AIMC Topic: Tomography, X-Ray Computed

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Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and hea...

Traumatic rib fracture patterns associated with bone mineral density statuses derived from CT images.

Frontiers in endocrinology
BACKGROUND: The impact of decreased bone mineral density (BMD) on traumatic rib fractures remains unknown. We combined computed tomography (CT) and artificial intelligence (AI) to measure BMD and explore its impact on traumatic rib fractures and thei...

Uncertainty-guided cross learning via CNN and transformer for semi-supervised honeycomb lung lesion segmentation.

Physics in medicine and biology
. Deep learning networks such as convolutional neural networks (CNN) and Transformer have shown excellent performance on the task of medical image segmentation, however, the usual problem with medical images is the lack of large-scale, high-quality p...

Learnable PM diffusion coefficients and reformative coordinate attention network for low dose CT denoising.

Physics in medicine and biology
Various deep learning methods have recently been used for low dose CT (LDCT) denoising. Aggressive denoising may destroy the edge and fine anatomical structures of CT images. Therefore a key issue in LDCT denoising tasks is the difficulty of balancin...

Deep-Interior: A new pathway to interior tomographic image reconstruction via a weighted backprojection and deep learning.

Medical physics
BACKGROUND: In recent years, deep learning strategies have been combined with either the filtered backprojection or iterative methods or the direct projection-to-image by deep learning only to reconstruct images. Some of these methods can be applied ...

Imaging in interventional oncology, the better you see, the better you treat.

Journal of medical imaging and radiation oncology
Imaging and image processing is the fundamental pillar of interventional oncology in which diagnostic, procedure planning, treatment and follow-up are sustained. Knowing all the possibilities that the different image modalities can offer is capital t...

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis.

PloS one
BACKGROUND: Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.

Enhancing Bone Cancer Diagnosis Through Image Extraction and Machine Learning: A State-of-the-Art Approach.

Surgical innovation
Bone cancer is a severe condition often leading to patient mortality. Diagnosis relies on X-rays, MRIs, or CT scans, which require time-consuming manual review by experts. Thus, developing an automated system is crucial for accurate classification o...

Deep learning-based segmentation of multisite disease in ovarian cancer.

European radiology experimental
PURPOSE: To determine if pelvic/ovarian and omental lesions of ovarian cancer can be reliably segmented on computed tomography (CT) using fully automated deep learning-based methods.

Research on augmented reality navigation of in vitro fenestration of stent-graft based on deep learning and virtual-real registration.

Computer assisted surgery (Abingdon, England)
OBJECTIVES: fenestration of stent-graft (IVFS) demands high-precision navigation methods to achieve optimal surgical outcomes. This study aims to propose an augmented reality (AR) navigation method for IVFS, which can provide overlay display to loc...