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Magnetic resonance imaging (MRI) and MRI based computational modelling studies provide insights into severity and recovery of ischemic stroke patients. The presence of brain lesions, however, can heavily distort and impair state‐of‐the‐art processing frameworks due to abnormal intensity values and tissue distortions. In this study, we introduce and validate the novel "Lesion Aware automated Processing Pipeline (LeAPP)." LeAPP automatically processes clinical stroke MRI data while significantly reducing the impact of pathological artefacts on processing outputs such as structural and functional connectomes (SC, FC). This helps to advance the identification of biomarkers for recovery and mechanism‐based intervention planning using MRI as well as computational approaches. We extended existing frameworks, such as the Human Connectome Project (HCP) minimal processing pipeline, introducing correction steps, and implementing and modifying functional and diffusion processing to cope with MRI acquisition protocols more typical for the clinical context. A total of 51 participants (36 patients, 15 age‐matched controls) were processed across four time points for patients (3–5, 30–40, 85–95, 340–380 days after stroke onset) and one time point for controls. We validated performance using artificial lesioned brains (N = 81), derived from healthy brains and informed by real stroke lesions. The quality of reconstructing the ground truth was quantified on whole brain level and for lesion affected and unaffected regions‐of‐interest (ROIs) for brain parcellations and for SCs. Volume based agreement was evaluated using metrics such as dice coefficient, volume difference or ROI center‐of‐gravity distance while SC based agreement was defined as the difference in network metrics (e.g., node strength, clustering coefficient, or centrality). The observed deviations in reconstructed ground truth brain parcellations and structural connectomes from lesioned brains were significantly reduced for LeAPP, compared to the performance of existing pipelines such as HCP minimal processing pipeline. For instance, in the case of lesion affected ROIS we achieved a mean dice coefficient (where a value of one represents total agreement as defined by an exact overlap of both ROIs) of 0.81 with LeAPP, compared to 0.75 for HCP (p <.0001*). Additionally, the average measured ROI distance (where a value of zero represents no difference) for lesion ROIs was 0.87 for LeAPP in contrast to 1.7 for HCP (p <.0001*), indicating an overall superior performance of LeAPP. The pipeline generates standardized output files ready for brain network modelling for instance with The Virtual Brain software. This novel open‐source automated processing framework contributes to reproducible research and provides a robust framework for automated processing of clinical stroke MRI data, supporting the identification of brain network‐based biomarkers of stroke recovery. [ABSTRACT FROM AUTHOR]

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter‐individual heterogeneity in the multisystem ageing process. Using machine‐learning regression models on the UK Biobank data set (N = 32,593, age range 44.6–82.3, mean age 64.1 years), we first estimated tissue‐specific brain age for white and gray matter based on diffusion and T1‐weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion‐based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle‐fat infiltration were related to older‐appearing body age compared to brain age. Conversely, higher hand‐grip strength and muscle volume were related to a younger‐appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals. [ABSTRACT FROM AUTHOR]

The goal of this study is to prove that the light propagation in the head by used the 3-D optical model from in vivo MRI data set can also provide significant characteristics on the spatial sensitivity of cerebral cortex folding geometry based on Monte Carlo simulation. Thus, we proposed a MRI based approach for 3-D brain modeling of near-infrared spectroscopy (NIRS). In the results, the spatial sensitivity profile of the cerebral cortex folding geometry and the arrangement of source-detector separation have being necessarily considered for applications of functional NIRS. The optimal choice of source-detector separation is suggested within 3-3.5 cm by the received intensity with different source-detector separations and the ratio of received light from the gray and white matter layer is greater than 50%. Additionally, this study has demonstrated the capability of NIRS in not only assessing the functional but also detecting the structural change of the brain by taking advantage of the low scattering and absorption coefficients observed in CSF of sagittal view. (© 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

