AIMC Topic: Tomography, X-Ray Computed

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Modern Approaches for Thoracic Image Registration and Respiratory Motion Management in Oncology.

Radiology. Imaging cancer
This review explores modern image registration techniques in the context of managing respiratory-induced motion in the thoracic region. The respiratory cycle introduces anatomic variability that poses challenges for radiation therapy, breathing dynam...

Deep Learning Can be Used to Classify the Disease Status of the Canine Middle Ear From Computed Tomographic Images.

Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Middle ear disease occurs frequently in dogs. CT has proven to be an excellent diagnostic tool for detecting middle ear structures, helping to achieve rapid and accurate diagnoses. Deep learning techniques are now widely used in CT scan-based human m...

Single Inspiratory Chest CT-based Generative Deep Learning Models to Evaluate Functional Small Airways Disease.

Radiology. Artificial intelligence
Purpose To develop a deep learning model that uses a single inspiratory chest CT scan to perform parametric response mapping (PRM) and predict functional small airways disease (fSAD). Materials and Methods In this retrospective study, predictive and ...

A Deep Learning Model for Comprehensive Automated Bone Lesion Detection and Classification on Staging Computed Tomography Scans.

Academic radiology
RATIONALE AND OBJECTIVES: A common site of metastases for a variety of cancers is the bone, which is challenging and time consuming to review and important for cancer staging. Here, we developed a deep learning approach for detection and classificati...

Automated instance segmentation and registration of spinal vertebrae from CT-Scans with an improved 3D U-net neural network and corner point registration.

Computers in biology and medicine
This paper presents a rapid and robust approach for 3D volumetric segmentation, labelling, and registration of human spinal vertebrae from CT scans using an optimised and improved 3D U-Net neural network architecture. The network is designed by incor...

CT-Mamba: A hybrid convolutional State Space Model for low-dose CT denoising.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-r...

Patient radiation safety in the intensive care unit.

The British journal of radiology
The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiological practices to limit radiation exposure in intensive care unit (ICU) patients. Radiological imaging...

AI-based CT assessment of sarcopenia in borderline resectable pancreatic Cancer: A narrative review of clinical and technical perspectives.

Computers in biology and medicine
BACKGROUND: Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with pancreatic cancer, particularly those with borderline resectable pancreatic cancer (BRPC). Although...

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.

Radiological physics and technology
Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, including complex structures such as the Valsalva sinus. This study evaluates a computed tomography (CT)-gui...

Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images.

Computer methods and programs in biomedicine
UNLABELLED: Applying HR-pQCT to image the knee necessitates the development and validation of novel image analysis workflows. Here, we present and validate the first automated workflow for in vivo quantitative assessment of peri-articular bone densit...