DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns.

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.

Fat suppression using frequency-sweep RF saturation and iterative reconstruction.

Purpose: To introduce an alternative idea for fat suppression that is suited both for low-field applications where conventional fat-suppression approaches become ineffective due to narrow spectral separation and for applications with strong B0 homogeneities.

Methods: Separation of fat and water is achieved by sweeping the frequency of RF saturation pulses during continuous radial acquisition and calculating frequency-resolved images using regularized iterative reconstruction. Voxel-wise signal-response curves are extracted that reflect tissue's response to RF saturation at different frequencies and allow the classification into fat or water.

Accelerated Diffusion-Weighted Magnetic Resonance Imaging of the Liver at 1.5 T With Deep Learning-Based Image Reconstruction: Impact on Image Quality and Lesion Detection.

Objective: To perform image quality comparison between deep learning-based multiband diffusion-weighted sequence (DL-mb-DWI), accelerated multiband diffusion-weighted sequence (accelerated mb-DWI), and conventional multiband diffusion-weighted sequence (conventional mb-DWI) in patients undergoing clinical liver magnetic resonance imaging (MRI).

Methods: Fifty consecutive patients who underwent clinical MRI of the liver at a 1.5-T scanner, between September 1, 2021, and January 31, 2022, were included in this study.

Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney.

Objective: Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge.

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.

The Pancreatic Cancer Early Detection (PRECEDE) Study is a Global Effort to Drive Early Detection: Baseline Imaging Findings in High-Risk Individuals.

Background: Pancreatic adenocarcinoma (PC) is a highly lethal malignancy with a survival rate of only 12%. Surveillance is recommended for high-risk individuals (HRIs), but it is not widely adopted. To address this unmet clinical need and drive early diagnosis research, we established the Pancreatic Cancer Early Detection (PRECEDE) Consortium.

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.

Low Field MRI Surveillance 6-24 Months Post-acute COVID-19 Pneumonia: Factors Influencing Severity and Evolution of Lung Opacities.

Rationale And Objectives: To determine factors influencing low-field MRI lung opacity severity 6-24 months after acute Covid-19 pneumonia.

Materials And Methods: 104 post-acute Covid-19 patients with 167 MRI exams were included. 32 patients had more than one exam, and 63 exams were serial exams.

Patient-centered radiology: a roadmap for outpatient imaging.

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam.

Pancreatic Cystic Lesions: Next Generation of Radiologic Assessment.

Pancreatic cystic lesions are frequently identified on cross-sectional imaging. As many of these are presumed branch-duct intraductal papillary mucinous neoplasms, these lesions generate much anxiety for the patients and clinicians, often necessitating long-term follow-up imaging and even unnecessary surgical resections. However, the incidence of pancreatic cancer is overall low for patients with incidental pancreatic cystic lesions.

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.

Characterization of motion dependent magnetic field inhomogeneity for DWI in the kidneys.

Purpose: Diffusion-weighted imaging (DWI) of the abdomen has increased dramatically for both research and clinical purposes. Motion and static field inhomogeneity related challenges limit image quality of abdominopelvic imaging with the most conventional echo-planar imaging (EPI) pulse sequence. While reversed phase encoded imaging is increasingly used to facilitate distortion correction, it typically assumes one motion independent magnetic field distribution.

MP-RAVE: IR-Prepared T -Weighted Radial Stack-of-Stars 3D GRE imaging with retrospective motion correction.

Purpose: To describe an inversion-recovery T -weighted radial stack-of-stars 3D gradient echo (GRE) sequence with comparable image quality to conventional MP-RAGE and to demonstrate how the radial acquisition scheme can be utilized for additional retrospective motion correction to improve robustness to head motion.

Methods: The proposed sequence, named MP-RAVE, has been derived from a previously described radial stack-of-stars 3D GRE sequence (RAVE) and includes a 180° inversion recovery pulse that is generated once for every stack of radial views. The sequence is combined with retrospective 3D motion correction to improve robustness.

Impact of 3D printed models on quantitative surgical outcomes for patients undergoing robotic-assisted radical prostatectomy: a cohort study.

Background: Three-dimensional (3D) printed anatomic models can facilitate presurgical planning by providing surgeons with detailed knowledge of the exact location of pertinent anatomical structures. Although 3D printed anatomic models have been shown to be useful for pre-operative planning, few studies have demonstrated how these models can influence quantitative surgical metrics.

Objective: To prospectively assess whether patient-specific 3D printed prostate cancer models can improve quantitative surgical metrics in patients undergoing robotic-assisted radical prostatectomy (RARP).

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.

Kz-accelerated variable-density stack-of-stars MRI.

This work aimed to develop a modified stack-of-stars golden-angle radial sampling scheme with variable-density acceleration along the slice (kz) dimension (referred to as VD-stack-of-stars) and to test this new sampling trajectory with multi-coil compressed sensing reconstruction for rapid motion-robust 3D liver MRI. VD-stack-of-stars sampling implements additional variable-density undersampling along the kz dimension, so that slice resolution (or volumetric coverage) can be increased without prolonging scan time. The new sampling trajectory (with increased slice resolution) was compared with standard stack-of-stars sampling with fully sampled kz (with standard slice resolution) in both non-contrast-enhanced free-breathing liver MRI and dynamic contrast-enhanced MRI (DCE-MRI) of the liver in volunteers.

Cardiac Phase and Flow Compensation Effects on REnal Flow and Microstructure AnisotroPy MRI in Healthy Human Kidney.

Background: Renal diffusion-weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity.

Purpose: To measure modulation of DWI with cardiac phase and flow-compensated (FC) diffusion gradient waveforms.

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.