arXiv subjects

Ouri Cohen

Publications and source records attributed to Ouri Cohen.

8 recordsLinked to original sources

arXiv2026

Quantitative Chemical Exchange Saturation Transfer Imaging with Golden-Angle Radial k-Space and Locally Low-Rank Reconstruction

Chemical exchange saturation transfer magnetic resonance fingerprinting (CEST-MRF) is a promising quantitative molecular imaging technique. To reduce scan time, current CEST-MRF implementations typically use echo-planar imaging (EPI) readouts, which are prone to geometric distortion and signal dropout. In this study, we developed a motion-robust geometrically accurate CEST-MRF method using radial k-space sampling, locally low-rank reconstruction, and neural network-based quantification. The acquisition schedule was optimized using deep learning, and its accuracy was validated in numerical simulations with digital phantoms. The number of spokes per measurement was determined through simulations and in vivo ablation studies in healthy volunteers. Five healthy subjects underwent repeated scans, and regions of interest were defined for analysis. Tissue maps generated with the proposed method were compared with values obtained from nonlinear least-squares (NLS) fitting of multi-saturation-power z-spectra. Motion sensitivity and test-retest reproducibility were evaluated using the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Clinical feasibility was demonstrated in a subject with a history of remote middle cerebral artery infarction. The results show that 3D quantitative CEST maps can be acquired in 11 minutes using 34 spokes per measurement. Numerical simulations yielded mean errors below 14% for all tissue parameters, while in vivo parameter estimates agreed well with both NLS-derived values and prior brain CEST-MRF studies. The mean ICC across all tissue maps was 0.92 in white matter and 0.87 in gray matter, with mean inter-subject CVs of 5.4% and 3.4%, respectively. For the radial acquisition, the mean error between motion and no-motion conditions was 8.6%. Changes in CEST-MRF tissue parameters were consistent with clinically diagnosed cystic gliosis.

physics.med-ph↗

arXiv2024

DCE-Qnet: Deep Network Quantification of Dynamic Contrast Enhanced (DCE) MRI

Introduction: Quantification of dynamic contrast-enhanced (DCE)-MRI has the potential to provide valuable clinical information, but robust pharmacokinetic modeling remains a challenge for clinical adoption. Methods: A 7-layer neural network called DCE-Qnet was trained on simulated DCE-MRI signals derived from the Extended Tofts model with the Parker arterial input function. Network training incorporated B1 inhomogeneities to estimate perfusion (Ktrans, vp, ve), tissue T1 relaxation, proton density and bolus arrival time (BAT). The accuracy was tested in a digital phantom in comparison to a conventional nonlinear least-squares fitting (NLSQ). In vivo testing was conducted in 10 healthy subjects. Regions of interest in the cervix and uterine myometrium were used to calculate the inter-subject variability. The clinical utility was demonstrated on a cervical cancer patient. Test-retest experiments were used to assess reproducibility of the parameter maps in the tumor. Results: The DCE-Qnet reconstruction outperformed NLSQ in the phantom. The coefficient of variation (CV) in the healthy cervix varied between 5-51% depending on the parameter. Parameter values in the tumor agreed with previous studies despite differences in methodology. The CV in the tumor varied between 1-47%. Conclusion: The proposed approach provides comprehensive DCE-MRI quantification from a single acquisition. DCE-Qnet eliminates the need for separate T1 scan or BAT processing, leading to a reduction of 10 minutes per scan and more accurate quantification.

physics.med-ph↗

arXiv2022

Rapid 3D Multiparametric Mapping of Brain Metastases with Deep Learning-Based Phase-Sensitive MR Fingerprinting

