A rapid algebraic 3D volume image reconstruction technique for cone beam computed tomography

Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media

Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.

High-performance reconstruction of CT medical images by using out-of-core methods in GPU

BACKGROUND AND OBJECTIVE

Since Computed Tomography (CT) is one of the most widely used medical imaging tests, it is essential to work on methods that reduce the radiation the patient is exposed to. Although there are several possible approaches to achieve this, we focus on reducing the exposure time through sparse sampling. With this approach, efficient algebraic methods are needed to be able to generate the images in real time, and since their computational cost is high, using high-performance computing is essential.

METHODS

In this paper we present a GPU (Graphics Processing Unit) software for solving the CT image reconstruction problem using the QR factorization performed with out-of-core (OOC) techniques. This implementation is optimized to reduce the data transfer times between disk, CPU, and GPU, as well as to overlap input/output operations and computations.

RESULTS

The experimental study shows that a block cache stored on main page-locked memory is more efficient than using a cache on GPU memory or mirroring it in both GPU and CPU memory. Compared to a CPU version, this implementation is up to 6.5 times faster, providing an improved image quality when compared to other reconstruction methods.

CONCLUSIONS

The software developed is an optimized version of the QR factorization for GPU that allows the algebraic reconstruction of CT images with high quality and resolution, with a performance that can be compared with state-of-the-art methods used in clinical practice. This approach allows reducing the exposure time of the patient and thus the radiation dose.

Denoising images of dual energy X-ray absorptiometry using non-local means filters

BACKGROUND

In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis.

OBJECTIVE

This study aims to develop and test a new technology to improve the quality, remove the noise, and preserve the edges and fine details of real DXA images.

METHODS

In this study, a denoising technique for high and low energy DXA images using a non-local mean filter (NLM) was presented. The source and detector noises of a DXA system were modeled for both high and low DXA images. Then, the optimized parameters of the NLM filter were derived utilizing the experimental data from CIRS-BFP phantoms. After that, the optimized NLM was tested and verified using the DXA images of the phantoms and real human spine and femur.

RESULTS

Quantitative evaluation of the results showed average 24.22% and 34.43% improvement of the signal-to-noise ratio for real high and low spine images, respectively, while the improvements were about 15.26% and 13.55% for the high and low images of the femur. The qualitative visual observations of both phantom and real structures also showed significantly improved quality and reduced noise while preserving the edges in both high and low energy images. Our results demonstrate that the proposed NLM outperforms the conventional method using an anisotropic diffusion filter (ADF) and median techniques for all phantom and real human DXA images.

CONCLUSIONS

Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.