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.

MR signature matching (MRSIGMA) implementation for true real-time free-breathing volumetric imaging with sub-200 ms latency on an MR-Linac.

PURPOSE: Develop a true real-time implementation of MR signature matching (MRSIGMA) for free-breathing 3D MRI with sub-200 ms latency on the Elekta Unity 1.5T MR-Linac. METHODS: MRSIGMA was implemented on an external computer with a network connection to the MR-Linac. Stack-of-stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground-truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real-time implementation of MRSIGMA. Dice coefficients between real-time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. RESULTS: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3-1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum-stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. CONCLUSION: The real-time implementation of MRSIGMA enabled true real-time free-breathing 3D MRI with sub-200 ms imaging latency on a clinical MR-Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion.

Phase-sensitive deep reconstruction method for rapid multiparametric MR fingerprinting and quantitative susceptibility mapping in the brain.

INTRODUCTION: This study explores the potential of Magnetic Resonance Fingerprinting (MRF) with a novel Phase-Sensitivity Deep Reconstruction Network (PS-DRONE) for simultaneous quantification of T1, T2, Proton Density, B1+, phase and quantitative susceptibility mapping (QSM). METHODS: Data were acquired at 3 T in vitro and in vivo using an optimized EPI-based MRF sequence. Phantom experiments were conducted using a standardized phantom for T1 and T2 maps and a custom-made agar-based gadolinium phantom for B1 and QSM maps. In vivo experiments included five healthy volunteers and one patient diagnosed with brain metastasis. PSDRONE maps were compared to reference maps obtained through standard imaging sequences. RESULTS: Total scan time was 2 min for 32 slices and a resolution of [1 mm, 1 mm, 4.5 mm]. The reconstruction of T1, T2, Proton Density, B1+ and phase maps were reconstructed within 1 s. In the phantoms, PS-DRONE analysis presented accurate and strongly correlated T1 and T2 maps (r = 0.99) compared to the reference maps. B1 maps from PS-DRONE showed slightly higher values, though still correlated (r = 0.6) with the reference. QSM values showed a small bias but were strongly correlated (r = 0.99) with reference data. In the in vivo analysis, PS-DRONE-derived T1 and T2 values for gray and white matter matched reference values in healthy volunteers. PS-DRONE B1 and QSM maps showed strong correlations with reference values. CONCLUSION: The PS-DRONE network enables concurrent acquisition of T1, T2, PD, B1+, phase and QSM maps, within 2 min of acquisition time and 1 s of reconstruction time.

Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI.

PURPOSE: To develop a novel deep learning approach for 4D-MRI reconstruction, named Movienet, which exploits space-time-coil correlations and motion preservation instead of k-space data consistency, to accelerate the acquisition of golden-angle radial data and enable subsecond reconstruction times in dynamic MRI. METHODS: Movienet uses a U-net architecture with modified residual learning blocks that operate entirely in the image domain to remove aliasing artifacts and reconstruct an unaliased motion-resolved 4D image. Motion preservation is enforced by sorting the input image and reference for training in a linear motion order from expiration to inspiration. The input image was collected with a lower scan time than the reference XD-GRASP image used for training. Movienet is demonstrated for motion-resolved 4D MRI and motion-resistant 3D MRI of abdominal tumors on a therapeutic 1.5T MR-Linac (1.5-fold acquisition acceleration) and diagnostic 3T MRI scanners (2-fold and 2.25-fold acquisition acceleration for 4D and 3D, respectively). Image quality was evaluated quantitatively and qualitatively by expert clinical readers. RESULTS: The reconstruction time of Movienet was 0.69 s (4 motion states) and 0.75 s (10 motion states), which is substantially lower than iterative XD-GRASP and unrolled reconstruction networks. Movienet enables faster acquisition than XD-GRASP with similar overall image quality and improved suppression of streaking artifacts. CONCLUSION: Movienet accelerates data acquisition with respect to compressed sensing and reconstructs 4D images in less than 1 s, which would enable an efficient implementation of 4D MRI in a clinical setting for fast motion-resistant 3D anatomical imaging or motion-resolved 4D imaging.

Convolutional network denoising for acceleration of multi-shot diffusion MRI.

Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase.

Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system.

Objective. To develop real-time 4D MRI using MR signature matching (MRSIGMA) for volumetric motion imaging in patients with pancreatic cancer on a 1.5T MR-Linac system.Approach. Two consecutive MRI scans with 3D golden-angle radial stack-of-stars acquisitions were performed on ten patients with inoperable pancreatic cancer. The complete first scan (905 angles) was used to compute a 4D motion dictionary including ten pairs of 3D motion images and signatures. The second scan was used for real-time imaging, where each angle (275 ms) was processed separately to match it to one of the dictionary entries. The complete second scan was also used to compute a 4D reference to assess motion tracking performance.Dicecoefficients of the gross tumor volume (GTV) and two organs-at-risk (duodenum-stomach and small bowel) were calculated between signature matching and reference. In addition, volume changes, displacements, center of mass shifts, andDicescores over time were calculated to characterize motion.Main results. Total imaging latency of MRSIGMA (acquisition + matching) was less than 300 ms. TheDicecoefficients were 0.87 ± 0.06 (GTV), 0.86 ± 0.05 (duodenum-stomach), and 0.85 ± 0.05 (small bowel), which indicate high accuracy (high mean value) and low uncertainty (low standard deviation) of MRSIGMA for real-time motion tracking. The center of mass shift was 3.1 ± 2.0 mm (GTV), 5.3 ± 3.0 mm (duodenum-stomach), and 3.4 ± 1.5 mm (small bowel). TheDicescores over time (0.97 ± [0.01-0.03]) were similarly high for MRSIGMA and reference scans in all the three contours.Significance. This work demonstrates the feasibility of real-time 4D MRI using MRSIGMA for volumetric motion tracking on a 1.5T MR-Linac system. The high accuracy and low uncertainty of real-time MRSIGMA is an essential step towards continuous treatment adaptation of tumors affected by real-time respiratory motion and could ultimately improve treatment safety by optimizing ablative dose delivery near gastrointestinal organs.

Free-breathing high isotropic resolution quantitative susceptibility mapping (QSM) of liver using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction.

PURPOSE: To enable free-breathing and high isotropic resolution liver quantitative susceptibility mapping (QSM) using 3D multi-echo UTE cones acquisition and respiratory motion-resolved image reconstruction. METHODS: Using 3D multi-echo UTE cones MRI, a respiratory motion was estimated from the k-space center of the imaging data. After sorting the k-space data with estimated motion, respiratory motion state-resolved reconstruction was performed for multi-echo data followed by nonlinear least-squares fitting for proton density fat fraction (PDFF), R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , and fat-corrected B0 field maps. PDFF and B0 field maps were subsequently used for QSM reconstruction. The proposed method was compared with motion-averaged (gridding) reconstruction and conventional 3D multi-echo Cartesian MRI in moving gadolinium phantom and in vivo studies. Region of interest (ROI)-based linear regression analysis was performed on these methods to investigate correlations between gadolinium concentration and QSM in the phantom study and between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM in in vivo study. RESULTS: Cones with motion-resolved reconstruction showed sharper image quality compared to motion-averaged reconstruction with a substantial reduction of motion artifacts in both moving phantom and in vivo studies. For ROI-based linear regression analysis of the phantom study, susceptibility values from cones with motion-resolved reconstruction ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.31 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.05, R 2 $$ {R}^2 $$ = 0.999) and Cartesian without motion ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.32 × gadolinium mM + $$ \times {\mathrm{gadolinium}}_{\mathrm{mM}}+ $$ 0.04, R 2 $$ {R}^2 $$ = 1.000) showed linear relationships with gadolinium concentrations and showed good agreement with each other. For in vivo, motion-resolved reconstruction showed higher goodness of fit ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.00261 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.524, R 2 $$ {R}^2 $$ = 0.977) compared to motion-averaged reconstruction ( QSM ppm $$ {\mathrm{QSM}}_{\mathrm{ppm}} $$ = 0.0021 × R 2 s - 1 * - $$ \times {\mathrm{R}}_{2_{{\mathrm{s}}^{-1}}}^{\ast }- $$ 0.572, R 2 $$ {R}^2 $$ = 0.723) in ROI-based linear regression analysis between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM. CONCLUSION: Feasibility of free-breathing liver QSM was demonstrated with motion-resolved 3D multi-echo UTE cones MRI, achieving high isotropic resolution currently unachievable in conventional Cartesian MRI.

Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review.

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

Quantitative longitudinal mapping of radiation-treated prostate cancer using MR fingerprinting with radial acquisition and subspace reconstruction.

MR fingerprinting (MRF) enables fast multiparametric quantitative imaging with a single acquisition and has been shown to improve diagnosis of prostate cancer. However, most prostate MRF studies were performed with spiral acquisitions that are sensitive to B0 inhomogeneities and consequent blurring. In this work, a radial MRF acquisition with a novel subspace reconstruction technique was developed to enable fast T1/T2 mapping in the prostate in under 4 min. The subspace reconstruction exploits the extensive temporal correlations in the MRF dictionary to pre-compute a low dimensional space for the solution and thus reduce the number of radial spokes to accelerate the acquisition. Iterative reconstruction with the subspace model and additional regularization of the signal representation in the subspace is performed to minimize the number of spokes and maintain matching quality and SNR. Reconstruction accuracy was assessed using the ISMRM NIST phantom. In-vivo validation was performed on two healthy subjects and two prostate cancer patients undergoing radiation therapy. The longitudinal repeatability was quantified using the concordance correlation coefficient (CCC) in one of the healthy subjects by repeated scans over 1 year. One prostate cancer patient was scanned at three time points, before initiating therapy and following brachytherapy and external beam radiation. Changes in the T1/T2 maps obtained with the proposed method were quantified. The prostate, peripheral and transitional zones, and visible dominant lesion were delineated for each study, and the statistics and distribution of the quantitative mapping values were analyzed. Significant image quality improvements compared with standard reconstruction methods were obtained with the proposed subspace reconstruction method. A notable decrease in the spread of the T1/T2 values without biasing the estimated mean values was observed with the subspace reconstruction and agreed with reported literature values. The subspace reconstruction enabled visualization of small differences in T1/T2 values in the tumor region within the peripheral zone. Longitudinal imaging of a volunteer subject yielded CCC of 0.89 for MRF T1, and 0.81 for MRF T2 in the prostate gland. Longitudinal imaging of the prostate patient confirmed the feasibility of capturing radiation treatment related changes. This work is a proof-of-concept for a high resolution and fast quantitative mapping using golden-angle radial MRF combined with a subspace reconstruction technique for longitudinal treatment response assessment in subjects undergoing radiation treatment.

Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule.

Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule.

Accelerated Diffusion-Weighted MRI of Rectal Cancer Using a Residual Convolutional Network.

This work presents a deep-learning-based denoising technique to accelerate the acquisition of high b-value diffusion-weighted MRI for rectal cancer. A denoising convolutional neural network (DCNN) with a combined L1-L2 loss function was developed to denoise high b-value diffusion-weighted MRI data acquired with fewer repetitions (NEX: number of excitations) using the low b-value image as an anatomical guide. DCNN was trained using 85 datasets acquired on patients with rectal cancer and tested on 20 different datasets with NEX = 1, 2, and 4, corresponding to acceleration factors of 16, 8, and 4, respectively. Image quality was assessed qualitatively by expert body radiologists. Reader 1 scored similar overall image quality between denoised images with NEX = 1 and NEX = 2, which were slightly lower than the reference. Reader 2 scored similar quality between NEX = 1 and the reference, while better quality for NEX = 2. Denoised images with fourfold acceleration (NEX = 4) received even higher scores than the reference, which is due in part to the effect of gas-related motion in the rectum, which affects longer acquisitions. The proposed deep learning denoising technique can enable eightfold acceleration with similar image quality (average image quality = 2.8 ± 0.5) and fourfold acceleration with higher image quality (3.0 ± 0.6) than the clinical standard (2.5 ± 0.8) for improved diagnosis of rectal cancer.

ComBat Harmonization for MRI Radiomics: Impact on Nonbinary Tissue Classification by Machine Learning.

