MRI Super-Resolution using Multi-Channel Total Variation

This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper introduces a prior based on multi-channel total variation for MRI super-resolution. Bias-variance trade-off is handled by estimating hyper-parameters from the low resolution input scans. The model was validated on a large database of brain images. The validation showed that the model can improve brain segmentation, that it can recover anatomical information between images of different MR contrasts, and that it generalises well to the large variability present in MR images of different subjects. The implementation is freely available at https://github.com/brudfors/spm_superre

An artificial intelligence natural language processing pipeline for information extraction in neuroradiology

The use of electronic health records in medical research is difficult because of the unstructured format. Extracting information within reports and summarising patient presentations in a way amenable to downstream analysis would be enormously beneficial for operational and clinical research. In this work we present a natural language processing pipeline for information extraction of radiological reports in neurology. Our pipeline uses a hybrid sequence of rule-based and artificial intelligence models to accurately extract and summarise neurological reports. We train and evaluate a custom language model on a corpus of 150000 radiological reports from National Hospital for Neurology and Neurosurgery, London MRI imaging. We also present results for standard NLP tasks on domain-specific neuroradiology datasets. We show our pipeline, called `neuroNLP', can reliably extract clinically relevant information from these reports, enabling downstream modelling of reports and associated imaging on a heretofore unprecedented scale.Comment: 20 pages, 2 png image figure

MRI Super-Resolution Using Multi-channel Total Variation

This paper presents a generative model for super-resolution in routine clinical magnetic resonance images (MRI), of arbitrary orientation and contrast. The model recasts the recovery of high resolution images as an inverse problem, in which a forward model simulates the slice-select profile of the MR scanner. The paper introduces a prior based on multi-channel total variation for MRI super-resolution. Bias-variance trade-off is handled by estimating hyper-parameters from the low resolution input scans. The model was validated on a large database of brain images. The validation showed that the model can improve brain segmentation, that it can recover anatomical information between images of different MR contrasts, and that it generalises well to the large variability present in MR images of different subjects

Solid NURBS Conforming Scaffolding for Isogeometric Analysis

This work introduces a scaffolding framework to compactly parametrise solid structures with conforming NURBS elements for isogeometric analysis. A novel formulation introduces a topological, geometrical and parametric subdivision of the space in a minimal plurality of conforming vectorial elements. These determine a multi-compartmental scaffolding for arbitrary branching patterns. A solid smoothing paradigm is devised for the conforming scaffolding achieving higher than positional geometrical and parametric continuity. Results are shown for synthetic shapes of varying complexity, for modular CAD geometries, for branching structures from tessellated meshes and for organic biological structures from imaging data. Representative simulations demonstrate the validity of the introduced scaffolding framework with scalable performance and groundbreaking applications for isogeometric analysis

Machine prescription for chronic migraine

Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring prolonged—currently heuristic—individual evaluation of available treatments, may reflect a diversity of causal mechanisms, or the failure to identify the most important, single causal factor. Distinguishing between these possibilities, now possible through the application of complex modelling to large-scale data, is critical to determining the optimal approach to identifying new interventions in migraine and making the best use of existing ones. Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, here we use causal multitask Gaussian process models to estimate individual treatment effects across ten classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to-response when sequentially trialling preventatives by individualized treatment effects and compare it to expert guideline sequences. All model performances are quantified out-of-sample. We identify significantly higher true response rates among individuals modelled to respond, compared to the overall population (mean difference of 0.034; 95% confidence interval 0.003 to 0.065; p = 0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% confidence interval 3.507 to 3.993; p < 0.0001) compared with expert guidelines, with no substantive increase in expense per patient. We conclude that the highly distributed mode of causation in chronic migraine necessitates high-dimensional modelling for optimal management. Machine prescription should be considered an essential clinical decision-support tool in the future management of chronic migraine

Characterizing phonemic fluency by transfer learning with deep language models

Though phonemic fluency tasks are traditionally indexed by the number of correct responses, the underlying disorder may shape the specific choice of words—both correct and erroneous. We report the first comprehensive qualitative analysis of incorrect and correct words generated on the phonemic (‘S’) fluency test, in a large sample of patients (n = 239) with focal, unilateral frontal or posterior lesions and healthy controls (n = 136). We conducted detailed qualitative analyses of the single words generated in the phonemic fluency task using categorical descriptions for different types of errors, low-frequency words and clustering/switching. We further analysed patients’ and healthy controls’ entire sequences of words by employing stochastic block modelling of Generative Pretrained Transformer 3–based deep language representations. We conducted predictive modelling to investigate whether deep language representations of word sequences improved the accuracy of detecting the presence of frontal lesions using the phonemic fluency test. Our qualitative analyses of the single words generated revealed several novel findings. For the different types of errors analysed, we found a non-lateralized frontal effect for profanities, left frontal effects for proper nouns and permutations and a left posterior effect for perseverations. For correct words, we found a left frontal effect for low-frequency words. Our novel large language model–based approach found five distinct communities whose varied word selection patterns reflected characteristic demographic and clinical features. Predictive modelling showed that a model based on Generative Pretrained Transformer 3–derived word sequence representations predicted the presence of frontal lesions with greater fidelity than models of native features. Our study reveals a characteristic pattern of phonemic fluency responses produced by patients with frontal lesions. These findings demonstrate the significant inferential and diagnostic value of characterizing qualitative features of phonemic fluency performance with large language models and stochastic block modelling

Brain tumour segmentation with incomplete data

Brain tumour segmentation remains a challenging task, complicated by the marked heterogeneity of imaging appearances and their distribution across multiple modalities: FLAIR, T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences (T1CE). This has compelled a research focus on uniformly multimodal models trained on complete acquisition sets rare in real-world clinical practice. Consider, for example, patients with renal failure who cannot receive contrast, artefact-spoiled sequences, or patients undergoing single-sequence intraoperative imaging. How well do segmentation models perform with such incomplete data, and what features of the lesion are identifiable under these circumstances? In a large-scale analysis involving 30 distinct segmentation models, we answer these questions with a state-of-the-art tumour segmentation modelling ensemble, nnU-Net-derived (Isensee et al, Nature Methods, 2020), deployed across all possible combinations of imaging modalities, trained, and tested with five-fold cross-validation on the 2021 BraTS-RSNA glioma population of 1251 patients. Segmentation performances for whole lesions range from Dice scores of 0.907 (single sequence) to 0.945 (full datasets) (Figure 1). When segmenting lesions by tissue type (enhancing tumour, non-enhancing tumour and oedema), Dice scores range from 0.701 (single sequence) to 0.891 (full datasets). Models missing postcontrast imaging still achieve a Dice coefficient for the whole tumour of 0.942 and identify the enhancing tumour component with Dice of up to 0.790 (Figure 2). Segmentation models can identify tumours with missing data, and can be used in clinical situations where partial data is frequent