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Phantoms, Imaging

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Image denoising by transfer learning of generative adversarial network for dental CT.

Biomedical physics & engineering express
The successful development of the image denoising techniques for low-dose computed tomography (LDCT) was largely owing to the public-domain availability of spatially-aligned high- and low-dose CT image pairs. Even though low-dose CT scans are also hi...

Whole-body voxel-based internal dosimetry using deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: In the era of precision medicine, patient-specific dose calculation using Monte Carlo (MC) simulations is deemed the gold standard technique for risk-benefit analysis of radiation hazards and correlation with patient outcome. Hence, we propo...

Deep learning segmentation of general interventional tools in two-dimensional ultrasound images.

Medical physics
PURPOSE: Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultraso...

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance.

International journal of computer assisted radiology and surgery
PURPOSE: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides ...

Deep learning-based fetoscopic mosaicking for field-of-view expansion.

International journal of computer assisted radiology and surgery
PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the bl...

Comparison of CBCT-based dose calculation methods in head and neck cancer radiotherapy: from Hounsfield unit to density calibration curve to deep learning.

Medical physics
PURPOSE: Anatomical variations occur during head and neck (H&N) radiotherapy treatment. kV cone-beam computed tomography (CBCT) images can be used for daily dose monitoring to assess dose variations owing to anatomic changes. Deep learning methods (D...

Artifact removal using a hybrid-domain convolutional neural network for limited-angle computed tomography imaging.

Physics in medicine and biology
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not successful due to incomplete projection data. Moreover, m...

Automatic robot-world calibration in an optical-navigated surgical robot system and its application for oral implant placement.

International journal of computer assisted radiology and surgery
PURPOSE: Robot-world calibration, used to precisely determine the spatial relation between optical tracker and robot, is regarded as an essential step for optical-navigated surgical robot system to improve the surgical accuracy. However, these method...

Improved myocardial perfusion PET imaging using artificial neural networks.

Physics in medicine and biology
Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information...

A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder.

Physics in medicine and biology
The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially...