Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning

Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities

Accelerated calibrationless parallel transmit mapping using joint transmit and receive low-rank tensor completion

Purpose To evaluate an algorithm for calibrationless parallel imaging to reconstruct undersampled parallel transmit field maps for the body and brain. Methods Using a combination of synthetic data and in vivo measurements from brain and body, 3 different approaches to a joint transmit and receive low-rank tensor completion algorithm are evaluated. These methods included: 1) virtual coils using the product of receive and transmit sensitivities, 2) joint-receiver coils that enforces a low rank structure across receive coils of all transmit modes, and 3) transmit low rank that uses a low rank structure for both receive and transmit modes simultaneously. The performance of each is investigated for different noise levels and different acceleration rates on an 8-channel parallel transmit 7 Tesla system. Results The virtual coils method broke down with increasing noise levels or acceleration rates greater than 2, producing normalized RMS error greater than 0.1. The joint receiver coils method worked well up to acceleration factors of 4, beyond which the normalized RMS error exceeded 0.1. Transmit low rank enabled an eightfold acceleration, with most normalized RMS errors remaining below 0.1. Conclusion This work demonstrates that undersampling factors of up to eightfold are feasible for transmit array mapping and can be reconstructed using calibrationless parallel imaging methods

Data for: Accelerated calibrationless parallel transmit mapping using joint transmit and receive low-rank tensor completion

These data provide low flip angle spoiled gradient echo images to measure the sensitivities of transmit and receiver arrays at 7T MRI. Both synthetic and measured data are included for both the heart and brain. The data were generated and measured to evaluate algorithms for reconstructing under-sampled parallel transmit maps. These algorithms are described in the journal article with title "Accelerated calibrationless parallel transmit mapping using joint transmit and receive low-rank tensor completion". The data are in the Matlab .mat format and can be processed by code provided https://users.fmrib.ox.ac.uk/~mchiew/Research.htm

Multi-Slice 2D pTx Readout-Segmented Diffusion-Weighted Imaging Using Slice-by-Slice B1+ Shimming

No abstract available

Rapid 3D absolute B1+ mapping using a sandwiched train presaturated TurboFLASH sequence at 7 T for the brain and heart

Purpose:&nbsp;To shorten the acquisition time of magnetization-prepared absolute transmit field (B1+) mapping known as presaturation TurboFLASH, or satTFL, to enable single breath-hold whole-heart 3D B1+&nbsp;mapping. Methods:&nbsp;SatTFL is modified to remove the delay between the reference and prepared images (typically 5 T1), with matching transmit configurations for excitation and preparation RF pulses. The new method, called Sandwich,&nbsp;is evaluated as a 3D sequence, measuring whole-brain and gated whole-heart B1+&nbsp;maps in a single breath-hold. We evaluate the sensitivity to B1+&nbsp;and T1&nbsp;using numerical Bloch, extended phase graph, and Monte Carlo simulations. Phantom and in vivo images were acquired in both the brain and heart using an 8-channel transmit 7 Tesla MRI system to support the simulations. A segmented satTFL with a short readout train was used as a reference. Results:&nbsp;The method significantly reduces acquisition times of 3D measurements from 360&thinsp;s to 20&thinsp;s, in the brain, while simultaneously reducing bias in the measured B1+&nbsp;due to T1&nbsp;and magnetization history. The mean coefficient of variation was reduced by 81% for T1s of 0.5&ndash;3&nbsp;s compared to conventional satTFL. In vivo, the reproducibility coefficient for flip angles in the range 0&ndash;130&deg; was 4.5&deg; for satTFL and 4.7&deg; for our scheme, significantly smaller than for a short TR satTFL sequence, which was 12&deg;. The 3D sequence measured B1+&nbsp;maps of the whole thorax in 26 heartbeats. Conclusion:&nbsp;Our adaptations enable faster B1+&nbsp;mapping, with minimal T1&nbsp;sensitivity and lower sensitivity to magnetization history, enabling single breath-hold whole-heart absolute B1+&nbsp;mapping.</p

Optimized dark-blood imaging for evaluation of the aorta and subclavian arteries in patients with giant cell arteritis

Giant cell arteritis (GCA), also known as temporal arteritis or Horton’s disease, is a granulomatous vasculitis of large- and medium-sized arteries. The disease usually concerns the superficial cranial arteries with predominance of the temporal arteries. However, GCA is not necessarily localized specifically to the temporal or cranial arteries. Involvement of extracranial arteries, mainly the aorta with its branches can also occur. In this work, we present a novel high resolution multi-contrast MR protocol allowing the depiction of vascular geometry with large coverage including the aorta and the subclavian arteries

Aortic atheroma as a source of stroke - assessment of embolization risk using 3D CMR in stroke patients and controls

Background It was our purpose to identify vulnerable plaques in the thoracic aorta using 3D multi-contrast CMR and estimate the risk of cerebral embolization using 4D flow CMR in cryptogenic stroke patients and controls. Methods One hundred patients (40 with cryptogenic stroke, 60 ophthalmologic controls matched for age, sex and presence of hypertension) underwent a novel 3D multi-contrast (T1w, T2w, PDw) CMR protocol at 3 Tesla for plaque detection and characterization within the thoracic aorta, which was combined with 4D flow CMR for mapping potential embolization pathways. Plaque morphology was assessed in consensus reading by two investigators and classified according to the modified American-Heart-Association (AHA) classification of atherosclerotic plaques. Results In the thoracic aorta, plaques <4 mm thickness were found in a similar number of stroke patients and controls [23 (57.5%) versus 33 (55.0%); p = 0.81]. However, plaques ≥4 mm were more frequent in stroke patients [22 (55.0%) versus 10 (16.7%); p < 0.001]. Of those patients with plaques ≥4 mm, seven (17.5%) stroke patients and two (3.3%) controls (p < 0.001) had potentially vulnerable AHA type VI plaques. Six stroke patients with vulnerable AHA type VI plaques ≥4 mm had potential embolization pathways connecting the plaque, located in the aortic arch (n = 3) and proximal descending aorta (n = 3), with the individual territory of stroke, which made them the most likely source of stroke in those patients. Conclusions Our findings underline the significance of ≥4 mm thick and vulnerable plaques in the aortic arch and descending aorta as a relevant etiology of stroke

Total Mapping Toolbox (TOMATO): an open source library for cardiac magnetic resonance parametric mapping

TOMATO (Total Mapping Toolbox) is a C++ library for the calculation of parametric maps in cardiac magnetic resonance imaging (MRI). As an open source project, TOMATO allows transparent and standardised cardiac longitudinal relaxation time (T1) mapping in clinical applications. With C++ implementation, TOMATO can easily interface and translate between research software environments and commercial vendors’ closed-source C++ environments on scanners as well as post-processing software. To complement the core library implementation, a ready-to-use command line tool has been provided