In MR fingerprinting (MRF) reconstruction, measured data is pattern-matched to simulated signals to extract quantitative tissue parameters. A critical drawback to this approach is the exponentially increasing compute time for mapping of multiple parameters. Previously, a deep learning (DL) reconstruction method called DRONE was shown to overcome this constraint by mapping the magnitude time-series signal to the underlying tissue parameters. However, relaxometry from magnitude images is susceptible to errors arising from ambiguities in the zero crossing of the signal or the non-zero noise mean. The aim of this study is to develop rapid acquisition and quantification methods to enable accurate multiparametric tissue mapping from complex data. An optimized EPI based MRF sequence is developed along with a novel phasesensitive DL quantification allowing the use of real-valued neural networks to reconstruct complex measured data and providing an additional quantitative map of the phase. Phantom experiments demonstrate the accuracy of the proposed approach. A comparison to previous DRONE methods in a healthy subject shows improved fidelity to known T1 and T2 values for the phase-sensitive approach. By processing the estimated phase map with conventional quantitative susceptibility mapping algorithms, we demonstrate the feasibility of simultaneous quantification of proton density, T1, T2, transmitter B1+ field and the quantitative susceptibility maps. In vivo experiments in a healthy volunteer and a subject with metastatic brain cancer are used to illustrate potential applications of this technology for treatment response assessment and tumor characterization.

physics.med-ph↗

arXiv2021

CEST MR fingerprinting (CEST-MRF) for Brain Tumor Quantification Using EPI Readout and Deep Learning Reconstruction

$\textbf{Purpose}$: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. $\textbf{Methods}$: A CEST-MRF pulse sequence originally designed for animal imaging was modified to conform to hardware limits on clinical scanners while keeping scan time $\leq$ 2 minutes. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the 6 parameter DRONE reconstruction was tested in simulations in a digital brain phantom. A healthy subject was scanned with the CEST-MRF sequence, conventional MRF and CEST sequences for comparison. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient (CCC) calculated for white matter (WM) and grey matter (GM). The clinical utility of CEST-MRF was demonstrated in 4 patients with brain metastases in comparison to standard clinical imaging sequences. Tumors were segmented into edema, solid core and necrotic core regions and the CEST-MRF values compared to the contra-lateral side. $\textbf{Results}$: The DRONE reconstruction of the digital phantom yielded a normalized RMS error of $\leq$ 7% for all parameters. The CEST-MRF parameters were in good agreement with those from conventional MRF and CEST sequences and previous studies. The mean CCC for all 6 parameters was 0.98$\pm$0.01 in WM and 0.98$\pm$0.02 in GM. The CEST-MRF values in nearly all tumor regions were significantly different (P=0.05) from each other and the contra-lateral side. $\textbf{Conclusion}$: Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.

physics.med-ph↗

arXiv2019

CEST MR-Fingerprinting: practical considerations and insights for acquisition schedule design and improved reconstruction

Purpose: To understand the influence of various acquisition parameters on the ability of CEST MR-Fingerprinting (MRF) to discriminate different chemical exchange parameters and to provide tools for optimal acquisition schedule design and parameter map reconstruction. Methods: Numerical simulations were conducted using a parallel-computing implementation of the Bloch-McConnell equations, examining the effect of TR, TE, flip-angle, water T$_{1}$ and T$_{2}$, saturation-pulse duration, power, and frequency on the discrimination ability of CEST-MRF. A modified Euclidean-distance matching metric was evaluated and compared to traditional dot-product matching. L-Arginine phantoms of various concentrations and pH were scanned at 4.7T and the results compared to numerical findings. Results: Simulations for dot-product matching demonstrated that the optimal flip-angle and saturation times are 30$^{\circ}$ and 1100 ms, respectively. The optimal maximal saturation power was 3.4 $μ$T for concentrated solutes with a slow exchange-rate, and 5.2 $μ$T for dilute solutes with medium-to-fast exchange-rates. Using the Euclidean-distance matching metric, much lower maximum saturation powers were required (1.6 and 2.4 $μ$T, respectively), with a slightly longer saturation time (1500 ms) and 90$^{\circ}$ flip-angle. For both matching metrics, the discrimination ability increased with the repetition time. The experimental results were in agreement with simulations, demonstrating that more than a 50% reduction in scan-time can be achieved by Euclidean-distance-based matching. Conclusion: Optimization of the CEST-MRF acquisition schedule is critical for obtaining the best exchange parameter accuracy. The use of Euclidean-distance-based matching of signal trajectories simultaneously improved the discrimination ability and reduced the scan time and maximal saturation power required.

