Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study.

Objectives: To qualitatively and quantitatively compare a single breath-hold fast half-Fourier single-shot turbo spin echo sequence with deep learning reconstruction (DL HASTE) with T2-weighted BLADE sequence for liver MRI at 3 T.

Methods: From December 2020 to January 2021, patients with liver MRI were prospectively included. For qualitative analysis, sequence quality, presence of artifacts, conspicuity, and presumed nature of the smallest lesion were assessed using the chi-squared and McNemar tests.

Accelerated T2-weighted MRI of the liver at 3 T using a single-shot technique with deep learning-based image reconstruction: impact on the image quality and lesion detection.

Purpose: Fat-suppressed T2-weighted imaging (T2-FS) requires a long scan time and can be wrought with motion artifacts, urging the development of a shorter and more motion robust sequence. We compare the image quality of a single-shot T2-weighted MRI prototype with deep-learning-based image reconstruction (DL HASTE-FS) with a standard T2-FS sequence for 3 T liver MRI.

Methods: 41 consecutive patients with 3 T abdominal MRI examinations including standard T2-FS and DL HASTE-FS, between 5/6/2020 and 11/23/2020, comprised the study cohort.

Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity.

Rationale And Objectives: To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBE) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBE).

Materials And Methods: Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included.

Clinical feasibility of accelerated diffusion weighted imaging of the abdomen with deep learning reconstruction: Comparison with conventional diffusion weighted imaging.

Purpose: To assess the clinical feasibility of accelerated deep learning-reconstructed diffusion weighted imaging (DWI) and to compare its image quality and acquisition time with those of conventional DWI.

Methods: Seventy-four consecutive patients who underwent 3 T abdominal magnetic resonance imaging (MRI) were retrospectively enrolled. DWI were acquired using both conventional DWI and DWI with deep-learning reconstruction (DL DWI).

Comprehensive Clinical Evaluation of a Deep Learning-Accelerated, Single-Breath-Hold Abdominal HASTE at 1.5 T and 3 T.

To evaluate the clinical performance of a deep learning-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE)-sequence for T2-weighted fat-suppressed MRI of the abdomen at 1.5 T and 3 T in comparison to standard T2-weighted fat-suppressed multi-shot turbo spin echo-sequence. A total of 320 patients who underwent a clinically indicated liver MRI at 1.

Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T: Reduction of Breath-Hold Time and Improvement of Image Quality.

Objectives: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradient echo imaging of the abdomen.

Materials And Methods: Fifty consecutive patients who underwent a 1.

A parallel spatial and Bloch manifold regularized iterative reconstruction method for MR Fingerprinting.

Magnetic Resonance Fingerprinting (MRF) reconstructs tissue maps based on a sequence of very highly undersampled images. In order to be able to perform MRF reconstruction, state-of-the-art MRF methods rely on priors such as the MR physics (Bloch equations) and might also use some additional low-rank or spatial regularization. However to our knowledge these three regularizations are not applied together in a joint reconstruction.

Development and Evaluation of Deep Learning-Accelerated Single-Breath-Hold Abdominal HASTE at 3 T Using Variable Refocusing Flip Angles.

Objective: Deep learning (DL) reconstruction enables substantial acceleration of image acquisition while maintaining diagnostic image quality. The aims of this study were to overcome the drawback of specific absorption rate (SAR)-related limitations at 3 T and to develop a DL-accelerated single-breath-hold half-Fourier acquisition single-shot turbo spin echo (HASTE) sequence for 2-dimesional T2-weighted fat-suppressed magnetic resonance imaging of the abdomen at 3 T using a variable flip angle (FA) evolution for the refocusing radiofrequency pulses, as well as to evaluate its feasibility and image quality in comparison to state-of-the-art T2-weighted fat-suppressed imaging technique (BLADE).

Materials And Methods: First, a suitable FA evolution with low cardiac motion-related signal loss (CRSL) and low SAR was determined through a prospective volunteer study with 11 participants.

Accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction: qualitative and quantitative comparison of image quality with conventional T2-weighted FS sequence.

Objective: To compare the image quality of an accelerated single-shot T2-weighted fat-suppressed (FS) MRI of the liver with deep learning-based image reconstruction (DL HASTE-FS) with conventional T2-weighted FS sequence (conventional T2 FS) at 1.5 T.

Methods: One hundred consecutive patients who underwent clinical MRI of the liver at 1.

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction.

Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes.

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold.

Objective: The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE).

Materials And Methods: Sixty-six patients who underwent 1.5-T liver magnetic resonance imaging were included in this monocentric, retrospective study.

Compressive source separation: theory and methods for hyperspectral imaging.

We propose and analyze a new model for hyperspectral images (HSIs) based on the assumption that the whole signal is composed of a linear combination of few sources, each of which has a specific spectral signature, and that the spatial abundance maps of these sources are themselves piecewise smooth and therefore efficiently encoded via typical sparse models. We derive new sampling schemes exploiting this assumption and give theoretical lower bounds on the number of measurements required to reconstruct HSI data and recover their source model parameters. This allows us to segment HSIs into their source abundance maps directly from compressed measurements.