Optimized MR pulse sequence for high-resolution brain 3D-T1ρ mapping with weighted spin-lock acquisitions.

Purpose: To implement and evaluate the feasibility of brain spin-lattice relaxation in the rotating frame (T1ρ) mapping using a novel optimized pulse sequence that incorporates weighted spin-lock acquisitions, enabling high-resolution three-dimensional (3D) mapping.

Methods: The optimized variable flip-angle framework, previously proposed for knee T1ρ mapping, was enhanced by integrating weighted spin-lock acquisitions. This strategic combination significantly boosts signal-to-noise ratio (SNR) while reducing data acquisition time, facilitating high-resolution 3D-T1ρ mapping of the brain.

MRI Dixon Fat-Corrected Look-Locker T1 Mapping for Quantification of Liver Fibrosis and Inflammation-A Comparison With the Non-Fat-Corrected Shortened Modified Look-Locker Inversion Recovery Technique.

Objectives: This study evaluates the impact of liver steatosis on the discriminative ability for liver fibrosis and inflammation using a novel Dixon water-only fat-corrected Look-Locker T1 mapping sequence, compared with a standard shortened Modified Look-Locker Inversion Recovery (shMOLLI) sequence, with the aim of overcoming the limitation of steatosis-related confounding in liver T1 mapping.

Materials And Methods: 3 T magnetic resonance imaging of the liver including the 2 T1 mapping sequences and proton density fat fraction (PDFF) was prospectively performed in 24 healthy volunteers and 38 patients with histologically proven liver fibrosis evaluated within 90 days of liver biopsy. Paired Mann-Whitney test compared sequences between participants with and without significant liver steatosis (PDFF cutoff 10%), and unpaired Kruskal-Wallis test compared healthy volunteers to patients with early (F0-2) and advanced (F3-4) liver fibrosis, as well as low (A0-1) and marked (A2-3) inflammatory activity.

Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential T and T mapping of knee cartilage.

Purpose: To compare the performance of a learned magnetization-prepared gradient echo (L-MPGRE) sequence against a commonly used sequence for 3D T and T mapping of the knee joint, the magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS), on bi-exponential (BE), stretched-exponential (SE), and mono-exponential (ME) relaxation models.

Methods: We used a combined differentiable and non-differentiable optimization to learn pulse sequence structure and its parameters for 3D T and T mapping of the knee joint using ME, SE, and BE models. The learned pulse sequence framework was used to improve quantitative accuracy and SNR and to reduce filtering effects.

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

Rationale And Objective: A single-shot T2-weighted deep-learning-based image reconstruction (DL-HASTE) has been recently developed allowing for shorter acquisition time than conventional half-Fourier acquisition single-shot turbo-spin echo (HASTE). The purpose of this study was to compare image quality of conventional 6 mm HASTE with DL-HASTE at 4 mm and 6 mm slice thickness.

Materials And Methods: 91 patients (51 female; mean±SD age: 44±10years) who underwent 3T MR enterography from 5/15/2023-7/15/2023 including pelvic conventional HASTE and DL-HASTE were included.

Feasibility of 3D MRI fingerprinting for rapid knee cartilage T, T and T mapping at 0.55T: Comparison with 3T.

Low-field strength scanners present an opportunity for more inclusive imaging exams and bring several challenges including lower signal-to-noise ratio (SNR) and longer scan times. Magnetic resonance fingerprinting (MRF) is a rapid quantitative multiparametric method that can enable multiple quantitative maps simultaneously. To demonstrate the feasibility of an MRF sequence for knee cartilage evaluation in a 0.

Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging.

Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data.

FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging.

Magnetic resonance imaging (MRI) has experienced remarkable advancements in the integration of artificial intelligence (AI) for image acquisition and reconstruction. The availability of raw k-space data is crucial for training AI models in such tasks, but public MRI datasets are mostly restricted to DICOM images only. To address this limitation, the fastMRI initiative released brain and knee k-space datasets, which have since seen vigorous use.

Quantitative Characterization of Respiratory Patterns on Dynamic Higher Temporal Resolution MRI to Stratify Postacute Covid-19 Patients by Cardiopulmonary Symptom Burden.

Background: Postacute Covid-19 patients commonly present with respiratory symptoms; however, a noninvasive imaging method for quantitative characterization of respiratory patterns is lacking.

Purpose: To evaluate if quantitative characterization of respiratory pattern on free-breathing higher temporal resolution MRI stratifies patients by cardiopulmonary symptom burden.

Study Type: Prospective analysis of retrospectively acquired data.

Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI.

