AIMC Topic: Tomography, X-Ray Computed

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Radon Inversion via Deep Learning.

IEEE transactions on medical imaging
The Radon transform is widely used in physical and life sciences, and one of its major applications is in medical X-ray computed tomography (CT), which is significantly important in disease screening and diagnosis. In this paper, we propose a novel r...

Segmentation of organs-at-risk in cervical cancer CT images with a convolutional neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: We introduced and evaluated an end-to-end organs-at-risk (OARs) segmentation model that can provide accurate and consistent OARs segmentation results in much less time.

Deep learning based image reconstruction algorithm for limited-angle translational computed tomography.

PloS one
As a low-end computed tomography (CT) system, translational CT (TCT) is in urgent demand in developing countries. Under some circumstances, in order to reduce the scan time, decrease the X-ray radiation or scan long objects, furthermore, to avoid the...

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

IEEE journal of biomedical and health informatics
Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the het...

Automated CT registration tool improves sensitivity to change in ventricular volume in patients with shunts and drains.

The British journal of radiology
OBJECTIVE: CT is the mainstay imaging modality for assessing change in ventricular volume in patients with ventricular shunts or external ventricular drains (EVDs). We evaluated the performance of a novel fully automated CT registration and subtracti...

AI-based computer-aided diagnosis (AI-CAD): the latest review to read first.

Radiological physics and technology
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve...

Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Medical physics
PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

Medical physics
PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissue...

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

IEEE journal of biomedical and health informatics
Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphysema (diffuse pulmonary disease), it is very difficult for experts to allocate semantic labels to ever...

Use of natural language processing to improve predictive models for imaging utilization in children presenting to the emergency department.

BMC medical informatics and decision making
OBJECTIVE: To examine the association between the medical imaging utilization and information related to patients' socioeconomic, demographic and clinical factors during the patients' ED visits; and to develop predictive models using these associated...