Free-breathing mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI.

Purpose: To enable motion-robust, ungated, free-breathing mapping of hepatic iron overload in children with 3D multi-echo UTE cones MRI.

Methods: A golden-ratio re-ordered 3D multi-echo UTE cones acquisition was developed with chemical-shift encoding (CSE). Multi-echo complex-valued source images were reconstructed via gridding and coil combination, followed by confounder-corrected (=1/ ) mapping.

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.

The image quality limitations of echo-planar diffusion-weighted imaging (DWI) are an obstacle to its widespread adoption in the breast. Steady-state DWI is an alternative DWI method with more robust image quality but its contrast for imaging breast cancer is not well-understood. The aim of this study was to develop and evaluate diffusion-weighted double-echo steady-state imaging with a three-dimensional cones trajectory (DW-DESS-Cones) as an alternative to conventional DWI for non-contrast-enhanced MRI in the breast.

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.

Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework.

DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES.

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered.

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

Purpose: To propose a novel combined parallel imaging and deep learning-based reconstruction framework for robust reconstruction of highly accelerated 2D cardiac cine MRI data.

Methods: We propose DL-ESPIRiT, an unrolled neural network architecture that utilizes an extended coil sensitivity model to address SENSE-related field-of-view (FOV) limitations in previously proposed deep learning-based reconstruction frameworks. Additionally, we propose a novel neural network design based on (2+1)D spatiotemporal convolutions to produce more accurate dynamic MRI reconstructions than conventional 3D convolutions.

Direct measurement of atrioventricular valve regurgitant jets using 4D flow cardiovascular magnetic resonance is accurate and reliable for children with congenital heart disease: a retrospective cohort study.

Background: 3D-time resolved flow (4DF) cardiovascular magnetic resonance (CMR) with retrospective analysis of atrioventricular valve regurgitation (AVVR) allows for internal validation by multiple direct and indirect methods. Limited data exist on direct measurement of AVVR by 4DF CMR in pediatric congenital heart disease (CHD). We aimed to validate direct measurement of the AVVR jet as accurate and reliable compared to the volumetric method (clinical standard by 2D CMR) and as a superior method of internal validation than the annular inflow method.

Extreme MRI: Large-scale volumetric dynamic imaging from continuous non-gated acquisitions.

Purpose: To develop a framework to reconstruct large-scale volumetric dynamic MRI from rapid continuous and non-gated acquisitions, with applications to pulmonary and dynamic contrast-enhanced (DCE) imaging.

Theory And Methods: The problem considered here requires recovering 100 gigabytes of dynamic volumetric image data from a few gigabytes of k-space data, acquired continuously over several minutes. This reconstruction is vastly under-determined, heavily stressing computing resources as well as memory management and storage.

Reconstruction of undersampled 3D non-Cartesian image-based navigators for coronary MRA using an unrolled deep learning model.

Purpose: To rapidly reconstruct undersampled 3D non-Cartesian image-based navigators (iNAVs) using an unrolled deep learning (DL) model, enabling nonrigid motion correction in coronary magnetic resonance angiography (CMRA).

Methods: An end-to-end unrolled network is trained to reconstruct beat-to-beat 3D iNAVs acquired during a CMRA sequence. The unrolled model incorporates a nonuniform FFT operator in TensorFlow to perform the data-consistency operation, and the regularization term is learned by a convolutional neural network (CNN) based on the proximal gradient descent algorithm.

4D flow vs. 2D cardiac MRI for the evaluation of pulmonary regurgitation and ventricular volume in repaired tetralogy of Fallot: a retrospective case control study.

Lengthy exams and breath-holding limit the use of pediatric cardiac MRI (CMR). 3D time-resolved flow MRI (4DF) is a free-breathing, single-sequence exam that obtains magnitude (anatomic) and phase contrast (PC) data. We compare the accuracy of gadobenate dimeglumine-enhanced 4DF on a 1.

Data-driven self-calibration and reconstruction for non-cartesian wave-encoded single-shot fast spin echo using deep learning.

Background: Current self-calibration and reconstruction methods for wave-encoded single-shot fast spin echo imaging (SSFSE) requires long computational time, especially when high accuracy is needed.

Purpose: To develop and investigate the clinical feasibility of data-driven self-calibration and reconstruction of wave-encoded SSFSE imaging for computation time reduction and quality improvement.

Study Type: Prospective controlled clinical trial.

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

Purpose: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

Methods: A residual convolutional neural network to correct off-resonance artifacts (Off-ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.

Conical ultrashort echo time (UTE) MRI in the evaluation of pediatric acute appendicitis.

Purpose: Magnetic resonance imaging (MRI) sequences with conical k-space trajectories are able to decrease motion artifacts while achieving ultrashort echo times (UTE). We assessed the performance of free-breathing conical UTE MRI in the evaluation of the pediatric pelvis for suspected appendicitis.

Methods: Our retrospective review of 84 pediatric patients who underwent MRI for suspected appendicitis compared three contrast-enhanced sequences: free-breathing conical UTE, breath-hold three-dimensional (3D) spoiled gradient echo (BH-SPGR), and free-breathing high-resolution 3D SPGR (FB-SPGR).

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.

4D flow MRI quantification of mitral and tricuspid regurgitation: Reproducibility and consistency relative to conventional MRI.

Background: In patients with mitral or tricuspid valve regurgitation, evaluation of regurgitant severity is essential for determining the need for surgery. While transthoracic echocardiography is widely accessible, it has limited reproducibility for grading inlet valve regurgitation. Multiplanar cardiac MRI is the quantitative standard but requires specialized local expertise, and is thus not widely available.

Mitigation of near-band balanced steady-state free precession through-plane flow artifacts using partial dephasing.

Purpose: To mitigate artifacts from through-plane flow at the locations of steady-state stopbands in balanced steady-state free precession (SSFP) using partial dephasing.

Methods: A 60° range in the phase accrual during a TR was created over the voxel by slightly unbalancing the slice-select dephaser. The spectral profiles of SSFP with partial dephasing for various constant flow rates and during pulsatile flow were simulated to determine if partial dephasing decreases through-plane flow artifacts originating near SSFP dark bands while maintaining on-resonant signal.

Self-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging.

Background: It is highly desirable in clinical abdominal MR scans to accelerate single-shot fast spin echo (SSFSE) imaging and reduce blurring due to T decay and partial-Fourier acquisition.

Purpose: To develop and investigate the clinical feasibility of wave-encoded variable-density SSFSE imaging for improved image quality and scan time reduction.

Study Type: Prospective controlled clinical trial.

Comprehensive Multi-Dimensional MRI for the Simultaneous Assessment of Cardiopulmonary Anatomy and Physiology.

Diagnostic testing often assesses the cardiovascular or respiratory systems in isolation, ignoring the major pathophysiologic interactions between the systems in many diseases. When both systems are assessed currently, multiple modalities are utilized in costly fashion with burdensome logistics and decreased accessibility. Thus, we have developed a new acquisition and reconstruction paradigm using the flexibility of MRI to enable a comprehensive exam from a single 5-15 min scan.

Free-breathing pediatric chest MRI: Performance of self-navigated golden-angle ordered conical ultrashort echo time acquisition.

Purpose: To assess the feasibility and performance of conical k-space trajectory free-breathing ultrashort echo time (UTE) chest magnetic resonance imaging (MRI) versus four-dimensional (4D) flow and effects of 50% data subsampling and soft-gated motion correction.

Materials And Methods: Thirty-two consecutive children who underwent both 4D flow and UTE ferumoxytol-enhanced chest MR (mean age: 5.4 years, range: 6 days to 15.

Autocalibrating motion-corrected wave-encoding for highly accelerated free-breathing abdominal MRI.

Purpose: To develop a motion-robust wave-encoding technique for highly accelerated free-breathing abdominal MRI.

Methods: A comprehensive 3D wave-encoding-based method was developed to enable fast free-breathing abdominal imaging: (a) auto-calibration for wave-encoding was designed to avoid extra scan for coil sensitivity measurement; (b) intrinsic butterfly navigators were used to track respiratory motion; (c) variable-density sampling was included to enable compressed sensing; (d) golden-angle radial-Cartesian hybrid view-ordering was incorporated to improve motion robustness; and (e) localized rigid motion correction was combined with parallel imaging compressed sensing reconstruction to reconstruct the highly accelerated wave-encoded datasets. The proposed method was tested on six subjects and image quality was compared with standard accelerated Cartesian acquisition both with and without respiratory triggering.

Feasibility of ferumoxytol-enhanced neonatal and young infant cardiac MRI without general anesthesia.

Purpose: To assess the feasibility of ferumoxytol-enhanced anesthesia-free cardiac MRI in neonates and young infants for complex congenital heart disease (CHD).

Materials And Methods: With Institutional Review Board approval, 21 consecutive neonates and young infants (1 day to 11 weeks old; median age of 3 days) who underwent a rapid two-sequence (MR angiography [MRA] and four-dimensional [4D] flow) MRI protocol with intravenous ferumoxytol without sedation (n = 17) or light sedation (n = 4) at 3 Tesla (T) (except one case at 1.5T) between June 2014 and February 2016 were retrospectively identified.