Random matrix theory (RMT) combined with principal component analysis has resulted in a widely used MPPCA noise mapping and denoising algorithm, that utilizes the redundancy in multiple acquisitions and in local image patches. RMT-based denoising relies on the uncorrelated identically distributed noise. This assumption breaks down after regridding of non-Cartesian sampling.

Impact of fat on the apparent T1 value of the liver: assessment by water-only derived T1 mapping.

Objectives: To determine the impact of fat on the apparent T1 value of the liver using water-only derived T1 mapping.

Methods: 3-T MRI included 2D Look-Locker T1 mapping and proton density fat fraction (PDFF) mapping. T1 values of the liver were compared among T1 maps obtained by in-phase (IP), opposed-phase (OP), and Dixon water sequences using paired t-test.

Optimizing variable flip angles in magnetization-prepared gradient-echo sequences for efficient 3D-T1ρ mapping.

Purpose: To optimize the choice of the flip angles of magnetization-prepared gradient-echo sequences for improved accuracy, precision, and speed of 3D-T mapping.

Methods: We propose a new optimization approach for finding variable flip-angle values that improve magnetization-prepared gradient-echo sequences used for 3D-T mapping. This new approach can improve the accuracy and SNR, while reducing filtering effects.

Feasibility of Accelerated Prostate Diffusion-Weighted Imaging on 0.55 T MRI Enabled With Random Matrix Theory Denoising.

Introduction: Prostate cancer diffusion weighted imaging (DWI) MRI is typically performed at high-field strength (3.0 T) in order to overcome low signal-to-noise ratio (SNR). In this study, we demonstrate the feasibility of prostate DWI at low field enabled by random matrix theory (RMT)-based denoising, relying on the MP-PCA algorithm applied during image reconstruction from multiple coils.

FastMRI Prostate: A Publicly Available, Biparametric MRI Dataset to Advance Machine Learning for Prostate Cancer Imaging.

The fastMRI brain and knee dataset has enabled significant advances in exploring reconstruction methods for improving speed and image quality for Magnetic Resonance Imaging (MRI) via novel, clinically relevant reconstruction approaches. In this study, we describe the April 2023 expansion of the fastMRI dataset to include biparametric prostate MRI data acquired on a clinical population. The dataset consists of raw k-space and reconstructed images for T2-weighted and diffusion-weighted sequences along with slice-level labels that indicate the presence and grade of prostate cancer.

Comparison of a Deep Learning-Accelerated vs. Conventional T2-Weighted Sequence in Biparametric MRI of the Prostate.

Background: Demand for prostate MRI is increasing, but scan times remain long even in abbreviated biparametric MRIs (bpMRI). Deep learning can be leveraged to accelerate T2-weighted imaging (T2WI).

Purpose: To compare conventional bpMRIs (CL-bpMRI) with bpMRIs including a deep learning-accelerated T2WI (DL-bpMRI) in diagnosing prostate cancer.

Modern Low-Field MRI of the Musculoskeletal System: Practice Considerations, Opportunities, and Challenges.

Magnetic resonance imaging (MRI) provides essential information for diagnosing and treating musculoskeletal disorders. Although most musculoskeletal MRI examinations are performed at 1.5 and 3.

New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First In Vitro Experience at 0.55 T and Comparison With 1.5 T.

Objectives: Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI.

Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate.

Background: Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality.

Purpose: To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction.

Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI.

Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming.

Diagnostic abdominal MR imaging on a prototype low-field 0.55 T scanner operating at two different gradient strengths.

Purpose: To develop a protocol for abdominal imaging on a prototype 0.55 T scanner and to benchmark the image quality against conventional 1.5 T exam.

Abdominal T2-Weighted Imaging and T2 Mapping Using a Variable Flip Angle Radial Turbo Spin-Echo Technique.

Background: T2 mapping is of great interest in abdominal imaging but current methods are limited by low resolution, slice coverage, motion sensitivity, or lengthy acquisitions.

Purpose: Develop a radial turbo spin-echo technique with refocusing variable flip angles (RADTSE-VFA) for high spatiotemporal T2 mapping and efficient slice coverage within a breath-hold and compare to the constant flip angle counterpart (RADTSE-CFA).

Study Type: Prospective technical efficacy.

Rapid high-resolution volumetric T mapping using a highly accelerated stack-of-stars Look Locker technique.

Purpose: To develop a fast volumetric T mapping technique.

Materials And Methods: A stack-of-stars (SOS) Look Locker technique based on the acquisition of undersampled radial data (>30× relative to Nyquist) and an efficient multi-slab excitation scheme is presented. A principal-component based reconstruction is used to reconstruct T maps.