AIMC Topic: Phantoms, Imaging

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A geometry-guided deep learning technique for CBCT reconstruction.

Physics in medicine and biology
Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL techn...

Ultrasound Scatterer Density Classification Using Convolutional Neural Networks and Patch Statistics.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Quantitative ultrasound (QUS) can reveal crucial information on tissue properties, such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or underdevelop...

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

PloS one
Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity ...

Toward autonomous robotic prostate biopsy: a pilot study.

International journal of computer assisted radiology and surgery
PURPOSE: We present the validation of PROST, a robotic device for prostate biopsy. PROST is designed to minimize human error by introducing some autonomy in the execution of the key steps of the procedure, i.e., target selection, image fusion and nee...

Deep-Learning-Driven Full-Waveform Inversion for Ultrasound Breast Imaging.

Sensors (Basel, Switzerland)
Ultrasound breast imaging is a promising alternative to conventional mammography because it does not expose women to harmful ionising radiation and it can successfully image dense breast tissue. However, conventional ultrasound imaging only provides ...

DeepBeam: a machine learning framework for tuning the primary electron beam of the PRIMO Monte Carlo software.

Radiation oncology (London, England)
BACKGROUND: Any Monte Carlo simulation of dose delivery using medical accelerator-generated megavolt photon beams begins by simulating electrons of the primary electron beam interacting with a target. Because the electron beam characteristics of any ...

Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI.

NeuroImage
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the d...

A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy.

International journal of computer assisted radiology and surgery
PURPOSE: The purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the acc...

Artificial intelligence for quality assurance in radiotherapy.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In radiotherapy, patient-specific quality assurance is very time-consuming and causes machine downtime. It consists of testing (using measurement with a phantom and detector) if a modulated plan is correctly delivered by a treatment unit. Artificial ...

Independent verification of brachytherapy treatment plan by using deep learning inference modeling.

Physics in medicine and biology
This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodo...