Optimization of CT scanning protocol of Type B aortic dissection follow-up through 3D printed model

This research aims to develop and evaluate a human tissue-like material 3D printed model used as a phantom in determining optimized scanning parameters to reduce the radiation dose for Type B aortic dissection patients after thoracic endovascular aortic repair. The results show that radiation risk for follow-up Type B aortic dissection patients can be potentially reduced. Further, the value of using 3D printed model in studying CT scanning protocols was further validated

DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans

Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult, besides increasing the scans quantity for the radiologists. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models reported in the literature. The novel TAA classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for development and test sets, respectively, using only the binary segmentation mask from DeepVox as input, without hand-engineered features. These two models together are a potential approach for TAA screening, as they can handle variable number of slices as input, handling thoracic and thoracoabdominal sequences, in a fully automated contrast- and dose-independent evaluation. This may assist to decrease TAA mortality and prioritize the evaluation queue of patients for radiologists.Comment: 23 pages, 4 figures, 7 table

Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification

Aortic dissection (AD) is a life-threatening cardiovascular disease with a high mortality rate. The accurate and generalized 3-D reconstruction of AD from CT-angiography can effectively assist clinical procedures and surgery plans, however, is clinically unavaliable due to the lacking of efficient tools. In this study, we presented a novel multi-stage segmentation framework for type B AD to extract true lumen (TL), false lumen (FL) and all branches (BR) as different classes. Two cascaded neural networks were used to segment the aortic trunk and branches and to separate the dual lumen, respectively. An aortic straightening method was designed based on the prior vascular anatomy of AD, simplifying the curved aortic shape before the second network. The straightening-based method achieved the mean Dice scores of 0.96, 0.95 and 0.89 for TL, FL, and BR on a multi-center dataset involving 120 patients, outperforming the end-to-end multi-class methods and the multi-stage methods without straightening on the dual-lumen segmentation, even using different network architectures. Both the global volumetric features of the aorta and the local characteristics of the primary tear could be better identified and quantified based on the straightening. Comparing to previous deep learning methods dealing with AD segmentations, the proposed framework presented advantages in segmentation accuracy

Diagnosis with ECG-gated MDCT of floating thrombus in aortic arch in a patient with type-A dissection

Multidetector computed tomography has been shown to be accurate in noninvasive assessment of chest vascular disease. The motion artifacts of the thoracic aorta and the supra-aortic vessels were significantly reduced in the electrocardiogram (ECG)-gated data acquisition. This positive effect of ECG synchronization is more pronounced in the region of the ascending aorta, aortic arch, and proximal descending aorta

Automatic Aorta Segmentation with Heavily Augmented, High-Resolution 3-D ResUNet: Contribution to the SEG.A Challenge

Automatic aorta segmentation from 3-D medical volumes is an important yet difficult task. Several factors make the problem challenging, e.g. the possibility of aortic dissection or the difficulty with segmenting and annotating the small branches. This work presents a contribution by the MedGIFT team to the SEG.A challenge organized during the MICCAI 2023 conference. We propose a fully automated algorithm based on deep encoder-decoder architecture. The main assumption behind our work is that data preprocessing and augmentation are much more important than the deep architecture, especially in low data regimes. Therefore, the solution is based on a variant of traditional convolutional U-Net. The proposed solution achieved a Dice score above 0.9 for all testing cases with the highest stability among all participants. The method scored 1st, 4th, and 3rd in terms of the clinical evaluation, quantitative results, and volumetric meshing quality, respectively. We freely release the source code, pretrained model, and provide access to the algorithm on the Grand-Challenge platform.Comment: MICCAI 2023 - SEG.A Challenge Contributio

The Importance of Imaging Assessment Before Endovascular Repair of Thoracic Aorta

AbstractIndications for and experience with placement of endovascular stent grafts in the thoracic aorta are still evolving. Recent advances in imaging technologies have drastically boosted the role of pre-procedural imaging. The accepted diagnostic gold standard, digital subtraction angiography, is now being challenged by the state-of-the-art computed tomography angiography (CTA), magnetic resonance angiography (MRA) and trans-oesophageal echocardiography (TEE). Among these, technological advancements of multidetector computed tomography (MDCT) have propelled it to being the default modality used, optimising the balance between spatial and temporal resolutions and invasiveness. MDCT angiography allows the comprehensive evaluation of thoracic lesions in terms of morphological features and extent, presence of thrombus, relationship with adjacent structures and branches as well as signs of impending or acute rupture, and is routinely used in these settings.In this article, we review the current state-of-the-art radiological imaging for thoracic endovascular aneurysm repair (TEVAR), especially focusing on the role of MDCT angiography. After analysing the technical aspects for optimised imaging protocols for thoracic aortic diseases, we discuss pre-procedural determinants of candidacy, and how to formulate interventional plans based on cross-sectional imaging

Advances in the diagnosis of acute aortic syndromes: Role of imaging techniques.

Aortic diseases include a wide range of pathological conditions: aortic aneurysms, pseudoaneurysms, acute aortic syndromes, atherosclerotic and inflammatory conditions, genetic diseases and congenital anomalies. Acute aortic syndromes have acute onset and may be life-threatening. They include aortic dissection, intramural haematoma, penetrating aortic ulcer and traumatic aortic injury. Pain is the common denominator to all acute aortic syndromes. Pain occurs regardless of age, gender and other associated clinical conditions. In this review, we deal with the main findings in the clinical setting and the most recent indications for diagnostic imaging, which are aimed to start an appropriate treatment and improve the short- and long-term prognosis of these patients. © The Author(s) 2016