arXiv subjects

Alan Bainbridge

Publications and source records attributed to Alan Bainbridge.

3 recordsLinked to original sources

arXiv2021

MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling

Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for Magnitude-Only PDFF and R2* estimation with Rician Noise modelling (MAGORINO). Methods: Simulations of multi-echo gradient echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically-plausible PDFF, R2* and signal-to-noise ratio (SNR) values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multi-site, multi-vendor and multi-field-strength phantom dataset and in vivo. Results: Simulations show that Rician noise-based magnitude fitting outperforms existing Gaussian noise-based fitting and reveals two key mechanisms underpinning the observed improvement. Firstly, the likelihood functions exhibit two local optima; Rician noise modelling increases the chance the global optimum corresponds to the ground truth. Secondly, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modelling is closer to ground truth. Multisite phantom experiments show good agreement of MAGORINO PDFF with reference values, and in vivo experiments replicate the performance benefits observed in simulation. Conclusion: MAGORINO reduces Rician noise-related bias in PDFF and R2* estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer an insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.

q-bio.QM↗

arXiv2021

Volume of hyperintense inflammation (VHI): a deep learning-enabled quantitative imaging biomarker of inflammation load in spondyloarthritis

Short inversion time inversion recovery (STIR) MRI is widely used in clinical practice to identify and quantify inflammation in axial spondyloarthritis. However, assessment of STIR images is limited by the need for qualitative evaluation, which depends on observer experience and expertise, creating substantial variability in inflammation assessments. To address this problem, we developed a deep learning-enabled, semiautomated workflow for segmentation of inflammatory lesions, whereby an initial segmentation is generated automatically and a radiologist then 'cleans' the segmentation by removing extraneous segmented voxels. The final cleaned segmentation defines the volume of hyperintense inflammation (VHI), which we propose as a quantitative imaging biomarker of inflammation load in spondyloarthritis. The data, code and models used in the study are available at https://github.com/c-hepburn/Bone_MRI.

eess.IV↗

arXiv2021

Negative-contrast neurography: Imaging the extracranial facial nerve and its branches using contrast-enhanced variable flip angle turbo spin echo MRI

Background and Purpose: Various 'positive-contrast' neurographic methods have been investigated for imaging the extracranial course of the facial nerve. However, nerve visibility can be inconsistent with these sequences and may depend on the composition of the parotid gland, limiting consistent identification. To address this, we describe and evaluate a 'negative-contrast' method for imaging of the extracranial facial nerve using three-dimensional variable flip angle turbo spin echo (VFA-TSE) imaging. We investigate strategies for further optimization, including parotid-specific VFA-TSE optimization and the use of gadolinium-based contrast agent (GBCA). Materials and Methods: 6 healthy volunteers and 10 patients with parotid tumors underwent VFA-TSE and double echo steady state (DESS) imaging of the extracranial facial nerve at 3T. The main trunk, divisions and branches of the extracranial facial nerve were manually segmented by three radiologists, enabling CNR and Hausdorff distance computation and confidence scoring. CNR, Hausdorff distance and confidence scores were compared between sequences and between pre- and post-contrast imaging to evaluate the effect of GBCA. Results: CNR, Hausdorff distances and confidence scores were superior for VFA-TSE compared to DESS imaging. GBCA administration produced a further increase in CNR of nerve against parotid and improved differentiation of nerve from tumor. Conclusion: Imaging of the extracranial facial nerve with VFA-TSE depicts the nerve as a low signal structure ('black nerve') against the high signal parotid parenchyma ('white parotid') and outperforms positive-contrast DESS imaging in terms of CNR, segmentation consistency and confidence. GBCA further increases negative contrast and improves differentiation of nerve from tumor.

physics.med-ph↗