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Radiation Dosage

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Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

Medical physics
PURPOSE: We are developing a computerized segmentation tool for the inner and outer bladder wall as a part of an image analysis pipeline for CT urography (CTU).

Ultra-Low-Dose Neck CT With Low-Dose Contrast Material for Preoperative Staging of Thyroid Cancer: Image Quality and Diagnostic Performance.

AJR. American journal of roentgenology
OBJECTIVE: Although CT has been used as a complementary diagnostic method for the preoperative diagnosis of thyroid cancer, it has the shortcomings of substantial radiation exposure and the use of contrast material (CM). The purpose of this article i...

Learning-based CBCT correction using alternating random forest based on auto-context model.

Medical physics
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and p...

3D Auto-Context-Based Locality Adaptive Multi-Modality GANs for PET Synthesis.

IEEE transactions on medical imaging
Positron emission tomography (PET) has been substantially used recently. To minimize the potential health risk caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality PET image from the low-dose on...

Automatic treatment planning based on three-dimensional dose distribution predicted from deep learning technique.

Medical physics
PURPOSE: To develop an automated treatment planning strategy for external beam intensity-modulated radiation therapy (IMRT), including a deep learning-based three-dimensional (3D) dose prediction and a dose distribution-based plan generation algorith...

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...

A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning.

Medical physics
PURPOSE: To develop a method for predicting optimal dose distributions, given the planning image and segmented anatomy, by applying deep learning techniques to a database of previously optimized and approved Intensity-modulated radiation therapy trea...

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...

The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.

European radiology
The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commer...

Iterative quality enhancement via residual-artifact learning networks for low-dose CT.

Physics in medicine and biology
Radiation exposure and the associated risk of cancer for patients in computed tomography (CT) scans have been major clinical concerns. The radiation exposure can be reduced effectively via lowering the x-ray tube current (mA). However, this strategy ...