MR Image Reconstruction

BACKGROUND: Deep-learning-based reconstruction (DLR) improves the quality of magnetic resonance (MR) images which allows faster acquisitions. The aim of this study was to compare the image quality of standard and accelerated T2 weighted turbo-spin-echo (TSE) images of the prostate reconstructed with and without DLR and to find associations between perceived image quality and calculated image characteristics. METHODS: In a cohort of 47 prospectively enrolled consecutive patients referred for bi-parametric prostate magnetic resonance imaging (MRI), two T2-TSE acquisitions in the transverse plane were acquired on a 3T scanner-a standard T2-TSE sequence and a short sequence accelerated by a factor of two using compressed sensing (CS)...

Ventricular aneurysm represents a rare complication of transmural acute myocardial infarction, although other cardiac, congenital, or metabolic diseases may also predispose to such condition. Ventricular expansion includes all the cardiac layers, usually with a large segment involved. Adverse events include recurrent angina, reduced ventricular stroke volume with congestive heart failure, mitral regurgitation, thromboembolism, and ventricular arrhythmias. Multimodality imaging is paramount to provide comprehensive assessment, allowing for appropriate therapeutic decision-making...

Cryo-electron tomography (cryo-ET) allows to visualize the cellular context at macromolecular level. To date, the impossibility of obtaining a reliable ground truth is limiting the application of deep learning-based image processing algorithms in this field. As a consequence, there is a growing demand of realistic synthetic datasets for training deep learning algorithms. In addition, besides assisting the acquisition and interpretation of experimental data, synthetic tomograms are used as reference models for cellular organization analysis from cellular tomograms...

Achieving high-resolution and high signal-to-noise ratio (SNR) in vivo metabolic imaging via fast magnetic resonance spectroscopic imaging (MRSI) has been a longstanding challenge. This study combines the methods of relaxation enhancement (RE) and subspace imaging for the first time, enabling high-resolution and high-SNR in vivo MRSI of rodent brains at 9.4 T. Specifically, an RE-based chemical shift imaging sequence, which combines a frequency-selective pulse to excite only the metabolite frequencies with minimum perturbation of the water spins and a pair of adiabatic pulses to spatially localize the slice of interest, is designed and evaluated in vivo...

Prostate cancer screening often relies on cost-intensive MRIs and invasive needle biopsies. Transrectal ultrasound imaging, as a more affordable and non-invasive alternative, faces the challenge of high inter-class similarity and intra-class variability between benign and malignant prostate cancers. This complexity requires more stringent differentiation of subtle features for accurate auxiliary diagnosis. In response, we introduce the novel Deep Augmented Metric Learning (DAML) network, specifically tailored for ultrasound-based prostate cancer classification...

OBJECTIVE: We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0  ∼  50mT) MRI. METHODS: Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0 -field map as well as the image...

BACKGROUND: The accuracy of morphological radiomic features (MRFs) can be affected by various acquisition settings and imaging conditions. To ensure that clinically irrelevant changes do not reduce sensitivity to capture the radiomics changes between successive acquisitions, it is essential to determine the optimal imaging systems and protocols to use. PURPOSE: The main goal of our study was to optimize CT protocols and minimize the minimum detectable difference (MDD) in successive acquisitions of MRFs...

BACKGROUND: We have recently described circumferential nerve involvement of neuromuscular choristoma associated with desmoid-type fibromatosis (NMC-DTF) in cases involving the sciatic nerve, supporting a nerve-derived mechanism for the DTF. We wondered whether a similar growth pattern occurs in cases involving the brachial plexus (BP). METHODS: We reviewed all available magnetic resonance (MR) imaging in patients diagnosed at our institution with NMC or NMC-DTF of the BP...

Foot and ankle joint models are widely used in the biomechanics community for musculoskeletal and finite element analysis. However, personalizing a foot and ankle joint model is highly time-consuming in terms of medical image collection and data processing. This study aims to develop and evaluate a framework for constructing a comprehensive 3D foot model that integrates statistical shape modeling (SSM) with free-form deformation (FFD) of internal bones. The SSM component is derived from external foot surface scans (skin measurements) of 50 participants, utilizing principal component analysis (PCA) to capture the variance in foot shapes...

