arXiv subjects

Ricardo Otazo

Publications and source records attributed to Ricardo Otazo.

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↗

arXiv2026

Spatiotemporal Gaussian representation-based dynamic reconstruction and motion estimation framework for time-resolved volumetric MR imaging (DREME-GSMR)

Time-resolved volumetric MR imaging that reconstructs a 3D MRI within sub-seconds to resolve deformable motion is essential for motion-adaptive radiotherapy. Representing patient anatomy and associated motion fields as 3D Gaussians, we developed a spatiotemporal Gaussian representation-based framework (DREME-GSMR), which enables time-resolved dynamic MRI reconstruction from a pre-treatment 3D MR scan without any prior anatomical/motion model. DREME-GSMR represents a reference MRI volume and a corresponding low-rank motion model (as motion-basis components) using 3D Gaussians, and incorporates a dual-path MLP/CNN motion encoder to estimate temporal motion coefficients of the motion model from raw k-space-derived signals. Furthermore, using the solved motion model, DREME-GSMR can infer motion coefficients directly from new online k-space data, allowing subsequent intra-treatment volumetric MR imaging and motion tracking (real-time imaging). A motion-augmentation strategy is further introduced to improve robustness to unseen motion patterns during real-time imaging. DREME-GSMR was evaluated on the XCAT digital phantom, a physical motion phantom, and MR-LINAC datasets acquired from 6 healthy volunteers and 20 patients (with independent sequential scans for cross-evaluation). DREME-GSMR reconstructs MRIs of a ~400ms temporal resolution, with an inference time of ~10ms/volume. In XCAT experiments, DREME-GSMR achieved mean(s.d.) SSIM, tumor center-of-mass-error(COME), and DSC of 0.92(0.01)/0.91(0.02), 0.50(0.15)/0.65(0.19) mm, and 0.92(0.02)/0.92(0.03) for dynamic reconstruction/real-time imaging. For the physical phantom, the mean target COME was 1.19(0.94)/1.40(1.15) mm for dynamic/real-time imaging, while for volunteers and patients, the mean liver COME for real-time imaging was 1.31(0.82) and 0.96(0.64) mm, respectively.

physics.med-ph↗

arXiv2025

A dynamic reconstruction and motion estimation framework for cardiorespiratory motion-resolved real-time volumetric MR imaging (DREME-MR)

Based on a 3D pre-treatment magnetic resonance (MR) scan, we developed DREME-MR to jointly reconstruct the reference patient anatomy and a data-driven, patient-specific cardiorespiratory motion model. Via a motion encoder simultaneously learned during the reconstruction, DREME-MR further enables real-time volumetric MR imaging and cardiorespiratory motion tracking with minimal intra treatment k-space data. From a 3D radial-spoke-based pre-treatment MR scan, DREME-MR uses spatiotemporal implicit-neural-representation (INR) to reconstruct pre-treatment dynamic volumetric MR images (learning task 1). The INR-based reconstruction takes a joint image reconstruction and deformable registration approach, yielding a reference anatomy and a corresponding cardiorespiratory motion model. The motion model adopts a low-rank, multi-resolution representation to decompose motion fields as products of motion coefficients and motion basis components (MBCs). Via a progressive, frequency-guided strategy, DREME-MR decouples cardiac MBCs from respiratory MBCs to resolve the two distinct motion modes. Simultaneously with the pre-treatment dynamic MRI reconstruction, DREME-MR also trains an INR-based motion encoder to infer cardiorespiratory motion coefficients directly from the raw k-space data (learning task 2), allowing real-time, intra-treatment volumetric MR imaging and motion tracking with minimal k-space data (20-30 spokes) acquired after the pre-treatment MRI scan. Evaluated using data from a digital phantom (XCAT) and a human scan, DREME-MR solves real-time 3D cardiorespiratory motion with a latency of < 165 ms (= 150-ms data acquisition + 15-ms inference time), fulfilling the temporal constraint of real-time imaging.

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↗

arXiv2022

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↗

arXiv2020

Accelerating Prostate Diffusion Weighted MRI using Guided Denoising Convolutional Neural Network: Retrospective Feasibility Study

Purpose: To investigate feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN). Materials and Methods: Raw data from the prostate DWI scans were retrospectively gathered (between July 2018 and July 2019) from six single-vendor MRI scanners. 118 data sets were used for training and validation (age: 64.3 +- 8 years) and 37 - for testing (age: 65.1 +- 7.3 years). High b-value diffusion-weighted (hb-DW) data were reconstructed into noisy images using two averages and reference images using all sixteen averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DWI image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb-DW images. A cumulative link mixed regression model was used to compare the readers scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland Altman analysis. Results: Compared to the DnCNN, the guided DnCNN produced denoised hb-DW images with higher peak signal-to-noise ratio and structural similarity index and lower normalized mean square error (p < 0.001). Compared to the reference images, the denoised images received higher image quality scores (p < 0.0001). The ADC values based on the denoised hb-DW images were in good agreement with the reference ADC values. Conclusion: Accelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible.

eess.IV↗

arXiv2018

Low Rank plus Sparse Decomposition of ODFs for Improved Detection of Group-level Differences and Variable Correlations in White Matter

A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis. While these approaches may detect large group differences, they do not fully capitalize on all acquired image volumes. Incorporation of all available diffusion information in the analysis however risks biasing the outcome by outliers. Here we propose a statistical analysis method operating on the ODF, either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably detect voxelwise group differences and correlations with demographic or behavioral variables, we apply the Low-Rank plus Sparse (L + S) matrix decomposition on the voxelwise ODFs which separates the sparse individual variability in the sparse matrix S whilst recovering the essential ODF features in the low-rank matrix L. We demonstrate the performance of this ODF L + S approach by replicating the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset. The volume of positive findings agrees with and expands on the volume found by TBSS, Connectivity based fixel enhancement and Connectometry. In the same dataset we further localize the correlations of brain structure with neurocognitive measures such as fluid intelligence and episodic memory. The presented ODF L + S approach will aid in the full utilization of all acquired diffusion weightings leading to the detection of smaller group differences in clinically relevant settings as well as in neuroscience applications.

physics.med-ph↗