Dual-Energy Computed Tomography in Stroke Imaging : Value of a New Image Acquisition Technique for Ischemia Detection after Mechanical Thrombectomy.

OBJECTIVE: To assess if a new dual-energy computed tomography (DECT) technique enables an improved visualization of ischemic brain tissue after mechanical thrombectomy in acute stroke patients. MATERIAL AND METHODS: The DECT head scans with a new sequential technique (TwinSpiral DECT) were performed in 41 patients with ischemic stroke after endovascular thrombectomy and were retrospectively included. Standard mixed and virtual non-contrast (VNC) images were reconstructed. Infarct visibility and image noise were assessed qualitatively by two readers using a 4-point Likert scale. Quantitative Hounsfield units (HU) were used to assess density differences of ischemic brain tissue versus healthy tissue on the non-affected contralateral hemisphere. RESULTS: Infarct visibility was significantly better in VNC compared to mixed images for both readers R1 (VNC: median 1 (range 1-3), mixed: median 2 (range 1-4), p < 0.05) and R2 (VNC: median 2 (range 1-3), mixed: 2 (range 1-4), p < 0.05). Qualitative image noise was significantly higher in VNC compared to mixed images for both readers R1 (VNC: median 3, mixed: 2) and R2 (VNC: median 2, mixed: 1, p < 0.05, each). Mean HU were significantly different between the infarcted tissue and the reference healthy brain tissue on the contralateral hemisphere in VNC (infarct 24 ± 3) and mixed images (infarct 33 ± 5, p < 0.05, each). The mean HU difference between ischemia and reference in VNC images (mean 8 ± 3) was significantly higher (p < 0.05) compared to the mean HU difference in mixed images (mean 5 ± 4). CONCLUSION: TwinSpiral DECT allows an improved qualitative and quantitative visualization of ischemic brain tissue in ischemic stroke patients after endovascular treatment.

Deep learning based detection of intracranial aneurysms on digital subtraction angiography: A feasibility study.

BACKGROUND: Digital subtraction angiography is the gold standard for detecting and characterising aneurysms. Here, we assess the feasibility of commercial-grade deep learning software for the detection of intracranial aneurysms on whole-brain anteroposterior and lateral 2D digital subtraction angiography images. MATERIAL AND METHODS: Seven hundred and six digital subtraction angiography images were included from a cohort of 240 patients (157 female, mean age 59 years, range 20-92; 83 male, mean age 55 years, range 19-83). Three hundred and thirty-five (47%) single frame anteroposterior and lateral images of a digital subtraction angiography series of 187 aneurysms (41 ruptured, 146 unruptured; average size 7±5.3 mm, range 1-5 mm; total 372 depicted aneurysms) and 371 (53%) aneurysm-negative study images were retrospectively analysed regarding the presence of intracranial aneurysms. The 2D data was split into testing and training sets in a ratio of 4:1 with 3D rotational digital subtraction angiography as gold standard. Supervised deep learning was performed using commercial-grade machine learning software (Cognex, ViDi Suite 2.0). Monte Carlo cross validation was performed. RESULTS: Intracranial aneurysms were detected with a sensitivity of 79%, a specificity of 79%, a precision of 0.75, a F1 score of 0.77, and a mean area-under-the-curve of 0.76 (range 0.68-0.86) after Monte Carlo cross-validation, run 45 times. CONCLUSION: The commercial-grade deep learning software allows for detection of intracranial aneurysms on whole-brain, 2D anteroposterior and lateral digital subtraction angiography images, with results being comparable to more specifically engineered deep learning techniques.

3D-cinematic rendering for dental and maxillofacial imaging.

OBJECTIVES: Aim of this technical note is to show the applicability of cinematic rendering (CR) for a photorealistic 3-dimensional (3D) visualization of maxillofacial structures. The focus is on maxillofacial hard tissue pathologies. METHODS: High density maxillofacial pathologies were selected in which CR is applicable. Data from both, CT and cone beam CT (CBCT) were postprocessed using a prototype CR software. RESULTS: CR 3D postprocessing of CT and CBCT imaging data is applicable on high density structures and pathologies such as bones, teeth, and tissue calcifications. Image reconstruction allows for a detailed visualization of surface structures, their plasticity, and 3D configuration. CONCLUSIONS: CR allows for the generation of photorealistic 3D reconstructions of high density structures and pathologies. Potential applications for maxillofacial bone and tooth imaging are given and examples for CT and CBCT images are displayed.