OBJECTIVES: The aims of this study were to determine whether ComBat harmonization improves multiclass radiomics-based tissue classification in technically heterogeneous MRI data sets and to compare the performances of 2 ComBat variants. MATERIALS AND METHODS: One hundred patients who had undergone T1-weighted 3D gradient echo Dixon MRI (2 scanners/vendors; 50 patients each) were retrospectively included. Volumes of interest (2.5 cm 3 ) were placed in 3 disease-free tissues with visually similar appearance on T1 Dixon water images: liver, spleen, and paraspinal muscle. Gray-level histogram (GLH), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and gray-level size-zone matrix (GLSZM) radiomic features were extracted. Tissue classification was performed on pooled data from the 2 centers (1) without harmonization, (2) after ComBat harmonization with empirical Bayes estimation (ComBat-B), and (3) after ComBat harmonization without empirical Bayes estimation (ComBat-NB). Linear discriminant analysis with leave-one-out cross-validation was used to distinguish among the 3 tissue types, using all available radiomic features as input. In addition, a multilayer perceptron neural network with a random 70%:30% split into training and test data sets was used for the same task, but separately for each radiomic feature category. RESULTS: Linear discriminant analysis-based mean tissue classification accuracies were 52.3% for unharmonized, 66.3% for ComBat-B harmonized, and 92.7% for ComBat-NB harmonized data. For multilayer perceptron neural network, mean classification accuracies for unharmonized, ComBat-B-harmonized, and ComBat-NB-harmonized test data were as follows: 46.8%, 55.1%, and 57.5% for GLH; 42.0%, 65.3%, and 71.0% for GLCM; 45.3%, 78.3%, and 78.0% for GLRLM; and 48.1%, 81.1%, and 89.4% for GLSZM. Accuracies were significantly higher for both ComBat-B- and ComBat-NB-harmonized data than for unharmonized data for all feature categories (at P = 0.005, respectively). For GLCM ( P = 0.001) and GLSZM ( P = 0.005), ComBat-NB harmonization provided slightly higher accuracies than ComBat-B harmonization. CONCLUSIONS: ComBat harmonization may be useful for multicenter MRI radiomics studies with nonbinary classification tasks. The degree of improvement by ComBat may vary among radiomic feature categories, among classifiers, and among ComBat variants.

View-sharing for 4D magnetic resonance imaging with randomized projection-encoding enables improvements of respiratory motion imaging for treatment planning in abdominothoracic radiotherapy.

Background and Purpose: The accuracy and precision of radiation therapy are dependent on the characterization of organ-at-risk and target motion. This work aims to demonstrate a 4D magnetic resonance imaging (MRI) method for improving spatial and temporal resolution in respiratory motion imaging for treatment planning in abdominothoracic radiotherapy. Materials and Methods: The spatial and temporal resolution of phase-resolved respiratory imaging is improved by considering a novel sampling function based on quasi-random projection-encoding and peripheral k-space view-sharing. The respiratory signal is determined directly from k-space, obviating the need for an external surrogate marker. The average breathing curve is used to optimize spatial resolution and temporal blurring by limiting the extent of data sharing in the Fourier domain. Improvements in image quality are characterized by evaluating changes in signal-to-noise ratio (SNR), resolution, target detection, and level of artifact. The method is validated in simulations, in a dynamic phantom, and in-vivo imaging. Results: Sharing of high-frequency k-space data, driven by the average breathing curve, improves spatial resolution and reduces artifacts. Although equal sharing of k-space data improves resolution and SNR in stationary features, phases with large temporal changes accumulate significant artifacts due to averaging of high frequency features. In the absence of view-sharing, no averaging and detection artifacts are observed while spatial resolution is degraded. Conclusions: The use of a quasi-random sampling function, with view-sharing driven by the average breathing curve, provides a feasible method for self-navigated 4D-MRI at improved spatial resolution.

4D lung MRI with high-isotropic-resolution using half-spoke (UTE) and full-spoke 3D radial acquisition and temporal compressed sensing reconstruction.

Objective. To develop a respiratory motion-resolved four-dimensional (4D) magnetic resonance imaging (MRI) technique with high-isotropic-resolution (1.1 mm) using 3D radial sampling, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for lung cancer imaging.Approach. Free-breathing half- and full-spoke 3D golden-angle radial acquisitions were performed on eight healthy volunteers and eight patients with lung tumors of varying size. A back-and-forth k-space ordering between consecutive interleaves of the 3D radial acquisition was performed to minimize eddy current-related artifacts. Data were sorted into respiratory motion states using camera-based motion navigation and 4D images were reconstructed using temporal compressed sensing to reduce scan time. Normalized sharpness indices of the diaphragm, apparent signal-to-noise ratio (aSNR) and contrast-to-noise ratio (CNR) of the lung tumor (patients only), liver, and aortic arch were compared between half- and full-spoke 4D MRI images to evaluate the impact of respiratory motion and image contrast on 4D MRI image quality. Respiration-induced changes in lung volumes and center of mass shifts were compared between half- and full-spoke 4D MRI measurements. In addition, the motion measurements from 4D MRI and the same-day 4D CT were presented in one of the lung tumor patients.Main results. Half-spoke 4D MRI provides better visualization of the lung parenchyma, while full-spoke 4D MRI presents sharper diaphragm images and higher aSNR and CNR in the lung tumor, liver, and aortic arch. Lung volume changes and center of mass shifts measured by half- and full-spoke 4D MRI were not statistically different. For the patient with 4D MRI and same-day 4D CT, lung volume changes and center of mass shifts were generally comparable.Significance. This work demonstrates the feasibility of a motion-resolved 4D MRI technique with high-isotropic-resolution using 3D radial acquisition, camera-based respiratory motion sensing, and temporal compressed sensing reconstruction for treatment planning and motion monitoring in radiotherapy of lung cancer.

