for aberration correction requires registration to the treatment day MR images; CT misregistration within a range of ± 2 degrees of rotation error along three dimensions was shown to reduce focal spot intensity by up to 9.4%. MRsimCT images used for phase aberration correction for the skull produce similar results as CT-based correction, while avoiding both CT to MR registration errors and unnecessary patient exposure to ionizing radiation.

Background The cerebrospinal fluid (CSF), primarily generated by the choroid plexus (ChP), is the major carrier of the glymphatic system. The alternations of CSF production and the ChP can be associated with the Alzheimer’s disease (AD). The present work investigated the roles of the ChP in the AD based on a proposed ChP image segmentation pipeline. Methods A human-in-the-loop ChP image segmentation pipeline was implemented with intermediate and active learning datasets. The performance of the proposed pipeline was evaluated on manual contours by five radiologists, compared to the FreeSurfer and FastSurfer toolboxes. The ChP volume and blood flow were investigated among AD groups. The correlations between the ChP volume and AD CSF biomarkers including phosphorylated tau (p-tau), total tau (t-tau), amyloid-β42 (Aβ42), and amyloid-β40 (Aβ40) was investigated using three models (univariate, multiple variables, and stepwise regression) on two datasets with 806 and 320 subjects. Results The proposed ChP segmentation pipeline achieved superior performance with a Dice coefficient of 0.620 on the test dataset, compared to the FreeSurfer (0.342) and FastSurfer (0.371). Significantly larger volumes ( p  < 0.001) and higher perfusion ( p  = 0.032) at the ChP were found in AD compared to CN groups. Significant correlations were found between the tau and the relative ChP volume (the ChP volume and ChP/parenchyma ratio) in each patient groups and in the univariate regression analysis ( p  < 0.001), the multiple regression model ( p  < 0.05 except for the t-tau in the LMCI), and in the step-wise regression model ( p  < 0.021). In addition, the correlation coefficients changed from − 0.32 to − 0.21 along with the AD progression in the multiple regression model. In contrast, the Aβ42 and Aβ40 shows consistent and significant associations with the lateral ventricle related measures in the step-wise regression model ( p  < 0.027). Conclusions The proposed pipeline provided accurate ChP segmentation which revealed the associations between the ChP and tau level in the AD. The proposed pipeline is available on GitHub ( https://github.com/princeleeee/ChP-Seg ).

Background Cardiovascular magnetic resonance (CMR) cine displacement encoding with stimulated echoes (DENSE) measures heart motion by encoding myocardial displacement into the signal phase, facilitating high accuracy and reproducibility of global and segmental myocardial strain and providing benefits in clinical performance. While conventional methods for strain analysis of DENSE images are faster than those for myocardial tagging, they still require manual user assistance. The present study developed and evaluated deep learning methods for fully-automatic DENSE strain analysis. Methods Convolutional neural networks (CNNs) were developed and trained to (a) identify the left-ventricular (LV) epicardial and endocardial borders, (b) identify the anterior right-ventricular (RV)-LV insertion point, and (c) perform phase unwrapping. Subsequent conventional automatic steps were employed to compute strain. The networks were trained using 12,415 short-axis DENSE images from 45 healthy subjects and 19 heart disease patients and were tested using 10,510 images from 25 healthy subjects and 19 patients. Each individual CNN was evaluated, and the end-to-end fully-automatic deep learning pipeline was compared to conventional user-assisted DENSE analysis using linear correlation and Bland Altman analysis of circumferential strain. Results LV myocardial segmentation U-Nets achieved a DICE similarity coefficient of 0.87 ± 0.04, a Hausdorff distance of 2.7 ± 1.0 pixels, and a mean surface distance of 0.41 ± 0.29 pixels in comparison with manual LV myocardial segmentation by an expert. The anterior RV-LV insertion point was detected within 1.38 ± 0.9 pixels compared to manually annotated data. The phase-unwrapping U-Net had similar or lower mean squared error vs. ground-truth data compared to the conventional path-following method for images with typical signal-to-noise ratio (SNR) or low SNR (p < 0.05), respectively. Bland–Altman analyses showed biases of 0.00 ± 0.03 and limits of agreement of − 0.04 to 0.05 or better for deep learning-based fully-automatic global and segmental end-systolic circumferential strain vs. conventional user-assisted methods. Conclusions Deep learning enables fully-automatic global and segmental circumferential strain analysis of DENSE CMR providing excellent agreement with conventional user-assisted methods. Deep learning-based automatic strain analysis may facilitate greater clinical use of DENSE for the quantification of global and segmental strain in patients with cardiac disease.

Use of MRI to track cell migration to target tissue, such as after stem cell transplantation in heart failure, offers a potential new way to monitor treatment. Most data come from preclinical studies but suggest a role for this technique in humans. This Review discusses the suitability of various contrast agents and labelling methods. Animal studies have shown some success in the use of stem cells of diverse origins to treat heart failure and ventricular dysfunction secondary to ischemic injury. The clinical use of these cells is, therefore, promising. In order to develop effective cell therapies, the location, distribution and long-term viability of these cells must be evaluated in a noninvasive manner. MRI of cells labeled with magnetically visible contrast agents after either direct injection or local or intravenous infusion has the potential to fulfill this goal. In this Review, techniques for labeling and imaging a variety of cells will be discussed. Particular attention will be given to the advantages and limitations of various contrast agents and passive and facilitated cell-labeling methods, as well as to imaging techniques that produce negative and positive contrast, and the effect on image quantification of compartmentalization of contrast agents within the cell. Key Points Local transplantation of stem cells has the potential to treat many cardiovascular diseases Noninvasive tracking of labeled therapeutic cells will permit assessment of local engraftment The labeling of therapeutic cells with imaging labels while maintaining cell viability requires use of both passive and facilitated transfection methods MRI labels such as iron oxide nanoparticles provide strong MRI signal alteration while imparting low cellular toxic effects

Hyperpolarized gas (HPG) magnetic resonance imaging, recently FDA-approved, offers an innovative approach to evaluating gas distribution and lung function in both adults and children. In this study, we present an algorithm for calculating maps of changes in regional ventilation in asthma, cystic fibrosis, and COPD patients before and after receiving treatment. We validate the results with a radiologist's evaluation for accuracy. Our hypothesis is that the change map would be in congruence with a radiologist's visual examination. Nine asthmatics, six cystic fibrosis patients, and five COPD patients underwent hyperpolarized 3He MRI. N4ITK bias correction, voxel smoothing, and normalization to the signal distribution's 95th percentile voxel signal value were performed on images. For calculating regional ventilation change maps, posttreatment images were registered to baseline images, and difference maps were created. Difference-map voxel values of > 60% of the baseline mean signal value were identified as improved, and those of < -60% were identified as worsened. In addition, short-term improvement (STI) was identified where voxels improved at Timepoint 2 but returned to baseline at Timepoint 3. A grading rubric was developed for radiologist scoring that had the following assessment categories: \"level of volume discrepancy\" and \"discrepancy causes\" for each ventilation change map. In 15 out of the 20 cases (75% of the data), there was a small to no volume disparity between the change map and the radiologists' visual evaluation. The rest of the two cases had moderate volume differences, and three cases had large ones. Our regional change maps demonstrated congruence with visual examination and may be a useful tool for clinicians evaluating ventilation changes longitudinally.

Background Quantitative noninvasive imaging of myocardial mechanics in mice enables studies of the roles of individual genes in cardiac function. We sought to develop comprehensive three-dimensional methods for imaging myocardial mechanics in mice. Methods A 3D cine DENSE pulse sequence was implemented on a 7T small-bore scanner. The sequence used three-point phase cycling for artifact suppression and a stack-of-spirals k -space trajectory for efficient data acquisition. A semi-automatic 2D method was adapted for 3D image segmentation, and automated 3D methods to calculate strain, twist, and torsion were employed. A scan protocol that covered the majority of the left ventricle in a scan time of less than 25 minutes was developed, and seven healthy C57Bl/6 mice were studied. Results Using these methods, multiphase normal and shear strains were measured, as were myocardial twist and torsion. Peak end-systolic values for the normal strains at the mid-ventricular level were 0.29 ± 0.17, -0.13 ± 0.03, and -0.18 ± 0.14 for E rr , E cc , and E ll , respectively. Peak end-systolic values for the shear strains were 0.00 ± 0.08, 0.04 ± 0.12, and 0.03 ± 0.07 for E rc , E rl , and E cl , respectively. The peak end-systolic normalized torsion was 5.6 ± 0.9°. Conclusions Using a 3D cine DENSE sequence tailored for cardiac imaging in mice at 7 T, a comprehensive assessment of 3D myocardial mechanics can be achieved with a scan time of less than 25 minutes and an image analysis time of approximately 1 hour.

Background Preliminary semi-quantitative cardiovascular magnetic resonance (CMR) perfusion studies have demonstrated reduced myocardial perfusion reserve (MPR) in patients with angina and risk factors for microvascular disease (MVD), however fully quantitative CMR has not been studied. The purpose of this study is to evaluate whether fully quantitative CMR identifies reduced MPR in this population, and to investigate the relationship between epicardial atherosclerosis, left ventricular hypertrophy (LVH), extracellular volume (ECV), and perfusion. Methods Forty-six patients with typical angina and risk factors for MVD (females, or males with diabetes or metabolic syndrome) who had no obstructive coronary artery disease by coronary angiography and 20 healthy control subjects underwent regadenoson stress CMR perfusion imaging using a dual-sequence quantitative spiral pulse sequence to quantify MPR. Subjects also underwent T1 mapping to quantify ECV, and computed tomographic (CT) coronary calcium scoring to assess atherosclerosis burden. Results In patients with risk factors for MVD, both MPR (2.21 [1.95,2.69] vs. 2.93 [2.763.19], p  < 0.001) and stress myocardial perfusion (2.65 ± 0.62 ml/min/g, vs. 3.17 ± 0.49 ml/min/g p  < 0.002) were reduced as compared to controls. These differences remained after adjusting for age, left ventricular (LV) mass, body mass index (BMI), and gender. There were no differences in native T1 or ECV between subjects and controls. Conclusions Stress myocardial perfusion and MPR as measured by fully quantitative CMR perfusion imaging are reduced in subjects with risk factors for MVD with no obstructive CAD as compared to healthy controls. Neither myocardial hypertrophy nor fibrosis accounts for these differences.

The purpose was to determine the reproducibility and utility of rest, exercise, and perfusion reserve (PR) measures by contrast-enhanced (CE) calf perfusion magnetic resonance imaging (MRI) of the calf in normal subjects (NL) and patients with peripheral arterial disease (PAD). Eleven PAD patients with claudication (ankle-brachial index 0.67 ±0.14) and 16 age-matched NL underwent symptom-limited CE-MRI using a pedal ergometer. Tissue perfusion and arterial input were measured at rest and peak exercise after injection of 0.1 mM/kg of gadolinium-diethylnetriamine pentaacetic acid (Gd-DTPA). Tissue function (TF) and arterial input function (AIF) measurements were made from the slope of time-intensity curves in muscle and artery, respectively, and normalized to proton density signal to correct for coil inhomogeneity. Perfusion index (PI) = TF/AIF. Perfusion reserve (PR) = exercise TF/ rest TF. Intraclass correlation coefficient (ICC) was calculated from 11 NL and 10 PAD with repeated MRI on a different day. Resting TF was low in NL and PAD (mean ± SD 0.25 ± 0.18 vs 0.35 ± 0.71, p = 0.59) but reproducible (ICC 0.76). Exercise TF was higher in NL than PAD (5.5 ± 3.2 vs. 3.4 ± 1.6, p = 0.04). Perfusion reserve was similar between groups and highly variable (28.6 ± 19.8 vs. 42.6 ± 41.0, p = 0.26). Exercise TF and PI were reproducible measures (ICC 0.63 and 0.60, respectively). Although rest measures are reproducible, they are quite low, do not distinguish NL from PAD, and lead to variability in perfusion reserve measures. Exercise TF and PI are the most reproducible MRI perfusion measures in PAD for use in clinical trials.