Fractional order vs. exponential fitting in UTE MR imaging of the patellar tendon

Purpose: Quantification of the T2 ∗ relaxation time constant is relevant in various magnetic resonance imaging applications. Mono- or bi-exponential models are typically used to determine these parameters. However, in case of complex, heterogeneous tissues these models could lead to inaccurate results. We compared a model, provided by the fractional-order extension of the Bloch equation with the conventional models. Methods: Axial 3D ultra-short echo time (UTE) scans were acquired using a 3.0 T MRI and a 16-channel surface coil. After image registration, voxel-wise T2 ∗ was quantified with mono-exponential, bi-exponential and fractional-order fitting. We evaluated all three models repeatability and the bias of their derived parameters by fitting at various noise levels. To investigate the effect of the SNR for the different models, a Monte-Carlo experiment with 1000 repeats was performed for different noise levels for one subject. For a cross-sectional investigation, we used the mean fitted values of the ROIs in five volunteers. Results: Comparing the mono-exponential and the fractional order T2 ∗ maps, the fractional order fitting method yielded enhanced contrast and an improved delineation of the different tissues. In the case of the bi-exponential method, the long T2 ∗ component map demonstrated the anatomy clearly with high contrast. Simulations showed a nonzero bias of the parameters for all three mathematical models. ROI based fitting showed that the T2 ∗ values were different depending on the applied method, and they differed most for the patellar tendon in all subjects. Conclusions: In high SNR cases, the fractional order and bi-exponential models are both performing well with low bias. However, in all observed cases, one of the bi-exponential components has high standard deviation in T2 ∗. The bi-exponential model is suitable for T2 ∗ mapping, but we recommend using the fractional order model for cases of low SNR

Compressed Sensing 3D-GRASE for faster High-Resolution MRI

textabstractPurpose: High-resolution three-dimensional (3D) structural MRI is useful for delineating complex or small structures of the body. However, it requires long acquisition times and high SAR, limiting its clinical use. The purpose of this work is to accelerate the acquisition of high-resolution images by combining compressed sensing and parallel imaging (CSPI) on a 3D-GRASE sequence and to compare it with a (CS)PI 3D-FSE sequence. Several sampling patterns were investigated to assess their influence on image quality. Methods: The proposed k-space sampling patterns are based on two undersampled k-space grids, variable density (VD) Poisson-disc, and VD pseudo-random Gaussian, and five different trajectories described in the literature. Bloch simulations are performed to obtain the transform point spread function and evaluate the coherence of each sampling pattern. Image resolution was assessed by the full-width at half-maximum (FWHM). Prospective CSPI 3D-GRASE phantom and in vivo experiments in knee and brain are carried out to assess image quality, SNR, SAR, and acquisition time compared to PI 3D-GRASE, PI 3D-FSE, and CSPI 3D-FSE acquisitions. Results: Sampling patterns with VD Poisson-disc obtain the lowest coherence for both PD-weighted and T2 -weighted acquisitions. VD pseudo-random Gaussian obtains lower FWHM, but higher sidelobes than VD Poisson-disc. CSPI 3D-GRASE reduces acquisition time (43% for PD-weighted and 40% for T2 -weighted) and SAR (∼45% for PD-weighted and T2 -weighted) compared to CSPI 3D-FSE. Conclusions: CSPI 3D-GRASE reduces acquisition time compared to a CSPI 3DFSE acquisition, preserving image quality. The design of the sampling pattern is crucial for image quality in CSPI 3D-GRASE image acquisitions

A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice

Arterial spin labeling (ASL) is a non-invasive MRI technique to measure cerebral blood flow (CBF). This review provides a practical guide and overview of the clinical applications of ASL of the brain, as well its potential pitfalls. The technical and physiological background is also addressed. At present, main areas of interest are cerebrovascular disease, dementia and neuro-oncology. In cerebrovascular disease, ASL is of particular interest owing to its quantitative nature and its capability to determine cerebral arterial territories. In acute stroke, the source of the collateral blood supply in the penumbra may be visualised. In chronic cerebrovascular disease, the extent and severity of compromised cerebral perfusion can be visualised, which may be used to guide therapeutic or preventative intervention. ASL has potential for the detection and follow-up of arteriovenous malformations. In the workup of dementia patients, ASL is proposed as a diagnostic alternative to PET. It can easily be added to the routinely performed structural MRI examination. In patients with established Alzheimer’s disease and frontotemporal dementia, hypoperfusion patterns are seen that are similar to hypometabolism patterns seen with PET. Studies on ASL in brain tumour imaging indicate a high correlation between areas of increased CBF as measured with ASL and increased cerebral blood volume as measured with dynamic susceptibility contrast-enhanced perfusion imaging. Major advantages of ASL for brain tumour imaging are the fact that CBF measurements are not influenced by breakdown of the blood–brain barrier, as well as its quantitative nature, facilitating multicentre and longitudinal studies