We investigated the influence of the temporal resolution of dynamic contrast-enhanced MRI data on pharmacokinetic parameter estimation. Dynamic Gd-DTPA (Gadolinium-diethylene triamine pentaacetic acid) enhanced MRI data of implanted prostate tumors on rat hind limb were acquired at 4.7 T, with a temporal resolution of ∼5 sec. The data were subsequently downsampled to temporal resolutions in the range of 15 sec to 85 sec, using a strategy that involves a recombination of k-space data. A basic two-compartment model was fit to the contrast agent uptake curves. The results demonstrated that as temporal resolution decreases, the volume transfer constant (Ktrans) is progressively underestimated (∼4% to ∼25%), and the fractional extravascular extracellular space (ve) is progressively overestimated (∼1% to ∼10%). The proposed downsampling strategy simulates the influence of temporal resolution more realistically than simply downsampling by removing samples. Magn Reson Med 63:811-816, 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Purpose To assess the temporal sampling requirements needed for quantitative analysis of dynamic contrast-enhanced MRI (DCE-MRI) data with a reference region (RR) model in human breast cancer. Materials and Methods Simulations were used to study errors in pharmacokinetic parameters ( K trans and v e) estimated by the RR model using six DCE-MRI acquisitions over a range of pharmacokinetic parameter values, arterial input functions, and temporal samplings. DCE-MRI data were acquired on 12 breast cancer patients and parameters were estimated using the native resolution data (16.4 seconds) and compared to downsampled 32.8-second and 65.6-second data. Results Simulations show that, in the majority of parameter combinations, the RR model results in an error less than 20% in the extracted parameters with temporal sampling as poor as 35.6 seconds. The experimental results show a high correlation between K trans and v e estimates from data acquired at 16.4-second temporal resolution compared to the downsampled 32.8-second data: the slope of the regression line was 1.025 (95% confidence interval [CI]: 1.021, 1.029), Pearson's correlation r = 0.943 (95% CI: 0.940, 0.945) for K trans, and 1.023 (95% CI: 1.021. 1.025), r = 0.979 (95% CI: 0.978, 0.980) for v e. For the 64-second temporal resolution data the results were: 0.890 (95% CI: 0.894, 0.905), r = 0.8645, (95% CI: 0.858, 0.871) for K trans, and 1.041 (95% CI: 1.039, 1.043), r = 0.970 (95% CI: 0.968, 0.971) for v e. Conclusion RR analysis allows for a significant reduction in temporal sampling requirements and this lends itself to analyze DCE-MRI data acquired in practical situations. J. Magn. Reson. Imaging 2009;30:121-134. © 2009 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Evaluation of high intensity focused ultrasound (HIFU) treatment with MRI is generally based on assessment of the non-perfused volume from contrast-enhanced T1-weighted images. However, the vascular status of tissue surrounding the non-perfused volume has not been extensively investigated with MRI. In this study, cluster analysis of the transfer constant Ktrans and extravascular extracellular volume fraction ve, derived from dynamic contrast-enhanced MRI (DCE-MRI) data, was performed in tumor tissue surrounding the non-perfused volume to identify tumor subregions with distinct contrast agent uptake kinetics. DCE-MRI was performed in CT26.WT colon carcinoma-bearing BALB/c mice before ( n = 12), directly after ( n = 12) and 3 days after ( n = 6) partial tumor treatment with HIFU. In addition, a non-treated control group ( n = 6) was included. The non-perfused volume was identified based on the level of contrast enhancement. Quantitative comparison between non-perfused tumor fractions and non-viable tumor fractions derived from NADH-diaphorase histology showed a stronger agreement between these fractions 3 days after treatment ( R2 to line of identity = 0.91) compared with directly after treatment ( R2 = 0.74). Next, k-means clustering with four clusters was applied to Ktrans and ve parameter values of all significantly enhanced pixels. The fraction of pixels within two clusters, characterized by a low Ktrans and either a low or high ve, significantly increased after HIFU. Changes in composition of these clusters were considered to be HIFU induced. Qualitative H&E histology showed that HIFU-induced alterations in these clusters may be associated with hemorrhage and structural tissue disruption. Combined microvasculature and hypoxia staining suggested that these tissue changes may affect blood vessel functionality and thereby tumor oxygenation. In conclusion, it was demonstrated that, in addition to assessment of the non-perfused tumor volume, the presented methodology gives further insight into HIFU-induced effects on tumor vascular status. This method may aid in assessment of the consequences of vascular alterations for the fate of the tissue. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Purpose Together with the development of MRI contrasts that are inherently small in their magnitude, increased magnetic field accuracy is also required. Hence, mapping of the static magnetic field (B0) and the excitation field (B1) is not only important to feedback shim algorithms, but also for postprocess contrast-correction procedures. Methods A novel field-inhomogeneity mapping method is presented that allows simultaneous mapping of the water shift and B1 (WASABI) using an off-resonant rectangular preparation pulse. The induced Rabi oscillations lead to a sinc-like spectrum in the frequency-offset dimension and allow for determination of B0 by its symmetry axis and of B1 by its oscillation frequency. Results Stability of the WASABI method with regard to the influences of T1, T2, magnetization transfer, and repetition time was investigated and its convergence interval was verified. B0 and B1 maps obtained simultaneously by means of WASABI in the human brain at 3 T and 7 T can compete well with maps obtained by standard methods. Finally, the method was applied successfully for B0 and B1 correction of chemical exchange saturation transfer MRI (CEST-MRI) data of the human brain. Conclusion The proposed WASABI method yields a novel simultaneous B0 and B1 mapping within 1 min that is robust and easy to implement. Magn Reson Med 77:571-580, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose To develop and test a new algorithm for fast direct Fourier transform (DrFT) reconstruction of MR data on non-Cartesian trajectories composed of lines with equally spaced points. Theory and Methods The DrFT, which is normally used as a reference in evaluating the accuracy of other reconstruction methods, can reconstruct images directly from non-Cartesian MR data without interpolation. However, DrFT reconstruction involves substantially intensive computation, which makes the DrFT impractical for clinical routine applications. In this article, the Chirp transform algorithm was introduced to accelerate the DrFT reconstruction of radial and Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) MRI data located on the trajectories that are composed of lines with equally spaced points. The performance of the proposed Chirp transform algorithm-DrFT algorithm was evaluated by using simulation and in vivo MRI data. Results After implementing the algorithm on a graphics processing unit, the proposed Chirp transform algorithm-DrFT algorithm achieved an acceleration of approximately one order of magnitude, and the speed-up factor was further increased to approximately three orders of magnitude compared with the traditional single-thread DrFT reconstruction. Conclusion Implementation the Chirp transform algorithm-DrFT algorithm on the graphics processing unit can efficiently calculate the DrFT reconstruction of the radial and PROPELLER MRI data. Magn Reson Med, 70:1087-1094, 2013. © 2012 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud‐based tool for ASL data processing, referred to as ASL‐MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL‐MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL‐MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo‐continuous ASL and pulsed ASL, and can process single‐delay and multi‐delay ASL data. The outputs of ASL‐MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel‐wise masks indicating regions with potential hyper‐perfusion and hypo‐perfusion, and an image quality index. The ASL tool is also integrated with a T1‐based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region‐of‐interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud‐based tool for ASL data processing, referred to as ASL‐MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL‐MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. [ABSTRACT FROM AUTHOR]

Purpose: To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Theory and Methods: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. Results: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. Conclusion: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055–3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Background: Pharmacokinetic (PK) models can describe microvascular density and integrity. An essential component of PK models is the arterial input function (AIF) representing the time-dependent concentration of contrast agent (CA) in the blood plasma supplied to a tissue.Purpose/hypothesis: To evaluate a novel method for subject-specific AIF estimation that takes inflow effects into account.Study Type: Retrospective study.Subjects: Thirteen clinical patients referred for spine-related complaints; 21 patients from a study into luminal Crohn's disease with known Crohn's Disease Endoscopic Index of Severity (CDEIS).Field Strength/sequence: Dynamic fast spoiled gradient echo (FSPGR) at 3T.Assessment: A population-averaged AIF, AIFs derived from distally placed regions of interest (ROIs), and the new AIF method were applied. Tofts' PK model parameters (including vp and Ktrans ) obtained with the three AIFs were compared. In the Crohn's patients Ktrans was correlated to CDEIS.Statistical Tests: The median values of the PK model parameters from the three methods were compared using a Mann-Whitney U-test. The associated variances were statistically assessed by the Brown-Forsythe test. Spearman's rank correlation coefficient was computed to test the correlation of Ktrans to CDEIS.Results: The median vp was significantly larger when using the distal ROI approach, compared to the two other methods (P < 0.05 for both comparisons, in both applications). Also, the variances in vp were significantly larger with the ROI approach (P < 0.05 for all comparisons). In the Crohn's disease study, the estimated Ktrans parameter correlated better with the CDEIS (r = 0.733, P < 0.001) when the proposed AIF was used, compared to AIFs from the distal ROI method (r = 0.429, P = 0.067) or the population-averaged AIF (r = 0.567, P = 0.011).Data Conclusion: The proposed method yielded realistic PK model parameters and improved the correlation of the Ktrans parameter with CDEIS, compared to existing approaches.Level Of Evidence: 3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2018;47:1197-1204. [ABSTRACT FROM AUTHOR]

Purpose Most dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data are evaluated for individual patients with cohorts analyzed to detect significant changes from baseline values, repeating the process at each posttreatment timepoint. Our study aimed to develop a statistically valid model for the complete time course of DCE-MRI data in a patient cohort. Materials and Methods Data from 10 patients with colorectal cancer liver metastases were analyzed, including two baseline scans and four post-bevacizumab scans. Apparent changes in tumor median Ktrans were adjusted for changes in observed enhancing tumor fraction (EnF) by multiplying Ktrans by EnF ( KEnF). A mixed-effects model (MEM) was defined to describe the KEnF time course for all patients simultaneously by assuming a three-parameter indirect response model with model parameters lognormally distributed across patients. Results The typical cohort time course showed a KEnF reduction to 59% of baseline at 24 hours, returning to 65% of baseline values by day 12. Interpatient variability of model parameters ranged from 11% to 307%. Conclusion The MEM approach has potential for comparing responses at a group level in clinical trials with different doses, schedules, or combination regimens. Furthermore, the KEnF biomarker successfully resolved confounds in interpreting Ktrans arising from therapy induced changes in the volume of enhancing tumor. J. Magn. Reson. Imaging 2015;41:132-141. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this study, we investigated the discriminative capacity of knee morphology in automatic detection of osteophytes defined by the Osteoarthritis Research Society International atlas, using X‐ray and magnetic resonance imaging (MRI) data. For the X‐ray analysis, we developed a deep learning (DL) based model to segment femur and tibia. In case of MRIs, we utilized previously validated segmentations of femur, tibia, corresponding cartilage tissues, and menisci. Osteophyte detection was performed using DL models in four compartments: medial femur (FM), lateral femur (FL), medial tibia (TM), and lateral tibia (TL). To analyze the confounding effects of soft tissues, we investigated their morphology in combination with bones, including bones+cartilage, bones+menisci, and all the tissues. From X‐ray‐based 2D morphology, the models yielded balanced accuracy of 0.73, 0.69, 0.74, and 0.74 for FM, FL, TM, TL, respectively. Using 3D bone morphology from MRI, balanced accuracy was 0.80, 0.77, 0.71, and 0.76, respectively. The performance was higher than in 2D for all the compartments except for TM, with significant improvements observed for femoral compartments. Adding menisci or cartilage morphology consistently improved balanced accuracy in TM, with the greatest improvement seen for small osteophyte. Otherwise, the models performed similarly to bones‐only. Our experiments demonstrated that MRI‐based models show higher detection capability than X‐ray based models for identifying knee osteophytes. This study highlighted the feasibility of automated osteophyte detection from X‐ray and MRI data and suggested further need for development of osteophyte assessment criteria in addition to OARSI, particularly, for early osteophytic changes. [ABSTRACT FROM AUTHOR]

This study applied radiomics to MRI data for automated classification of soft tissue abnormalities near total hip arthroplasty (THA). A total of 126 subjects with 1.5 T MRI of symptomatic THA were included in the analysis. Peri‐prosthetic soft tissue regions of interest were manually segmented and classified by an expert radiologist. An established radiomics library was used to extract 96 features from 2D image patches across segmented regions. Logistic regression was employed as the primary radiomic classifier, achieving an average area under curve (AUC) of 0.71 in differentiating tissue classifications spanning normal, infected, and several inflammatory, noninfectious categories. Notably, infection cases were identified with the highest accuracy, attaining an AUC of 0.79. Statement of Clinical Significance: This study demonstrates that radiomics applied to MRI data can effectively automate the classification of soft tissue abnormalities in symptomatic total hip arthroplasty, particularly in differentiating periprosthetic infections. [ABSTRACT FROM AUTHOR]

Purpose Physiological noise often dominates the blood-oxygen level-dependent (BOLD) signal fluctuations in high-field functional MRI (fMRI) data. Therefore, to optimize fMRI protocols, it becomes crucial to investigate how physiological signal fluctuations impact various acquisition and reconstruction schemes at different acquisition speeds. In particular, further differences can arise between 2D and 3D fMRI acquisitions due to different encoding strategies, thereby impacting fMRI sensitivity in potentially significant ways. Methods The amount of physiological noise to be removed from the BOLD fMRI signal acquired at 7 T was quantified for different sampling rates (repetition time from 3300 to 350 ms, acceleration 1 to 8) and techniques dedicated to fast fMRI (simultaneous multislice echo planar imaging [EPI] and 3D EPI). Resting state fMRI (rsfMRI) performances were evaluated using temporal signal-to-noise ratio (tSNR) and network characterization based on seed correlation and independent component analysis. Results Overall, acceleration enhanced tSNR and rsfMRI metrics. 3D EPI benefited the most from physiological noise removal at long repetition times. Differences between 2D and 3D encoding strategies disappeared at high acceleration factors (6- to 8-fold). Conclusion After physiological noise correction, 2D- and 3D-accelerated sequences provide similar performances at high fields, both in terms of tSNR and resting state network identification and characterization. Magn Reson Med 78:888-896, 2017. © 2017 International Society for Magnetic Resonance in Medicine. [ABSTRACT FROM AUTHOR]

Pharmacokinetic parameters corresponding to perfused microvascular volume determined from dynamic contrast-enhanced (DCE) MRI data were compared to immunohistochemical measures of microvascular density (MVD) and perfused microvascular density. DCE MRI data from human mammary tumors (MDA-MB-435) implanted in nude mice using low (Gd-DTPA, MW ≈0.6 kDa), medium (Gadomer-17, MWeff ≈35 kDa), and high (PG-Gd-DTPA, MW ≈220 kDa) molecular weight contrast agents were analyzed with single- and dual-tracer pharmacokinetic models. MVD values were determined by two manual counting methods, 'hot spot' and summed region of interest (SROI). Pharmacokinetic parameters determined using the single-tracer model (Gd-DTPA [ n = 15] and Gadomer-17 [ n = 13]) did not correlate with MVD measures using either manual counting method. For dual-tracer studies (Gadomer-17/Gd-DTPA [ n = 15] and PG-Gd-DTPA/Gd-DTPA [ n = 13]), pharmacokinetic parameters demonstrated a statistically significant correlation with MVD determined by the SROI method, but not the 'hot spot' method. Ten mice successfully underwent intravital FITC-labeled lectin perfusion with the hemisphere of highest lectin labeling correlating with pharmacokinetic parameter values in 9 of 10 tumors (single-tracer Gd-DTPA [ n = 2], single-tracer Gadomer-17 [ n = 3], and dual-tracer Gadomer-17/Gd-DTPA [ n = 5]). This study demonstrates that dual-tracer DCE MRI studies yield pharmacokinetic parameters that correlate with immunohistochemical measures of MVD. Magn Reson Med 58:705-716, 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Purpose To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Methods Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Results Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Conclusion Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. Magn Reson Med 74:1435-1448, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D flow MRI. Methods: A novel deep learning–based approach is proposed to estimate pressure gradient across stenosis from four‐dimensional flow MRI (4D flow MRI) velocities. A deep neural network 4D flow Velocity‐to‐Presure Network (4Dflow‐VP‐Net) was trained to learn the spatiotemporal relationship between velocities and pressure in stenotic vessels. Training data were simulated by computational fluid dynamics (CFD) for different pulsatile flow conditions under an aortic flow waveform. The network was tested to predict pressure from CFD‐simulated velocity data, in vitro 4D flow MRI data, and in vivo 4D flow MRI data of patients with both moderate and severe aortic stenosis. TVPG derived from 4Dflow‐VP‐Net was compared to catheter‐based pressure measurements for available flow rates, in vitro and Doppler echocardiography–based pressure measurement, in vivo. Results: Relative pressures calculated by 4Dflow‐VP‐Net and in vitro pressure catheterization revealed strong correlation (r2 = 0.91). Correlations analysis of TVPG from reference CFD and 4Dflow‐VP‐Net for 450 simulated flow conditions showed strong correlation (r2 = 0.99). TVPG from in vitro MRI had a correlation coefficient of r2 = 0.98 with reference CFD. 4Dflow‐VP‐Net, applied to 4D flow MRI in 16 patients, showed comparable TVPG measurement with Doppler echocardiography (r2 = 0.85). Bland–Altman analysis of TVPG measurements showed mean bias and limits of agreement of −0.20 ± 2.07 mmHg and 0.19 ± 0.45 mmHg for CFD‐simulated velocities and in vitro 4D flow velocities. In patients, overestimation of Doppler echocardiography relative to TVPG from 4Dflow‐VP‐Net (10.99 ± 6.77 mmHg) was observed. Conclusion: The proposed approach can predict relative pressure in both in vitro and in vivo 4D flow MRI of aortic stenotic patients with high fidelity. [ABSTRACT FROM AUTHOR]

Purpose Existence of low SNR regions and rapid-phase variations pose challenges to spatial phase unwrapping algorithms. Global optimization-based phase unwrapping methods are widely used, but are significantly slower than greedy methods. In this paper, dual decomposition acceleration is introduced to speed up a three-dimensional graph cut-based phase unwrapping algorithm. Methods The phase unwrapping problem is formulated as a global discrete energy minimization problem, whereas the technique of dual decomposition is used to increase the computational efficiency by splitting the full problem into overlapping subproblems and enforcing the congruence of overlapping variables. Using three dimensional (3D) multiecho gradient echo images from an agarose phantom and five brain hemorrhage patients, we compared this proposed method with an unaccelerated graph cut-based method. Results Experimental results show up to 18-fold acceleration in computation time. Conclusions Dual decomposition significantly improves the computational efficiency of 3D graph cut-based phase unwrapping algorithms. Magn Reson Med 77:1353-1358, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Purpose To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI. Methods Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner. Results Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets. Conclusion High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine [ABSTRACT FROM AUTHOR]

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to quantify the response of tumors to vascular targeting agents. Tumor response is frequently assessed using mathematical models that describe the distribution of contrast agents over time as a function of fundamental characteristics of vascular physiology. Generally, mathematical models of biological systems are abstractions that attempt to retain fundamental physiologic characteristics, thereby allowing for multiple potential modeling approaches and structures. Various DCE-MRI modeling techniques are discussed in this article. [ABSTRACT FROM AUTHOR]

Purpose To improve the accuracy and precision of tracer kinetic model parameter estimates for use in dynamic contrast enhanced (DCE) MRI studies of solid tumors. Theory Quantitative DCE-MRI requires an estimate of precontrast T1, which is obtained prior to fitting a tracer kinetic model. As T1 mapping and tracer kinetic signal models are both a function of precontrast T1 it was hypothesized that its joint estimation would improve the accuracy and precision of both precontrast T1 and tracer kinetic model parameters. Methods Accuracy and/or precision of two-compartment exchange model (2CXM) parameters were evaluated for standard and joint fitting methods in well-controlled synthetic data and for 36 bladder cancer patients. Methods were compared under a number of experimental conditions. Results In synthetic data, joint estimation led to statistically significant improvements in the accuracy of estimated parameters in 30 of 42 conditions (improvements between 1.8% and 49%). Reduced accuracy was observed in 7 of the remaining 12 conditions. Significant improvements in precision were observed in 35 of 42 conditions (between 4.7% and 50%). In clinical data, significant improvements in precision were observed in 18 of 21 conditions (between 4.6% and 38%). Conclusion Accuracy and precision of DCE-MRI parameter estimates are improved when signal models are fit jointly rather than sequentially. Magn Reson Med 76:1270-1281, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Diffusion-weighted magnetic resonance imaging data obtained early in the course of therapy can be used to estimate tumor proliferation rates, and the estimated rates can be used to predict tumor cellularity at the conclusion of therapy. Six patients underwent diffusion-weighted magnetic resonance imaging immediately before, after one cycle, and after all cycles of neoadjuvant chemotherapy. Apparent diffusion coefficient values were calculated for each voxel and for a whole tumor region of interest. Proliferation rates were estimated using the apparent diffusion coefficient data from the first two time points and then used with the logistic model of tumor growth to predict cellularity after therapy. The predicted number of tumor cells was then correlated to the corresponding experimental data. Pearson's correlation coefficient for the region of interest analysis yielded 0.95 ( P = 0.004), and, after applying a 3 × 3 mean filter to the apparent diffusion coefficient data, the voxel-by-voxel analysis yielded a Pearson correlation coefficient of 0.70 ± 0.10 ( P < 0.05). Magn Reson Med, 2011. © 2011 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The article presents research on the partial volume (PV) correction of multiple inversion time in arterial spin labelling in magnetic resonance imaging (MRI) data. Cerebral blood flow (CBF) estimates in arterial spin labelling (ASL) can be affected by the presence of both gray matter (GM) and white matter within any voxel. A PV correction method for ASL has been demonstrated where the use of local linear regression separated the GM and white matter ASL signal. Increases in mean GM CBF after correction of 69-80 percent were observed.

Here, we describe an automated nonparametric method for evaluating gadolinium-diethylene triamine pentaacetic acid (Gd-DTPA) kinetics, based on dynamic contrast-enhanced-MRI scans of glioblastoma patients taken before and after treatment with bevacizumab; no specific model or equation structure is assumed or used. Tumor and venous blood concentration-time profiles are smoothed, using a robust algorithm that removes artifacts due to patient motion, and then deconvolved, yielding an impulse response function. In addition to smoothing, robustness of the deconvolution operation is assured by excluding data that occur prior to the plasma peak; an exhaustive analysis was performed to demonstrate that exclusion of the prepeak plasma data does not significantly affect results. All analysis steps are executed by a single R script that requires blood and tumor curves as the sole input. Statistical moment analysis of the Impulse response function yields the area under the curve ( AUC) and mean residence time (MRT). Comparison of deconvolution results to fitted Tofts model parameters suggests that ${{AUC} \over {{\rm MRT}}}$ and AUC of the Impulse response function closely approximate fractional clearance from plasma to tissue ( Ktrans) and fractional interstitial volume ( v e) . Intervisit variability is shown to be comparable when using the deconvolution method (11% [ ${{AUC} \over {{\rm MRT}}}$] and 13%[ AUC]) compared to the Tofts model (14%[ Ktrans] and 24%[ v e]). AUC and ${{AUC} \over {{\rm MRT}}}$ both exhibit a statistically significant decrease ( P < 0.005) 1 day after administration of bevacizumab. Magn Reson Med 63:1366-1375, 2010. © 2010 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Multivariate analysis has been applied on proton magnetic resonance spectroscopic imaging (1H-MRSI) and dynamic contrast enhanced MRI (DCE-MRI) data of patients with different prostatic diseases such as chronic inflammation, fibrosis and adenocarcinoma. Multivariate analysis offers a global view of the entire range of information coming from both the imaging and spectroscopic side of NMR technology, leading to an integrated picture of the system relying upon the entire metabolic and dynamic profile of the studied samples. In this study, we show how this approach, applied to 1H-MRSI/DCE-MRI results, allows us to differentiate among the various prostatic diseases in a non-invasive way with a 100% accuracy. These findings suggest that multivariate analysis of 1H-MRSI/DCE-MRI can significantly improve the diagnostic accuracy for these pathological entities. From a more theoretical point of view, the complementation of a single biomarker approach with an integrated picture of the entire metabolic and dynamic profile allows for a more realistic appreciation of pathological entities. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Accurately measuring renal function is crucial for pediatric patients with kidney conditions. Traditional methods have limitations, but dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) provides a safe and efficient approach for detailed anatomical evaluation and renal function assessment. However, motion artifacts during DCE‐MRI can degrade image quality and introduce misalignments, leading to unreliable results. This study introduces a motion‐compensated reconstruction technique for DCE‐MRI data acquired using golden‐angle radial sampling. Our proposed method achieves three key objectives: (1) identifying and removing corrupted data (outliers) using a Gaussian process model fitting with a k‐space center navigator, (2) efficiently clustering the data into motion phases and performing interphase registration, and (3) utilizing a novel formulation of motion‐compensated radial reconstruction. We applied the proposed motion correction (MoCo) method to DCE‐MRI data affected by varying degrees of motion, including both respiratory and bulk motion. We compared the outcomes with those obtained from the conventional radial reconstruction. Our evaluation encompassed assessing the quality of images, concentration curves, and tracer kinetic model fitting, and estimating renal function. The proposed MoCo reconstruction improved the temporal signal‐to‐noise ratio for all subjects, with a 21.8% increase on average, while total variation values of the aorta, right, and left kidney concentration were improved for each subject, with 32.5%, 41.3%, and 42.9% increases on average, respectively. Furthermore, evaluation of tracer kinetic model fitting indicated that the median standard deviation of the estimated filtration rate (σFT), mean normalized root‐mean‐squared error (nRMSE), and chi‐square goodness‐of‐fit of tracer kinetic model fit were decreased from 0.10 to 0.04, 0.27 to 0.24, and, 0.43 to 0.27, respectively. The proposed MoCo technique enabled more reliable renal function assessment and improved image quality for detailed anatomical evaluation in the case of bulk and respiratory motion during the acquisition of DCE‐MRI. [ABSTRACT FROM AUTHOR]

Background: Magnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re‐acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k‐space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)‐based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra‐scan motion. Purpose: To retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI‐based approaches, provide useful information to the reconstruction method, or prompt for re‐acquisition while the patient is still in the scanner. Methods: We developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion‐corrupted data can be leveraged to train the convolutional neural network (CNN)‐based motion artifact estimator, generalizing well to real‐world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion‐robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise. Results: Training and validation were performed on 4387 and 1304 synthetically motion‐corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion‐corrupted data from 28 volunteers, where our model distinguished head motion from motion‐free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label ('Good', 'Medium' or 'Bad' quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values. Conclusions: The proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real‐time motion artifact detection that can help improve the safety and quality of AI‐based reconstructions. [ABSTRACT FROM AUTHOR]

Renal pathologies often manifest as alterations in kidney size, providing a valuable avenue for employing dynamic parametric MRI as a means to derive kidney size measurements for the diagnosis, treatment, and monitoring of renal disease. Furthermore, this approach holds significant potential in supporting MRI data‐driven preclinical investigations into the intricate mechanisms underlying renal pathophysiology. The integration of deep learning algorithms is crucial in achieving rapid and precise segmentation of the kidney from temporally resolved parametric MRI, facilitating the use of kidney size as a meaningful (pre)clinical biomarker for renal disease. To explore this potential, we employed dynamic parametric T2 mapping of the kidney in rats in conjunction with a custom‐tailored deep dilated U‐Net (DDU‐Net) architecture. The architecture was trained, validated, and tested on manually segmented ground truth kidney data, with benchmarking against an analytical segmentation model and a self‐configuring no new U‐Net. Subsequently, we applied our approach to in vivo longitudinal MRI data, incorporating interventions that emulate clinically relevant scenarios in rats. Our approach achieved high performance metrics, including a Dice coefficient of 0.98, coefficient of determination of 0.92, and a mean absolute percentage error of 1.1% compared with ground truth. The DDU‐Net enabled automated and accurate quantification of acute changes in kidney size, such as aortic occlusion (−8% ± 1%), venous occlusion (5% ± 1%), furosemide administration (2% ± 1%), hypoxemia (−2% ± 1%), and contrast agent‐induced acute kidney injury (11% ± 1%). This approach can potentially be instrumental for the development of dynamic parametric MRI‐based tools for kidney disorders, offering unparalleled insights into renal pathophysiology. [ABSTRACT FROM AUTHOR]

Brain atrophy is a typical feature of many neurological conditions. Therefore, quantitative evaluation and spatial characterization of atrophy are potentially useful for monitoring the evolution of central nervous system (CNS) disorders. In this study, a method for measuring atrophy of the major white matter (WM) fiber bundles in the brain using diffusion tensor (DT) MRI data is developed. To this end, an atlas was created from sets of diffusion anisotropy images from normal subjects, and the deformations necessary to match single subject anisotropy images to this atlas were then computed. Because diffusion anisotropy images were used, this approach should be sensitive to fiber bundle volume changes in the same way that using T1-weighted images allows gray matter volume changes to be measured. The Jacobian determinant of the deformation field for each subject was then used as a measure of contraction or expansion of the tissue at each image voxel. An overview of the nonlinear registration problem is given; then an optimization of the parameters for the chosen algorithm is performed and the method for producing the atlas is described. The effectiveness of the method was then tested on data from five patients with multiple sclerosis (MS) and two patients with amyotrophic lateral sclerosis (ALS). Magn Reson Med 58:527-534, 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Models have been developed for analyzing dynamic contrast-enhanced (DCE)-MRI data that do not require measurements of the arterial input function (AIF). In this study, experimental results obtained from a reference region (RR) analysis are compared with results of an AIF analysis in the same set of five animals (four imaged twice, yielding nine data sets), returning estimates of the volume transfer constant (Ktrans) and the extravascular extracellular volume fraction (ve). Student's t-test values for comparisons of Ktrans and ve between the two models were 0.14 ( P = 0.88) and 0.85 ( P > 0.4), respectively (where the high P-values indicate no significant difference between values derived from the two models). Linear regression analysis indicated there was a correlation between Ktrans extracted by the two methods: r2 = 0.80, P = 0.001 (where the low P-value indicates a significant linear correlation). For ve there was no such correlation (r2 = 0.02). The mean (absolute) percent difference between the models was 22.0% for Ktrans and 28.1% for ve. However, the RR parameter values were much less precise than the AIF method. The mean SDs for Ktrans and ve for the RR analysis were 0.024 min-1 and 0.06, respectively, vs. 0.002 min-1 and 0.03 for AIF analysis. Magn Reson Med 57:353-361, 2007. © 2007 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

The Rician distribution of noise in magnitude magnetic resonance (MR) images is particularly problematic in low signal-to-noise ratio (SNR) regions. The Rician noise distribution causes a nonzero minimum signal in the image, which is often referred to as the rectified noise floor. True low signal is likely to be concealed in the noise, and quantification is severely hampered in low-SNR regions. To address this problem we performed noise reduction (or denoising) by Wiener-like filtering in the wavelet domain. The filtering was applied to complex MRI data before construction of the magnitude image. The noise-reduction algorithm was applied to simulated and experimental diffusion-weighted (DW) images. Denoising considerably reduced the signal standard deviation (SD, by up to 87% in simulated images) and decreased the background noise floor (by approximately a factor of 6 in simulated and experimental images). Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

High-resolution 7T BOLD fMRI data can also allow for sophisticated surface-based and laminar-based analysis of the human cortex.4 Despite its obvious advantages, 7T BOLD fMRI studies still have remaining technical hurdles, among which geometric distortions are particularly challenging. Using these metrics, the authors concluded that B SB 0 sb inhomogeneity is the major cause of distortions compared with gradient nonlinearity, but that gradient-nonlinearity distortion correction is still necessary for precise surface-based BOLD fMRI analysis at 7T. It has been over 20 years since the first-ever 7T human magnetic resonance imaging (MRI) scanner was installed in 1999. [Extracted from the article]

