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Objectives: To assess how radiomic features may be combined with plaque morphological and compositional features identified by multi-contrast MRI to improve upon conventional risk assessment models in determining culprit carotid artery lesions., Methods: Fifty-five patients (mean age: 62.6; 35 males) with bilateral carotid stenosis who experienced transient ischaemic attack (TIA) or stroke were included from the CARE-II multi-centre carotid imaging trial (ClinicalTrials.gov Identifier: NCT02017756). They underwent MRI within 2 weeks of the event. Classification capability in distinguishing culprit lesions was assessed by machine learning. Repeatability and reproducibility of the results were investigated by assessing the robustness of the radiomic features., Results: Radiomics combined with a relatively conventional plaque morphological and compositional metric-based model provided incremental value over a conventional model alone (area under curve [AUC], 0.819 ± 0.002 vs 0.689 ± 0.019, respectively, P = .014). The radiomic model alone also provided value over the conventional model (AUC, 0.805 ± 0.003 vs 0.689 ± 0.019, respectively, P = .031). T2-weighted imaging-based radiomic features had consistently higher robustness and classification capabilities compared with T1-weighted images. Higher-dimensional radiomic features outperformed first-order features. Grey Level Co-occurrence Matrix, Grey Level Dependence Matrix, and Grey Level Size Zone Matrix sub-types were particularly useful in identifying textures which could detect vulnerable lesions., Conclusions: The combination of MRI-based radiomic features and lesion morphological and compositional parameters provided added value to the reference-standard risk assessment for carotid atherosclerosis. This may improve future risk stratification for individuals at risk of major adverse ischaemic cerebrovascular events., Advances in Knowledge: The clinical relevance of this work is that it addresses the need for a more comprehensive method of risk assessment for patients at risk of ischaemic stroke, beyond conventional stenosis measurement. This paper shows that in the case of carotid stroke, high-dimensional radiomics features can improve classification capabilities compared with stenosis measurement alone., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology.)

Background: Adequate tissue perfusion is an important prognostic and diagnostic factor during the management of lower limb peripheral arterial disease. Convenient and real-time tissue perfusion monitoring remains an elusive challenge., Methods: Tissue perfusion on the dorsal and plantar surfaces of both feet of 20 participants was measured during and after cuff-induced ischemia using a novel 4-channel, laser-based perfusion monitoring device based on diffuse speckle contrast analysis technology (Pedra sensors). Participants were free of significant peripheral arterial disease. Transcutaneous partial pressure of oxygen (TcPO2) measurements were recorded concurrently for comparison., Results: Pedra sensors detected perfusion changes significantly more quickly than TcPO2 sensors. One minute after induced ischemia, the mean percent changes from baseline values (before ischemia) were -22.7±32.0% and -3.1±8.8% (P<0.001) for Pedra and TcPO2 sensors, respectively. One minute into induced ischemia, Pedra sensors had reached 50.5% of the 5-minute ischemia reading whereas TcPO2 sensors had reached only 18.6% of the 5-minute reading (P=0.046). Pedra sensors reported hyperemia immediately after cuff release with a mean percent change from baseline of 143.8±122.3%/173.4±121.8% on the dorsal/plantar surfaces while TcPO2 measurements were still recording negative changes at that time (-26.7±19.4%/-18.6±24.4% dorsal/plantar). Pedra sensors exhibited markedly lower interobserver and intraobserver variability than TcPO2 sensors., Conclusions: A device based on diffuse speckle contrast analysis reported tissue perfusion in real time. Cuff-induced ischemia and hyperemia following cuff release were rapidly and consistently detected on both the dorsal and plantar surfaces of the foot. Diffuse speckle contrast analysis may have value for real-time perfusion monitoring during angiography procedures.

Advances in medical imaging have enabled patient-specific biomechanical modelling of arterial lesions such as atherosclerosis and aneurysm. Geometry acquired from in-vivo imaging is already pressurized and a zero-pressure computational start shape needs to be identified. The backward displacement algorithm was proposed to solve this inverse problem, utilizing fixed-point iterations to gradually approach the start shape. However, classical fixed-point implementations were reported with suboptimal convergence properties under large deformations. In this paper, a dynamic learning rate guided by the deformation gradient tensor was introduced to control the geometry update. The effectiveness of this new algorithm was demonstrated for both idealized and patient-specific models. The proposed algorithm led to faster convergence by accelerating the initial steps and helped to avoid the non-convergence in large-deformation problems., (Copyright © 2021 Elsevier Ltd. All rights reserved.)