Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three-dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans.

A cone-beam X-ray computed tomography data collection designed for machine learning.

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning.

Tomographic approach for the quantitative scene reconstruction from light field images.

Current computational methods for light field photography model the ray-tracing geometry inside the plenoptic camera. This representation of the problem, and some common approximations, can lead to errors in the estimation of object sizes and positions. We propose a representation that leads to the correct reconstruction of object sizes and distances to the camera, by showing that light field images can be interpreted as limited angle cone-beam tomography acquisitions.

Random forests to predict tumor recurrence following cervical cancer therapy using pre- and per-treatment F-FDG PET parameters.

The ability to predict tumor recurrence after chemoradiotherapy of locally advanced cervical cancer is a crucial clinical issue to intensify the treatment of the most high-risk patients. The objective of this study was to investigate tumor metabolism characteristics extracted from pre- and per-treatment F-FDG PET images to predict 3-year overall recurrence (OR). A total of 53 locally advanced cervical cancer patients underwent pre- and per-treatment F-FDG PET (respectively PET1 and PET2).