Purpose To accelerate dynamic MR imaging through development of a novel image reconstruction technique using low-rank temporal signal models preestimated from training data. Theory We introduce the model consistency condition (MOCCO) technique, which utilizes temporal models to regularize reconstruction without constraining the solution to be low-rank, as is performed in related techniques. This is achieved by using a data-driven model to design a transform for compressed sensing-type regularization. The enforcement of general compliance with the model without excessively penalizing deviating signal allows recovery of a full-rank solution. Methods Our method was compared with a standard low-rank approach utilizing model-based dimensionality reduction in phantoms and patient examinations for time-resolved contrast-enhanced angiography (CE-MRA) and cardiac CINE imaging. We studied the sensitivity of all methods to rank reduction and temporal subspace modeling errors. Results MOCCO demonstrated reduced sensitivity to modeling errors compared with the standard approach. Full-rank MOCCO solutions showed significantly improved preservation of temporal fidelity and aliasing/noise suppression in highly accelerated CE-MRA (acceleration up to 27) and cardiac CINE (acceleration up to 15) data. Conclusions MOCCO overcomes several important deficiencies of previously proposed methods based on pre-estimated temporal models and allows high quality image restoration from highly undersampled CE-MRA and cardiac CINE data. Magn Reson Med 74:1279-1290, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose To characterize the local consistency by integrating temporal and spatial information in the local region using functional magnetic resonance imaging (fMRI). Materials and Methods One simulation was implemented to explain the definition of FOur-dimensional (spatiotemporal) Consistency of local neural Activities (FOCA). Then three experiments included resting state data (33 subjects), resting state reproducibility data (16 subjects), and event state data (motor execution task, 26 subjects) were designed. Finally, FOCA were respectively analyzed using statistical analysis methods, such as one-sample t-test and paired t-test, etc. Results During resting state (Experiment 1), the FOCA values ( P < 0.05, family-wise error [FWE] corrected, voxel size >621 mm3) were found to be distinct at the bilateral inferior frontal gyrus, middle frontal gyrus, angular gyrus, and precuneus/cuneus. In Experiment 2 (reproducibility), a high degree of consistency within subjects (correlation ≈0.8) and between subjects (correlation ≈0.6) of FOCA were obtained. Comparing event with resting state in Experiment 3, enhanced FOCA ( P < 0.05, FWE-corrected, voxel size >621 mm3) was observed mainly in the precentral gyrus and lingual gyrus. Conclusion These findings suggest that FOCA has the potential to provide further information that will help to better understand brain function in neural imaging. J. Magn. Reson. Imaging 2015;42:729-736. [ABSTRACT FROM AUTHOR]

Purpose In functional MRI (fMRI), faster sampling of data can provide richer temporal information and increase temporal degrees of freedom. However, acceleration is generally performed on a volume-by-volume basis, without consideration of the intrinsic spatio-temporal data structure. We present a novel method for accelerating fMRI data acquisition, k-t FASTER (FMRI Accelerated in Space-time via Truncation of Effective Rank), which exploits the low-rank structure of fMRI data. Theory and Methods Using matrix completion, 4.27× retrospectively and prospectively under-sampled data were reconstructed (coil-independently) using an iterative nonlinear algorithm, and compared with several different reconstruction strategies. Matrix reconstruction error was evaluated; a dual regression analysis was performed to determine fidelity of recovered fMRI resting state networks (RSNs). Results The retrospective sampling data showed that k-t FASTER produced the lowest error, approximately 3-4%, and the highest quality RSNs. These results were validated in prospectively under-sampled experiments, with k-t FASTER producing better identification of RSNs than fully sampled acquisitions of the same duration. Conclusion With k-t FASTER, incoherently under-sampled fMRI data can be robustly recovered using only rank constraints. This technique can be used to improve the speed of fMRI sampling, particularly for multivariate analyses such as temporal independent component analysis. Magn Reson Med 74:353-364, 2015. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Multiparametric medical imaging data can be large and are often complex. Machine learning algorithms can assist in image interpretation when reliable training data exist. In most cases, however, knowledge about ground truth (e.g. histology) and thus training data is limited, which makes application of machine learning algorithms difficult. The purpose of this study was to design and implement a learning algorithm for classification of multidimensional medical imaging data that is robust and accurate even with limited prior knowledge and that allows for generalization and application to unseen data. Local prostate cancer was chosen as a model for application and validation. 16 patients underwent combined simultaneous [11C]-choline positron emission tomography (PET)/MRI. The following imaging parameters were acquired: T2 signal intensities, apparent diffusion coefficients, parameters Ktrans and Kep from dynamic contrast-enhanced MRI, and PET standardized uptake values (SUVs). A spatially constrained fuzzy c-means algorithm (sFCM) was applied to the single datasets and the resulting labeled data were used for training of a support vector machine (SVM) classifier. Accuracy and false positive and false negative rates of the proposed algorithm were determined in comparison with manual tumor delineation. For five of the 16 patients rates were also determined in comparison with the histopathological standard of reference. The combined sFCM/SVM algorithm proposed in this study revealed reliable classification results consistent with the histopathological reference standard and comparable to those of manual tumor delineation. sFCM/SVM generally performed better than unsupervised sFCM alone. We observed an improvement in accuracy with increasing number of imaging parameters used for clustering and SVM training. In particular, including PET SUVs as an additional parameter markedly improved classification results. A variety of applications are conceivable, especially for imaging of tissues without easily available histopathological correlation. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization. Purpose: We propose a physics‐informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with high accuracy. Methods: The framework uses physics‐informed neural networks (PINNs) to learn the voxel‐wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov‐regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high‐grade gliomas. Results: The simulation results demonstrate that PINN‐derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast‐to‐noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN‐predicted perfusion parameters (Pearson's rho for nCBF: 0.84±0.03$0.84 \pm 0.03$ and nCBV: 0.92±0.03$0.92 \pm 0.03$) and very high indices of image similarity (structural similarity index for nCBF: 0.91±0.03$0.91 \pm 0.03$ and for nCBV: 0.98±0.00$0.98 \pm 0.00$). Conclusions: PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy. [ABSTRACT FROM AUTHOR]

