AIMC Topic: Tomography, X-Ray Computed

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SANet: A Slice-Aware Network for Pulmonary Nodule Detection.

IEEE transactions on pattern analysis and machine intelligence
Lung cancer is the most common cause of cancer death worldwide. A timely diagnosis of the pulmonary nodules makes it possible to detect lung cancer in the early stage, and thoracic computed tomography (CT) provides a convenient way to diagnose nodule...

LRFNet: A deep learning model for the assessment of liver reserve function based on Child-Pugh score and CT image.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Liver reserve function should be accurately evaluated in patients with hepatic cellular cancer before surgery to evaluate the degree of liver tolerance to surgical methods. Meanwhile, liver reserve function is also an import...

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement.

Radiology
Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their d...

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT.

International journal of computer assisted radiology and surgery
PURPOSE: Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized ...

Object recognition in medical images via anatomy-guided deep learning.

Medical image analysis
PURPOSE: Despite advances in deep learning, robust medical image segmentation in the presence of artifacts, pathology, and other imaging shortcomings has remained a challenge. In this paper, we demonstrate that by synergistically marrying the unmatch...

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.

European radiology
OBJECTIVES: To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).

Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

Medical physics
BACKGROUND: Recently, computed tomography (CT) manufacturers have developed deep-learning-based reconstruction algorithms to compensate for the limitations of iterative reconstruction (IR) algorithms, such as image smoothing and the spatial resolutio...