physics.med-ph↗

arXiv2018

Ex Vivo Mouse Brain Microscopy at 15T with Loop-Gap RF Coil

The design of a loop-gap-resonator RF coil optimized for ex vivo mouse brain microscopy at ultra high fields is described and its properties characterized using simulations, phantoms and experimental scans of mouse brains fixed in 10% formalin containing 4 mM Magnevist. The RF (B1) and magnetic field (B0) homogeneities are experimentally quantified and compared to electromagnetic simulations of the coil. The coil's performance is also compared to a similarly sized surface coil and found to yield double the sensitivity. A three-dimensional gradient-echo (GRE) sequence is used to acquire high resolution mouse brain scans at 47 μm3 resolution in 1.8 hours and a 20x20x19 μm3 resolution in 27 hours. The high resolution obtained permitted clear visualization and identification of multiple structures in the ex vivo mouse brain and represents, to our knowledge, the highest resolution ever achieved for a whole mouse brain. Importantly, the coil design is simple and easy to construct.

physics.med-ph↗

arXiv2017

Rapid and Quantitative Chemical Exchange Saturation Transfer (CEST) Imaging with Magnetic Resonance Fingerprinting (MRF)

Purpose: To develop a fast magnetic resonance fingerprinting (MRF) method for quantitative chemical exchange saturation transfer (CEST) imaging. Methods: We implemented a CEST-MRF method to quantify the chemical exchange rate and volume fraction of the N${\alpha}$-amine protons of L-arginine (L-Arg) phantoms and the amide and semi-solid exchangeable protons of in vivo rat brain tissue. L-Arg phantoms were made with different concentrations (25-100 mM) and pH (pH 4-6). The MRF acquisition schedule varied the saturation power randomly for 30 iterations (phantom: 0-6 ${\mu}$T; in vivo: 0-4 ${\mu}$T) with a total acquisition time of <=2 minutes. The signal trajectories were pattern-matched to a large dictionary of signal trajectories simulated using the Bloch-McConnell equations for different combinations of exchange rate, exchangeable proton volume fraction, and water T1 and T2* relaxation times. Results: The chemical exchange rates of the N${\alpha}$-amine protons of L-Arg were significantly (p<0.0001) correlated with the rates measured with the Quantitation of Exchange using Saturation Power method. Similarly, the L-Arg concentrations determined using MRF were significantly (p<0.0001) correlated with the known concentrations. The pH dependence of the exchange rate was well fit (R2=0.9186) by a base catalyzed exchange model. The amide proton exchange rate measured in rat brain cortex (36.3+-12.9 Hz) was in good agreement with that measured previously with the Water Exchange spectroscopy method (28.6+-7.4 Hz). The semi-solid proton volume fraction was elevated in white (11.2+-1.7%) compared to gray (7.6+-1.8%) matter brain regions in agreement with previous magnetization transfer studies. Conclusion: CEST-MRF provides a method for fast, quantitative CEST imaging.

physics.med-ph↗

arXiv2017

MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

PURPOSE: Demonstrate a novel fast method for reconstruction of multi-dimensional MR Fingerprinting (MRF) data using Deep Learning methods. METHODS: A neural network (NN) is defined using the TensorFlow framework and trained on simulated MRF data computed using the Bloch equations. The accuracy of the NN reconstruction of noisy data is compared to conventional MRF template matching as a function of training data size, and quantified in a both simulated numerical brain phantom data and acquired data from the ISMRM/NIST phantom. The utility of the method is demonstrated in a healthy subject in vivo at 1.5 T. RESULTS: Network training required 10 minutes and once trained, data reconstruction required approximately 10 ms. Reconstruction of simulated brain data using the NN resulted in a root-mean-square error (RMSE) of 3.5 ms for T1 and 7.8 ms for T2. The RMSE for the NN trained on sparse dictionaries was approximately 6 fold lower for T1 and 2 fold lower for T2 than conventional MRF dot-product dictionary matching on the same dictionaries. Phantom measurements yielded good agreement (R2=0.99) between the T1 and T2 estimated by the NN and reference values from the ISMRM/NIST phantom. CONCLUSION: Reconstruction of MRF data with a NN is accurate, 300 fold faster and more robust to noise and undersampling than conventional MRF dictionary matching.

cs.CV↗