GRASPNET: Fast spatiotemporal deep learning reconstruction of golden-angle radial data for free-breathing dynamic contrast-enhanced magnetic resonance imaging.

The purpose of the current study was to develop a deep learning technique called Golden-angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic contrast-enhanced 4D MRI acquired with golden-angle radial k-space trajectories. GRASPnet operates in the image-time space and does not use explicit data consistency to minimize the reconstruction time. Three different network architectures were developed: (1) GRASPnet-2D: 2D convolutional kernels (x,y) and coil and contrast dimensions collapsed into a single combined dimension; (2) GRASPnet-3D: 3D kernels (x,y,t); and (3) GRASPnet-2D + time: two 3D kernels to first exploit spatial correlations (x,y,1) followed by temporal correlations (1,1,t). The networks were trained using iterative GRASP reconstruction as the reference. Free-breathing 3D abdominal imaging with contrast injection was performed on 33 patients with liver lesions using a T1-weighted golden-angle stack-of-stars pulse sequence. Ten datasets were used for testing. The three GRASPnet architectures were compared with iterative GRASP results using quantitative and qualitative analysis, including impressions from two body radiologists. The three GRASPnet techniques reduced the reconstruction time to about 13 s with similar results with respect to iterative GRASP. Among the GRASPnet techniques, GRASPnet-2D + time compared favorably in the quantitative analysis. Spatiotemporal deep learning enables reconstruction of dynamic 4D contrast-enhanced images in a few seconds, which would facilitate translation to clinical practice of compressed sensing methods that are currently limited by long reconstruction times.

CEST MR fingerprinting (CEST-MRF) for brain tumor quantification using EPI readout and deep learning reconstruction.

PURPOSE: To develop a clinical CEST MR fingerprinting (CEST-MRF) method for brain tumor quantification using EPI acquisition and deep learning reconstruction. 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 under 2 min. Quantitative MRF reconstruction was performed using a deep reconstruction network (DRONE) to yield the water relaxation and chemical exchange parameters. The feasibility of the six parameter DRONE reconstruction was tested in simulations using 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 calculated for white matter and gray matter. The clinical utility of CEST-MRF was demonstrated on four 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. RESULTS: DRONE reconstruction of the digital phantom yielded a normalized RMS error of ≤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 concordance correlation coefficient for all six parameters was 0.98 ± 0.01 in white matter and 0.98 ± 0.02 in gray matter. The CEST-MRF values in nearly all tumor regions were significantly different (P = 0.05) from each other and the contra-lateral side. CONCLUSION: Combination of EPI readout and deep learning reconstruction enabled fast, accurate and reproducible CEST-MRF in brain tumors.

Dynamic contrast-enhanced MRI parametric mapping using high spatiotemporal resolution Golden-angle RAdial Sparse Parallel MRI and iterative joint estimation of the arterial input function and pharmacokinetic parameters.

The aim of this work is to develop a data-driven quantitative dynamic contrast-enhanced (DCE) MRI technique using Golden-angle RAdial Sparse Parallel (GRASP) MRI with high spatial resolution and high flexible temporal resolution and pharmacokinetic (PK) analysis with an arterial input function (AIF) estimated directly from the data obtained from each patient. DCE-MRI was performed on 13 patients with gynecological malignancy using a 3-T MRI scanner with a single continuous golden-angle stack-of-stars acquisition and image reconstruction with two temporal resolutions, by exploiting a unique feature in GRASP that reconstructs acquired data with user-defined temporal resolution. Joint estimation of the AIF (both AIF shape and delay) and PK parameters was performed with an iterative algorithm that alternates between AIF and PK estimation. Computer simulations were performed to determine the accuracy (expressed as percentage error [PE]) and precision of the estimated parameters. PK parameters (volume transfer constant [Ktrans ], fractional volume of the extravascular extracellular space [ve ], and blood plasma volume fraction [vp ]) and normalized root-mean-square error [nRMSE] (%) of the fitting errors for the tumor contrast kinetic data were measured both with population-averaged and data-driven AIFs. On patient data, the Wilcoxon signed-rank test was performed to compare nRMSE. Simulations demonstrated that GRASP image reconstruction with a temporal resolution of 1 s/frame for AIF estimation and 5 s/frame for PK analysis resulted in an absolute PE of less than 5% in the estimation of Ktrans and ve , and less than 11% in the estimation of vp . The nRMSE (mean ± SD) for the dual temporal resolution image reconstruction and data-driven AIF was 0.16 ± 0.04 compared with 0.27 ± 0.10 (p < 0.001) with 1 s/frame using population-averaged AIF, and 0.23 ± 0.07 with 5 s/frame using population-averaged AIF (p < 0.001). We conclude that DCE-MRI data acquired and reconstructed with the GRASP technique at dual temporal resolution can successfully be applied to jointly estimate the AIF and PK parameters from a single acquisition resulting in data-driven AIFs and voxelwise PK parametric maps.

