White Matter Hyperintensities and Poststroke Apathy: A Fully Automated MRI Segmentation Study.

INTRODUCTION: Poststroke apathy (PSA) is a common neuropsychiatric disorder that may affect up to 30% of stroke patients. Despite the difficulties of investigating this condition (overlapping with depression, heterogeneity of diagnostic criteria, a small number of studies), some recent diffusion tensor imaging studies have suggested that widespread microstructural white matter (WM) disruption plays a key role in the development of PSA. Therefore, we intended to investigate this hypothesis by evaluating the relationship between WM hyperintensities (WMH) and apathy in patients with cerebrovascular disease. METHODS: We studied patients with apathy (n = 7), depression (n = 13), comorbid apathy and depression (n = 13), and controls (n = 20), and we investigated the variables associated with the volume of WMH measured by an automated brain MRI segmentation software. RESULTS: The overall prevalence of PSA was 37.7% (pure and comorbid). Patients with apathy presented a higher volume of WMH in comparison to controls. Mini-Mental State Examination (MMSE), NPI-A, and the number of cerebral microbleeds were the only variables associated with WMH. Conversely, NPI-D did not correlate to WMH. DISCUSSION/CONCLUSION: This is an exploratory study that supports the view of PSA as a distinct syndrome from PSD mediated mainly by diffuse white matter hyperintensities, which suggests that WM disruption is an important pathway to the development of apathy in stroke patients.

An automated computational biomechanics workflow for improving breast cancer diagnosis and treatment.

Clinicians face many challenges when diagnosing and treating breast cancer. These challenges include interpreting and co-locating information between different medical imaging modalities that are used to identify tumours and predicting where these tumours move to during different treatment procedures. We have developed a novel automated breast image analysis workflow that integrates state-of-the-art image processing and machine learning techniques, personalized three-dimensional biomechanical modelling and population-based statistical analysis to assist clinicians during breast cancer detection and treatment procedures. This paper summarizes our recent research to address the various technical and implementation challenges associated with creating a fully automated system. The workflow is applied to predict the repositioning of tumours from the prone position, where diagnostic magnetic resonance imaging is performed, to the supine position where treatment procedures are performed. We discuss our recent advances towards addressing challenges in identifying the mechanical properties of the breast and evaluating the accuracy of the biomechanical models. We also describe our progress in implementing a prototype of this workflow in clinical practice. Clinical adoption of these state-of-the-art modelling techniques has significant potential for reducing the number of misdiagnosed breast cancers, while also helping to improve the treatment of patients.