AIMC Topic: Tomography, X-Ray Computed

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High quality imaging from sparsely sampled computed tomography data with deep learning and wavelet transform in various domains.

Medical physics
PURPOSE: Sparsely sampled computed tomography (CT) has been attracting attention as a technique that can reduce the high radiation dose of conventional CT. In general, iterative reconstruction techniques have been applied to sparsely sampled CT to re...

Convolutional neural network evaluation of over-scanning in lung computed tomography.

Diagnostic and interventional imaging
INTRODUCTION: The purpose of this study was to develop a convolutional neural network (CNN) to determine the extent of over-scanning in the Z-direction associated with lung computed tomography (CT) examinations.

Metal artifact reduction on cervical CT images by deep residual learning.

Biomedical engineering online
BACKGROUND: Cervical cancer is the fifth most common cancer among women, which is the third leading cause of cancer death in women worldwide. Brachytherapy is the most effective treatment for cervical cancer. For brachytherapy, computed tomography (C...

Brain CT and MRI medical image fusion using convolutional neural networks and a dual-channel spiking cortical model.

Medical & biological engineering & computing
The aim of medical image fusion is to improve the clinical diagnosis accuracy, so the fused image is generated by preserving salient features and details of the source images. This paper designs a novel fusion scheme for CT and MRI medical images bas...

Statistical CT reconstruction using region-aware texture preserving regularization learning from prior normal-dose CT image.

Physics in medicine and biology
In some clinical applications, prior normal-dose CT (NdCT) images are available, and the valuable textures and structure features in them may be used to promote follow-up low-dose CT (LdCT) reconstruction. This study aims to learn texture information...

Robotically assisted long bone biopsy under MRI: cadaver study results.

International journal of computer assisted radiology and surgery
RATIONALE AND OBJECTIVES: We have designed and constructed an MR-safe robot made entirely of nonmetallic components with pneumatic actuators and optical encoders. The robot was developed to enable bone biopsies to be performed under magnetic resonanc...

Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm.

Journal of medical systems
Renal segmentation is one of the most fundamental and challenging task in computer aided diagnosis systems. In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic seg...

Deep Sequential Segmentation of Organs in Volumetric Medical Scans.

IEEE transactions on medical imaging
Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from...

Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis.

Medical image analysis
Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a thr...

Robot-assisted percutaneous placement of K-wires during minimally invasive interventions of the spine.

Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
To assess the accuracy and time requirements of image-guided percutaneous K-wire insertion in the spine using an advanced robot assistance device for needle guidance and to demonstrate a radiation-free workflow for the physician. A planning CT-scan...