Combined Compressed Sensing and SENSE to Enhance Radiation Therapy Magnetic Resonance Imaging Simulation.

PURPOSE: To assess the effect of a combination of compressed sensing and SENSitivity Encoding (SENSE) acceleration techniques on radiation therapy magnetic resonance imaging (MRI) simulation workflows. METHODS AND MATERIALS: Thirty-seven acquisitions were performed with both SENSE-only (SENSE) and combined compressed sensing and SENSE (CS) techniques in 24 patients receiving radiation therapy MRI simulation for a wide range of disease sites. The anatomic field of view prescription and image resolution were identical for both SENSE and CS acquisitions to ensure fair comparison. The acquisition time of all images was recorded to assess time savings. For each image pair, image quality, and ability to contour were assessed by 2 radiation oncologists. Aside from direct image pair comparisons, the feasibility of using CS to improve MRI simulation protocols by increasing image resolution, field of view, and reducing motion artifacts was also evaluated. RESULTS: CS resulted in an average reduction of 27% in scan time with negligible changes in image quality and the ability to contour structures for RT treatment planning compared with SENSE. Physician scoring of image quality and ability to contour shows that while SENSE still has slightly better image quality compared with CS, this observed difference in image quality did not affect the ability to contour. In addition, the higher acceleration capability of CS enabled use of superior-inferior direction phase encoding in a sagittal 3-dimensional T2-weighted scan for substantially improved visibility of the prostatic urethra, which eliminated the need for a Foley catheter in most patients. CONCLUSIONS: The combination of compressed sensing and parallel imaging resulted in marked improvements in the MRI Simulation workflow. The scan time was reduced without significantly affecting image quality in the context of ability to contour. The acceleration capabilities allowed for increased image resolution under similar scanning times as well as significantly improved urethra visualization in prostate simulations.

MR SIGnature MAtching (MRSIGMA) with retrospective self-evaluation for real-time volumetric motion imaging.

Objective. MR SIGnature MAtching (MRSIGMA) is a real-time volumetric MRI technique to image tumor and organs at risk motion in real-time for radiotherapy applications, where a dictionary of high-resolution 3D motion states and associated motion signatures are computed first during offline training and real-time 3D imaging is performed afterwards using fast signature-only acquisition and signature matching. However, the lack of a reference image with similar spatial resolution and temporal resolution introduces significant challenges forin vivovalidation.Approach. This work proposes a retrospective self-validation for MRSIGMA, where the same data used for real-time imaging are used to create a non-real-time reference for comparison. MRSIGMA with self-validation is tested in patients with liver tumors using quantitative metrics defined on the tumor and nearby organs-at-risk structures. The dice coefficient between contours defined on the real-time MRSIGMA and non-real-time reference was used to assess motion imaging performance.Main Results. Total latency (including signature acquisition and signature matching) was between 250 and 314 ms, which is sufficient for organs affected by respiratory motion. Mean ± standard deviation dice coefficient over time was 0.74 ± 0.03 for patients imaged without contrast agent and 0.87 ± 0.03 for patients imaged with contrast agent, which demonstrated high-performance real-time motion imaging.Signficance. MRSIGMA with self-evaluation provides a means to perform real-time volumetric MRI for organ motion tracking with quantitative performance measures.

Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors.

BACKGROUND AND PURPOSE: Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients. METHODS: We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm2 s-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm2 s-1 ), volume (mm3 ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI. RESULTS: The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm2 s-1 ) and volume (mm3 ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts. CONCLUSIONS: This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.