Complete spinal cord injury causes an irreversible disruption in the central nervous system, leading to motor, sensory, and autonomic function loss, and a secondary injury that constitutes a physical barrier preventing tissue repair. Tissue engineering scaffolds are presented as a permissive platform for cell migration and the reconnection of spared tissue. Iodine-doped plasma pyrrole polymer (pPPy-I), a neuroprotective material, was applied to polylactic acid (PLA) fibers and implanted in a rat complete spinal cord transection injury model to evaluate whether the resulting composite implants provided structural and functional recovery, using magnetic resonance (MR) imaging, diffusion tensor imaging and tractography, magnetic resonance spectroscopy, locomotion analysis, histology, and immunofluorescence...

INTRODUCTION: Brain viscoelasticity as assessed by magnetic resonance elastography (MRE) has been discussed as a promising surrogate of microstructural alterations due to neurodegenerative processes. Existing studies indicate that multiple sclerosis (MS) is associated with a global reduction in brain stiffness. However, no study to date systematically investigated the MS-related characteristics of brain viscoelasticity separately in normal-appearing white matter (NAWM), deep gray matter (DGM) and T2-hyperintense white matter (WM) lesions...

PURPOSE: Advanced MR fiber tracking imaging reflects fiber bundle invasion by glioblastoma, particularly of the corticospinal tract (CST), which is more susceptible as the largest downstream fiber tracts. We aimed to investigate whether CST features can predict the overall survival of glioblastoma. METHODS: In this prospective secondary analysis, 40 participants (mean age, 58 years; 16 male) pathologically diagnosed with glioblastoma were enrolled. Diffusion spectrum MRI was used for CST reconstruction...

Medical imaging is limited by acquisition time and scanning equipment. CT and MR volumes, reconstructed with thicker slices, are anisotropic with high in-plane resolution and low through-plane resolution. We reveal an intriguing phenomenon that due to the mentioned nature of data, performing slice-wise interpolation from the axial view can yield greater benefits than performing super-resolution from other views. Based on this observation, we propose an Inter-Intra-slice Interpolation Network (I3 Net), which fully explores information from high in-plane resolution and compensates for low through-plane resolution...

To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2...

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023...

RATIONALE AND OBJECTIVES: Investigate the feasibility of using deep learning-based accelerated 3D T1-weighted volumetric isotropic turbo spin-echo acquisition (VISTA) for vessel wall magnetic resonance imaging (VW-MRI), compared to traditional Compressed SENSE and optimize acceleration factor (AF) to obtain high-quality clinical images. METHODS: 40 patients with atherosclerotic plaques in the intracranial or carotid artery were prospectively enrolled in our study from October 1, 2022 to October 31, 2023 underwent high-resolution vessel wall imaging on a 3...

Magnetic resonance imaging produces detailed anatomical and physiological images of the human body that can be used in the clinical diagnosis and treatment of diseases. However, MRI suffers its comparatively longer acquisition time than other imaging methods and is thus vulnerable to motion artifacts, which ultimately lead to likely failed or even wrong diagnosis. In order to perform faster reconstruction, deep learning-based methods along with traditional strategies such as parallel imaging and compressed sensing come into play in recent years in this field...

Deep-learning networks for super-resolution (SR) reconstruction enhance the spatial-resolution of 3D magnetic resonance imaging (MRI) for MR-guided radiotherapy (MRgRT). However, variations between MRI scanners and patients impact the quality of SR for real-time 3D low-resolution (LR) cine MRI. In this study, we present a personalized super-resolution (psSR) network that incorporates transfer-learning to overcome the challenges in inter-scanner SR of 3D cine MRI.
Approach: Development of the proposed psSR network comprises two-stages: 1) a cohort-specific SR (csSR) network using clinical patient datasets, and 2) a psSR network using transfer-learning to target datasets...

OBJECTIVES: To assess a deep learning-based reconstruction algorithm (DLRecon) in zero echo-time (ZTE) MRI of the shoulder at 1.5 Tesla for improved delineation of osseous findings. METHODS: In this retrospective study, 63 consecutive exams of 52 patients (28 female) undergoing shoulder MRI at 1.5 Tesla in clinical routine were included. Coronal 3D isotropic radial ZTE pulse sequences were acquired in the standard MR shoulder protocol. In addition to standard-of-care (SOC) image reconstruction, the same raw data was reconstructed with a vendor-supplied prototype DLRecon algorithm...

Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction...