Search

PurposeTo explore the feasibility of 2D and 3D golden-angle radial acquisition strategies in conjunction with k-space weighted image contrast (KWIC) temporal filtering to achieve noncontrast enhanced dynamic MRA (dMRA) with high spatial resolution, low streaking artifacts and high temporal fidelity.MethodsSimulations and in vivo examinations in eight normal volunteers and an arteriovenous malformation patient were carried out. Both 2D and 3D golden angle radial sequences, preceded by spin tagging, were used for dMRA of the brain. The radial dMRA data were temporally filtered using the KWIC strategy and compared with matched standard Cartesian techniques.ResultsThe 2D and 3D dynamic MRA image series acquired with the proposed radial techniques demonstrated excellent image quality without discernible temporal blurring compared with standard Cartesian based approaches. The image quality of radial dMRA was equivalent to or higher than that of Cartesian dMRA by visual inspection. A reduction factor of up to 10 and 3 in scan time was achieved for 2D and 3D radial dMRA compared with the Cartesian-based counterparts.ConclusionThe proposed 2D and 3D radial dMRA techniques demonstrated image quality comparable or even superior to those obtained with standard Cartesian methods, but within a fraction of the scan time.

BackgroundLate gadolinium enhanced (LGE) cardiovascular magnetic resonance (CMR) is frequently used to evaluate myocardial viability, estimate total infarct size and transmurality, but is not always straightforward is and contraindicated in patients with renal failure because of the risk of nephrogenic systemic fibrosis. T2- and T1-weighted CMR alone is however relatively insensitive to chronic myocardial infarction (MI) in the absence of a contrast agent. The objective of this manuscript is to explore T1ρ-weighted rotating frame CMR techniques for infarct characterization without contrast agents. We hypothesize that T1ρ CMR accurately measures infarct size in chronic MI on account of a large change in T1ρ relaxation time between scar and myocardium.Methods7Yorkshire swine underwent CMR at 8 weeks post-surgical induction of apical or posterolateral myocardial infarction. Late gadolinium enhanced and T1ρ CMR were performed at high resolution to visualize MI. T1ρ-weighted imaging was performed with a B₁  = 500 Hz spin lock pulse on a 3 T clinical MR scanner. Following sacrifice, the heart was excised and infarct size was calculated by optical planimetry. Infarct size was calculated for all three methods (LGE, T1ρ and planimetry) and statistical analysis was performed. T1ρ relaxation time maps were computed from multiple T1ρ-weighted images at varying spin lock duration.ResultsMean infarct contrast-to-noise ratio (CNR) in LGE and T1ρ CMR was 2.8 ± 0.1 and 2.7 ± 0.1. The variation in signal intensity of tissues was found to be, in order of decreasing signal intensity, LV blood, fat and edema, infarct and healthy myocardium. Infarct size measured by T1ρ CMR (21.1% ± 1.4%) was not significantly different from LGE CMR (22.2% ± 1.5%) or planimetry (21.1% ± 2.7%; p

Purpose: To accelerate radially sampled diffusion weighted spin‐echo (Rad‐DW‐SE) acquisition method for generating high quality ADC maps. Methods: A deep learning method was developed to generate accurate ADC maps from accelerated DWI data acquired with the Rad‐DW‐SE method. The deep learning method integrates convolutional neural networks (CNNs) with vision transformers to generate high quality ADC maps from accelerated DWI data, regularized by a monoexponential ADC model fitting term. A model was trained on DWI data of 147 mice and evaluated on DWI data of 36 mice, with acceleration factors of 4× and 8× compared to the original acquisition parameters. Results: Ablation studies and experimental results have demonstrated that the proposed deep learning model generates higher quality ADC maps from accelerated DWI data than alternative deep learning methods under comparison when their performance is quantified in whole images as well as in regions of interest, including tumors, kidneys, and muscles. Conclusions: The deep learning method with integrated CNNs and transformers provides an effective means to accurately compute ADC maps from accelerated DWI data acquired with the Rad‐DW‐SE method. [ABSTRACT FROM AUTHOR]

Simple Summary: Intraductal papillary mucinous neoplasms (IPMN) are common premalignant precursors to pancreatic ductal adenocarcinoma (PDAC). However, current guidelines for clinical management of IPMN are suboptimal and limited to information obtained through anatomical imaging and cytology of biopsy samples. Our results have suggested that diffusion-weighted MRI, an imaging method sensitive to tumor cell density, architecture, and microenvironment is able to differentiate mouse models of IPMN versus PDAC. KPC (KrasG12D:Trp53R172H:Pdx1-Cre) and CKS (KrasG12D:Smad4L/L:Ptf1a-Cre) mice are genetically engineered mouse (GEM) models that capture features of human pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasms (IPMN), respectively. We compared these autochthonous tumors using quantitative imaging metrics from diffusion-weighted MRI (DW-MRI) and dynamic contrast enhanced (DCE)-MRI in reference to quantitative histological metrics including cell density, fibrosis, and microvasculature density. Our results revealed distinct DW-MRI metrics between the KPC vs. CKS model (mimicking human PDAC vs. IPMN lesion): the apparent diffusion coefficient (ADC) of CKS tumors is significantly higher than that of KPC, with little overlap (mean ± SD 2.24 ± 0.2 vs. 1.66 ± 0.2 , p < 10 − 10 ) despite intratumor and intertumor variability. Kurtosis index (KI) is also distinctively separated in the two models. DW imaging metrics are consistent with growth pattern, cell density, and the cystic nature of the CKS tumors. Coregistration of ex vivo ADC maps with H&E-stained sections allowed for regional comparison and showed a correlation between local cell density and ADC value. In conclusion, studies in GEM models demonstrate the potential utility of diffusion-weighted MRI metrics for distinguishing pancreatic cancer from benign pancreatic cysts such as IPMN. [ABSTRACT FROM AUTHOR]

A postprocessing technique is proposed for the correction of both translational and rotational motion artifacts in magnetic resonance imaging (MRI). The method consists of two steps: 1) k-space extrapolation to generate a motion-free reference, followed by 2) correlation with actual data to estimate motion. In this paper, two different extrapolation methods were investigated for the purpose of motion estimation: edge enhancement and finite-support solution. It was found that finite-support solution performs better near the k-space center, while the edge enhancement method is superior in the outer k-space regions. Therefore, a combination of the two methods was employed to generate a motion-free reference, whose correlations with the acquired data can subsequently determine the object motion. Motion compensation was demonstrated in simulation and in vivo MR experiments. The technique is shown to be robust against noise and various types of motion. Index Terms--Correlation, EXTRACT, extrapolation, magnetic resonance imaging (MRI), motion artifacts, motion correction.

A technique is proposed for correcting both translational and rotational motion artifacts in magnetic resonance imaging without the need to collect additional navigator data or to perform intensive postprocessing. The method is based on measuring the point spread function (PSF) by attaching one or two point-sized markers to the main imaging object. Following the isolation of a PSF marker from the acquired image, translational motion could be corrected directly from the modulation transfer function, without the need to determine the object's positions during the scan, although the shifts could be extracted if desired. Rotation is detected by analyzing the relative displacements of two such markers. The technique was evaluated with simulations, phantom and in vivo experiments. Index Terms--Modulation transfer function, motion correction, MRI, point spread function.