ORCID Profile
0000-0002-3034-8986
Current Organisation
University of Oxford
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Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2021
Publisher: Cold Spring Harbor Laboratory
Date: 15-07-2017
DOI: 10.1101/163196
Abstract: Clinical pain is difficult to study using standard Blood Oxygenation Level Dependent (BOLD) magnetic resonance imaging because it is often ongoing and, if evoked, it is associated with stimulus-correlated motion. Arterial spin labelling (ASL) offers an attractive alternative. This study used arm repositioning to evoke clinically-relevant musculoskeletal pain in patients with shoulder impingement syndrome. Fifty-five patients were scanned using a multi post-labelling delay pseudo-continuous ASL (pCASL) sequence, first with both arms along the body and then with the affected arm raised into a painful position. Twenty healthy volunteers were scanned as a control group. Arm repositioning resulted in increased perfusion in brain regions involved in sensory processing and movement integration, such as the contralateral primary motor and primary somatosensory cortex, mid- and posterior cingulate cortex, and, bilaterally, in the insular cortex/operculum, putamen, thalamus, midbrain and cerebellum. Perfusion in the thalamus, midbrain and cerebellum was larger in the patient group. Results of a post hoc analysis suggested that the observed perfusion changes were related to pain rather than arm repositioning. This study showed that ASL can be useful in research on clinical ongoing musculoskeletal pain but the technique is not sensitive enough to detect small differences in perfusion.
Publisher: Springer Science and Business Media LLC
Date: 15-12-2022
DOI: 10.1007/S00261-022-03762-4
Abstract: R2*, a measurement obtained using magnetic resonance imaging (MRI) can be used to estimate liver iron concentration (LIC). 3 T and 1.5 T scanners can be used but conversion of 3 T R2* to LIC is less well validated. In this study the aim was to compare 3 T-R2* LIC and 1.5 T-R2* LIC estimations to assess if they can be used interchangeably. Thirty participants were scanned at both 1.5 T and 3 T. R2* was measured at both field strengths. 3 T R2* and 1.5 R2* were compared using linear regression and were converted to LIC using different calibration curves. Pearson’s rho and Intraclass Correlation Coefficients (ICCs) were used to assess correlation and agreement between 1.5 and 3 T LIC. Bland Altman plots were used to assess bias and limits of agreement. All 1.5 T and 3 T LIC comparisons gave Pearson’s rho of 0.99 ( p 0.001). ICC ranged from 0.83 ( p = 0.005) to 0.96 ( p 0.001). Biases had magnitude of less than 0.2 mg/g dry weight. Agreement and bias between 3 and 1.5 T-R2* LIC depended on the method used for conversion. There were instances when the agreement was excellent and bias was small, indicating that potentially 3 T-R2* LIC can be used alongside or instead of 1.5 T-R2* LIC but care needs to be taken over the conversion methods selected. Clinicaltrials.gov NCT03743272, 16 November 2018.
Publisher: Cold Spring Harbor Laboratory
Date: 30-11-2021
DOI: 10.1101/2021.11.30.21266158
Abstract: Pancreatic disease can be spatially inhomogeneous. For this reason, quantitative imaging studies of the pancreas have often targeted the 3 main anatomical pancreatic segments, head, body, and tail, traditionally using a balanced region of interest (ROI) strategy. Existing automated analysis methods have implemented whole-organ segmentation, which provides an overall quantification, but fails to address spatial heterogeneity in disease. A method to automatically refine a whole-organ segmentation of the pancreas into head, body, and tail subregions is presented for abdominal magnetic resonance imaging (MRI). The subsegmentation method is based on diffeomorphic registration to a group average template image, where the parts are manually annotated. For a new whole-pancreas segmentation, the aligned template’s part labels are automatically propagated to the segmentation of interest. The method is validated retrospectively on the UK Biobank imaging substudy (scanned using a 2-point Dixon protocol at 1.5 tesla), using a nominally healthy cohort of 100 subjects for template creation, and 50 independent subjects for validation. Pancreas head, body, and tail were annotated by multiple experts on the validation cohort, which served as the benchmark for the automated method’s performance. Good intra-rater (Dice overlap mean, Head: 0.982, Body: 0.940, Tail: 0.961, N=30) as well as inter-rater (Dice overlap mean, Head: 0.968, Body: 0.905, Tail: 0.943, N=150) agreement was observed. No significant difference (Wilcoxon rank sum test, DSC, Head: p=0.4358, Body: p=0.0992, Tail: p=0.1080) was observed between the manual annotations and the automated method’s predictions. Results on regional pancreatic fat assessment are also presented, by intersecting the 3-D parts segmentation with one 2-D multi-echo gradient-echo slice, available from the same scanning session, that was used to compute MRI proton density fat fraction (MRI-PDFF). Initial application of the method on a type 2 diabetes cohort showed the utility of the method for assessing pancreatic disease heterogeneity.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Daniel Bulte.