Objective: Dystonic posturing (DP) is a common semiology in temporal lobe epilepsy (TLE). We aimed to explore cerebellar gradient alterations in functional connectivity in TLE patients with and without DP. Methods: Resting‐state functional MRI data were obtained in 60 TLE patients and 32 matched healthy controls. Patients were further divided into two groups: TLE with DP (TLE + DP, 31 patients) and TLE without DP (TLP‐DP, 29 patients). We explored functional gradient alterations in the cerebellum based on cerebellar–cerebral functional connectivity and combined with independent component analysis to evaluate cerebellar–cerebral functional integration and reveal the contribution of the motor components to the gradient. Results: There were no obvious differences in clinical features and postoperative seizure outcomes between TLE + DP and TLE‐DP patients. Patients and controls all showed a clear unimodal‐to‐transmodal gradient transition in the cerebellum, while TLE patients demonstrated an extended principal gradient in functional connectivity compared to healthy controls, which was more limited in TLE + DP patients. Gradient alterations were more widespread in TLE‐DP patients, involving bilateral cerebellum, while gradient alterations in TLE + DP patients were limited in the cerebellum ipsilateral to the seizure focus. In addition, more cerebellar motor components contributed to the gradient alterations in TLE + DP patients, mainly in ipsilateral cerebellum. Significance: Extended cerebellar principal gradients in functional connectivity revealed excessive functional segregation between unimodal and transmodal systems in TLE. The functional connectivity gradients were more limited in TLE + DP patients. Functional connectivity in TLE patients with dystonic posturing involved more contribution of cerebellar motor function to ipsilateral cerebellar gradient. Plain Language Summary: Dystonic posturing contralateral to epileptic focus is a common symptom in temporal lobe epilepsy, and the cerebellum may be involved in its generation. In this study, we found cerebellar gradients alterations in functional connectivity in temporal lobe epilepsy patients with and without contralateral dystonic posturing. In particular, we found that TLE patients with dystonic posturing may have more limited cerebellar gradient in functional connectivity, involving more contribution of cerebellar motor function to ipsilateral cerebellar gradient. Our study suggests a close relationship between ipsilateral cerebellum and contralateral dystonic posturing. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) is considered to initiate cerebrovascular pathology, involving in the development of multiple forms of neurodegeneration. However, it is unknown the relationships between imaging marker of cerebrovascular injury (white matter hyperintensity, WMH), its load on white matter tract and disrupted brain dynamics with cognitive function in mild TBI (mTBI). MRI data and neuropsychological assessments were collected from 85 mTBI patients and 52 healthy controls. Between‐group difference was conducted for the tract‐specific WMH volumes, white matter integrity, and dynamic brain connectivity (i.e., fractional occupancies [%], dwell times [seconds], and state transitions). Regression analysis was used to examine associations between white matter damage, brain dynamics, and cognitive function. Increased WMH volumes induced by mTBI within the thalamic radiation and corpus callosum were highest among all tract fibers, and related with altered fractional anisotropy (FA) within the same tracts. Clustering identified two brain states, segregated state characterized by the sparse inter‐independent component connections, and default mode network (DMN)‐centered integrated state with strongly internetwork connections between DMN and other networks. In mTBI, higher WMH loads contributed to the longer dwell time and larger fractional occupancies in DMN‐centered integrated state. Every 1 mL increase in WMH volume within the left thalamic radiation was associated with a 47% increase fractional occupancies, and contributed to 65.6 s delay in completion of cognitive processing speed test. Our study provided the first evidence for the structural determinants (i.e., small vessel lesions) that mediate the spatiotemporal brain dynamics to cognitive impairments in mTBI. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer. Accurate preoperative prediction of histological grade holds potential for improving clinical management and disease prognostication. Purpose: To evaluate the performance of a radiomics signature based on multiphase MRI in assessing histological grade in solitary HCC. Study Type: Retrospective. Subjects: A total of 405 patients with histopathologically confirmed solitary HCC and with liver gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced MRI within 1 month of surgery. Field Strength/Sequence: Contrast‐enhanced T1‐weighted spoiled gradient echo sequence (LAVA) at 1.5 or 3.0 T. Assessment: Tumors were graded (low/high) according to results of histopathology. Basic clinical characteristics (including age, gender, serum alpha‐fetoprotein (AFP) level, history of hepatitis B, and cirrhosis) were collected and tumor size measured. Radiomics features were extracted from Gd‐EOB‐DTPA‐enhanced MRI data. Three feature selection strategies were employed sequentially to identify the optimal features: SelectFromModel (SFM), SelectPercentile (SP), and recursive feature elimination with cross‐validation (RFECV). Probabilities of five single‐phase radiomics‐based models were averaged to generate a radiomics signature. A combined model was built by combining the radiomics signature and clinical predictors. Statistical Tests: Pearson χ2 test/Fisher exact test, Wilcoxon rank sum test, interclass correlation coefficient (ICC), univariable/multivariable logistic regression analysis, area under the receiver operating characteristic (ROC) curve (AUC), DeLong test, calibration curve, Brier score, decision curve, Kaplan–Meier curve, and log‐rank test. A P‐value <0.05 was considered statistically significant. Results: High‐grade HCCs were present in 33.8% of cases. AFP levels (odds ratio [OR] 1.89) and tumor size (>5 cm; OR 2.33) were significantly associated with HCC grade. The combined model had excellent performance in assessing HCC grade in the test dataset (AUC: 0.801), and demonstrated satisfactory calibration and clinical utility. Data Conclusion: A model that combined a radiomics signature derived from preoperative multiphase Gd‐EOB‐DTPA‐enhanced MRI and clinical predictors showed good performance in assessing HCC grade. Level of Evidence: 3 Technical Efficacy: Stage 5 [ABSTRACT FROM AUTHOR]

Background: Myocardial hypoxia has been demonstrated in many cardiomyopathies and is related to development of myocardial fibrosis. However, myocardial hypoxia and its association with myocardial fibrosis are understudied in Duchenne muscular dystrophy (DMD)‐associated cardiomyopathy. Purpose: To evaluate myocardial hypoxia by oxygenation‐sensitive (OS) cardiac magnetic resonance imaging, and further explore its association with fibrosis. Study Type: Prospective. Subjects: Ninety‐one DMD boys (8.78 ± 2.32) and 30 healthy boys (9.07 ± 2.30). Field Strength/Sequence: 3 T, Balanced steady‐state free procession, Modified Look‐Locker inversion recovery sequence and Single‐shot phase‐sensitive inversion recovery sequence. Assessment: Cardiac MRI data, including left ventricular functional, segmental native T1, and oxygenation signal‐intensity (SI) according to AHA 17‐segment model, were acquired. Patients were divided into LGE+ and LGE− groups. In patients with LGE, all segments were further classified as positive or negative segments by segmentally presence/absence of LGE. Statistical Tests: Variables were compared using Student's t, Wilcoxon, Kruskal–Wallis test and one‐way analysis of variance. Bivariate Pearson or Spearman correlation were calculated to determine association between oxygenation SI and native T1. Variables with P < 0.10 in the univariable analysis were included in multivariable model. Receiver operating characteristic analysis was used to assess the performance of OS in diagnosing myocardial hypoxia. Results: The myocardial oxygenation SI of DMD was significantly decreased in all segments compared with normal controls, and more obvious in the LGE+ segments (0.46 ± 0.03 vs. 0.52 ± 0.03). For patients with and without LGE, myocardial oxygenation SI were significantly negatively correlated with native T1 in all segments (r = −0.23 to −0.42). The inferolateral oxygenation SI was a significant independent associator of LGE presence (adjusted OR = 0.900). Data Conclusion: Myocardial hypoxia evaluated by the OS‐Cardiac‐MRI indeed occurs in DMD and associate with myocardial fibrosis, which might be used as a biomarker in assessing myocardial damage in DMD. Evidence Level: 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance (IR) and is often accompanied by mild cognitive impairment (MCI). The detrimental effects of T2DM and IR on the hippocampus have been extensively demonstrated. Few studies have examined the effects of IR on structure and function of hippocampal subfields in T2DM‐MCI patients. Method: A total of 104 T2DM patients were recruited in this prospective study and divided into four groups (T2DM‐MCI‐higherIR, n = 17; T2DM‐MCI‐lowerIR, n = 32; T2DM‐nonMCI‐higherIR, n = 19; T2DM‐nonMCI‐lowerIR, n = 36). Structure and function MRI data were captured. Clinical variables and neuropsychological scores were determined for all participants. Hippocampal subfield volume and functional connectivity were compared among four groups. Partial correlation analysis was performed between imaging indicators, clinical variables, and neuropsychological scores in all patients. Results: T2DM‐MCI‐higher IR group had the smallest volumes of bilateral hippocampal tail, right subiculum‐body, right GC‐ML‐DG‐body, and right CA4‐body. IR in right hippocampal tail, right subiculum‐body, and right GC‐ML‐DG‐body exerted main effect. Furthermore, elevated functional connectivity was found between right subiculum‐body and bilateral dorsolateral prefrontal cortex and right anterior cingulate–medial prefrontal cortex. Hippocampal subfield volume positively correlates with total cholesterol and triglycerides and negatively correlates with fasting insulin. Conclusion: The present study found that T2DM‐MCI patients have structural and functional alterations in hippocampal subfields, and IR is a negative factor influencing the alteration of hippocampal subfields volume. These findings support the importance of IR in T2DM‐MCI patients and might be potential neuroimaging biomarkers of cerebral impairment in T2DM‐MCI patients. [ABSTRACT FROM AUTHOR]

Macroscale functional gradient techniques provide a continuous coordinate system that extends from unimodal regions to transmodal higher‐order networks. However, the alterations of these functional gradients in AD and their correlations with cognitive terms and gene expression profiles remain to be established. In the present study, we directly studied the functional gradients with functional MRI data from seven scanners. We adopted data‐driven meta‐analytic techniques to unveil AD‐associated changes in the functional gradients. The principal primary‐to‐transmodal gradient was suppressed in AD. Compared to NCs, AD patients exhibited global connectome gradient alterations, including reduced gradient range and gradient variation, increased gradient scores in the somatomotor, ventral attention, and frontoparietal regions, and decreased in the default mode network. More importantly, the Gene Ontology terms of biological processes were significantly enriched in the potassium ion transport and protein‐containing complex remodeling. Our compelling evidence provides a new perspective in understanding the connectome alterations in AD. [ABSTRACT FROM AUTHOR]

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter‐dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the clinical characteristics and predictors of relapse in double negative atypical inflammatory demyelinating disease (IDD) and to explore potential antigenic targets by tissue‐based assays (TBA) using rat brain indirect immunofluorescence. Methods: We compared the clinical, laboratory, and MRI data of double negative atypical IDD with other IDD patients. Serum samples were collected for TBA. The predictors of relapse were examined over a minimum of 24 months follow‐up. Results: In our cohort of 98 patients with double negative atypical IDD, there was no significant female predominance (58.2%, 57/98). The lesions primarily affected the spinal cord and brain stem, with fewer cases of involvement in the area postrema (5.1%, 5/98) and longitudinally extensive transverse myelitis (43.9%, 43/98). A total of 62.5% (50/80) patients tested positive for anti‐astrocyte antibodies based on rat brain TBA. Over a median duration of 39.5 months, 80 patients completed the entire follow‐up, and 47.5% (38/80) patients exhibited monophasic course. A total of 36% (18/50) patients positively for anti‐astrocyte antibodies had a monophasic course, which is significantly lower than patients negatively for anti‐astrocyte antibodies (66.7%, 20/30) (p = 0.008). The presence of anti‐astrocyte antibodies (hazard ratio (HR), 2.243; 95% CI, 1.087–4.627; p = 0.029) and ≥4 cerebrum lesions at first attack (HR, 2.494; 95% CI, 1.224–5.078; p = 0.012) were risk factors for disease relapse, while maintenance immunotherapy during remission (HR, 0.361; 95% CI, 0.150–0.869; p = 0.023) was protective factor. Interpretation: Double negative atypical IDD are unique demyelinating diseases with a high relapse rate. Maintenance immunotherapy is helpful to the prevention of relapse, particularly in patients with anti‐astrocyte antibodies or ≥4 cerebrum lesions at first attack. [ABSTRACT FROM AUTHOR]

Background: Quantification of aortic morphology plays an important role in the evaluation and follow‐up assessment of patients with aortic diseases, but often requires labor‐intensive and operator‐dependent measurements. Automatic solutions would help enhance their quality and reproducibility. Purpose: To design a deep learning (DL)‐based automated approach for aortic landmarks and lumen detection derived from three‐dimensional (3D) MRI. Study Type: Retrospective. Population: Three hundred ninety‐one individuals (female: 47%, age = 51.9 ± 18.4) from three sites, including healthy subjects and patients (hypertension, aortic dilation, Turner syndrome), randomly divided into training/validation/test datasets (N = 236/77/78). Twenty‐five subjects were randomly selected and analyzed by three operators with different levels of expertise. Field Strength/Sequence: 1.5‐T and 3‐T, 3D spoiled gradient‐recalled or steady‐state free precession sequences. Assessment: Reinforcement learning and a two‐stage network trained using reference landmarks and segmentation from an existing semi‐automatic software were used for aortic landmark detection and segmentation from sinotubular junction to coeliac trunk. Aortic segments were defined using the detected landmarks while the aortic centerline was extracted from the segmentation and morphological indices (length, aortic diameter, and volume) were computed for both the reference and the proposed segmentations. Statistical Tests: Segmentation: Dice similarity coefficient (DSC), Hausdorff distance (HD), average symmetrical surface distance (ASSD); landmark detection: Euclidian distance (ED); model robustness: Spearman correlation, Bland–Altman analysis, Kruskal–Wallis test for comparisons between reference and DL‐derived aortic indices; inter‐observer study: Williams index (WI). A WI 95% confidence interval (CI) lower bound >1 indicates that the method is within the inter‐observer variability. A P‐value <0.05 was considered statistically significant. Results: DSC was 0.90 ± 0.05, HD was 12.11 ± 7.79 mm, and ASSD was 1.07 ± 0.63 mm. ED was 5.0 ± 6.1 mm. A good agreement was found between all DL‐derived and reference aortic indices (r >0.95, mean bias <7%). Our segmentation and landmark detection performances were within the inter‐observer variability except the sinotubular junction landmark (CI = 0.96;1.04). Data Conclusion: A DL‐based aortic segmentation and anatomical landmark detection approach was developed and applied to 3D MRI data for achieve aortic morphology evaluation. Evidence Level: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

Quantitative analysis of diffusion‐weighted magnetic resonance imaging (DW‐MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW‐MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel‐wise fitting are often unreliable. In this study, we propose a kernelized total difference‐based curve‐fitting method to estimate the IVIM parameters. Simulated DW‐MRI data at five signal‐to‐noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW‐MRI data acquired on a 1.5‐T MRI scanner with nine b‐values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion‐encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust‐region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root‐mean‐square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference‐based curve‐fitting method has the potential to improve the reliability of IVIM parametric imaging. [ABSTRACT FROM AUTHOR]