AIMC Topic: Tomography, X-Ray Computed

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Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging.

Acta radiologica (Stockholm, Sweden : 1987)
This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging to enhance diagnostic accuracy, improve image quality, and reduce radiation exposure. The use of phot...

Review of the targeting accuracy of frameless and frame-based robot-assisted deep brain stimulation electrode implantation in pediatric patients using the Neurolocate module.

Journal of neurosurgery. Pediatrics
OBJECTIVE: The Neurolocate module is a 3D frameless patient registration module that is designed for use with the Neuromate stereotactic robot. Long-term electrical stimulation of the globus pallidus internus (GPi) and subthalamic nucleus (STN) via d...

CT-based deep learning model for predicting hospital discharge outcome in spontaneous intracerebral hemorrhage.

European radiology
OBJECTIVES: To predict the functional outcome of patients with intracerebral hemorrhage (ICH) using deep learning models based on computed tomography (CT) images.

Pediatric evaluations for deep learning CT denoising.

Medical physics
BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL deno...

Evaluating the Hounsfield unit assignment and dose differences between CT-based standard and deep learning-based synthetic CT images for MRI-only radiation therapy of the head and neck.

Journal of applied clinical medical physics
BACKGROUND: Magnetic resonance image only (MRI-only) simulation for head and neck (H&N) radiotherapy (RT) could allow for single-image modality planning with excellent soft tissue contrast. In the MRI-only simulation workflow, synthetic computed tomo...

COVID-19 infection segmentation using hybrid deep learning and image processing techniques.

Scientific reports
The coronavirus disease 2019 (COVID-19) epidemic has become a worldwide problem that continues to affect people's lives daily, and the early diagnosis of COVID-19 has a critical importance on the treatment of infected patients for medical and healthc...

Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model.

European journal of radiology
PURPOSE: Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative ...

Robot-Assisted Minimally Invasive Multivessel Coronary Bypass Guided by Computerized Tomography.

Innovations (Philadelphia, Pa.)
OBJECTIVE: Robot-assisted minimally invasive coronary bypass surgery is one of the least invasive approaches that offers multivessel revascularization and accelerated recovery. We investigated the benefits of computed tomography angiography (CTA) gui...