Search

Coronary artery disease (CAD) affects over 200 million individuals globally, accounting for approximately 9 million deaths annually. Patients living with diabetes mellitus exhibit an up to fourfold increased risk of developing CAD compared to individuals without diabetes. Furthermore, CAD is responsible for 40 to 80 percent of the observed mortality rates among patients with type 2 diabetes. Patients with diabetes typically present with non-specific clinical complaints in the setting of myocardial ischemia, and as such, it is critical to select appropriate diagnostic tests to identify those at risk for major adverse cardiac events (MACEs) and for determining optimal management strategies. Studies indicate that patients with diabetes often exhibit more advanced atherosclerosis, a higher calcified plaque burden, and smaller epicardial vessels. The diagnostic performance of coronary computed tomographic angiography (CCTA) in identifying significant stenosis is well-established, and as such, CCTA has been incorporated into current clinical guidelines. However, the predictive accuracy of obstructive CAD in patients with diabetes has been less extensively characterized. CCTA provides detailed insights into coronary anatomy, plaque burden, epicardial vessel stenosis, high-risk plaque features, and other features associated with a higher incidence of MACEs. Recent evidence supports the efficacy of CCTA in diagnosing CAD and improving patient outcomes, leading to its recommendation as a primary diagnostic tool for stable angina and risk stratification. However, its specific benefits in patients with diabetes require further elucidation. This review examines several key aspects of the utility of CCTA in patients with diabetes: (i) the diagnostic accuracy of CCTA in detecting obstructive CAD, (ii) the effect of CCTA as a first-line test for individualized risk stratification for cardiovascular outcomes, (iii) its role in guiding therapeutic management, and (iv) future perspectives in risk stratification and the role of artificial intelligence. [ABSTRACT FROM AUTHOR]

Background: Recent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity. Purpose: In light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self‐supervised approach, for multiple chest‐related abnormality identification including low lung density area ("LLDA"), consolidation ("CONS"), nodules ("NOD") and interstitial pattern ("IP"). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3D localization. Methods: We introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest‐related abnormalities: "LLDA", "CONS", "NOD", "IP" and "NORMAL". This method is based on a 3D patch‐based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine‐tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine‐tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi‐label patient‐level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine‐tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine‐tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitative evaluation. Results: Validated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi‐abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], p$ p$‐value < 0.001) and 0.963 (95% CI: [0.952, 0.976], p$ p$‐value < 0.001) in the multi‐label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non‐pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against false positives. Conclusions: The proposed method integrates self‐supervised learning algorithms for pretraining, utilizes a patch‐based approach for 3D pathology localization and develops an aggregation method for multi‐label prediction at patient‐level. It shows promise in efficiently detecting and localizing multiple anomalies within a single scan. [ABSTRACT FROM AUTHOR]

3D‐printed biomaterial implants are revolutionizing personalized medicine for tissue repair, especially in orthopedics. In this study, a radiopaque bismuth oxide (Bi2O3) doped polycaprolactone (PCL) composite is developed and implemented to enable the use of diagnostic X‐ray technologies, especially spectral photon counting X‐ray computed tomography (SPCCT), for comprehensive tissue engineering scaffold (TES) monitoring. PCL filament with homogeneous Bi2O3 nanoparticle (NP) dispersion (0.8 to 11.7 wt%) is first fabricated. TES are then 3D printed with the composite filament, optimizing printing parameters for small features and severely overhung geometries. These composite TES are characterized via micro‐computed tomography (µCT), tensile testing, and a cytocompatibility study, with 2 wt% Bi2O3 NPs providing improved tensile properties, equivalent cytocompatibility to neat PCL, and excellent radiographic distinguishability. Radiographic performance is validated in situ by imaging 4 and 7 wt% Bi2O3 doped PCL TES in a mouse model with µCT, showing excellent agreement with in vitro measurements. Subsequently, CT image‐derived swine menisci are 3D printed with composite filament and re‐implanted in corresponding swine legs ex vivo. Re‐imaging the swine legs via clinical CT allows facile identification of device location and alignment. Finally, the emergent technology of SPCCT unambiguously distinguishes the implanted meniscus in situ via color K‐edge imaging. [ABSTRACT FROM AUTHOR]

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD). Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score. Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic. Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images. [ABSTRACT FROM AUTHOR]