A neural network-based method for spectral distortion correction in photon counting x-ray CT.
Abstract
Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for
measuring material composition based on energy dependent x-ray attenuation. Spectral
CT is especially suited for imaging with K-edge contrast agents to address the otherwise
limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD.
This system enables both 4 energy bins acquisition, as well as full-spectrum mode
in which the energy thresholds of the PCXD are swept to sample the full energy spectrum
for each detector element and projection angle. Measurements provided by the PCXD,
however, are distorted due to undesirable physical effects in the detector and can
be very noisy due to photon starvation in narrow energy bins. To address spectral
distortions, we propose and demonstrate a novel artificial neural network (ANN)-based
spectral distortion correction mechanism, which learns to undo the distortion in spectral
CT, resulting in improved material decomposition accuracy. To address noise, post-reconstruction
denoising based on bilateral filtration, which jointly enforces intensity gradient
sparsity between spectral samples, is used to further improve the robustness of ANN
training and material decomposition accuracy. Our ANN-based distortion correction
method is calibrated using 3D-printed phantoms and a model of our spectral CT system.
To enable realistic simulations and validation of our method, we first modeled the
spectral distortions using experimental data acquired from (109)Cd and (133)Ba radioactive
sources measured with our PCXD. Next, we trained an ANN to learn the relationship
between the distorted spectral CT projections and the ideal, distortion-free projections
in a calibration step. This required knowledge of the ground truth, distortion-free
spectral CT projections, which were obtained by simulating a spectral CT scan of the
digital version of a 3D-printed phantom. Once the training was completed, the trained
ANN was used to perform distortion correction on any subsequent scans of the same
system with the same parameters. We used joint bilateral filtration to perform noise
reduction by jointly enforcing intensity gradient sparsity between the reconstructed
images for each energy bin. Following reconstruction and denoising, the CT data was
spectrally decomposed using the photoelectric effect, Compton scattering, and a K-edge
material (i.e. iodine). The ANN-based distortion correction approach was tested using
both simulations and experimental data acquired in phantoms and a mouse with our PCXD-based
micro-CT system for 4 bins and full-spectrum acquisition modes. The iodine detectability
and decomposition accuracy were assessed using the contrast-to-noise ratio and relative
error in iodine concentration estimation metrics in images with and without distortion
correction. In simulation, the material decomposition accuracy in the reconstructed
data was vastly improved following distortion correction and denoising, with 50% and
20% reductions in material concentration measurement error in full-spectrum and 4
energy bins cases, respectively. Overall, experimental data confirms that full-spectrum
mode provides superior results to 4-energy mode when the distortion corrections are
applied. The material decomposition accuracy in the reconstructed data was vastly
improved following distortion correction and denoising, with as much as a 41% reduction
in material concentration measurement error for full-spectrum mode, while also bringing
the iodine detectability to 4-6 mg ml(-1). Distortion correction also improved the
4 bins mode data, but to a lesser extent. The results demonstrate the experimental
feasibility and potential advantages of ANN-based distortion correction and joint
bilateral filtration-based denoising for accurate K-edge imaging with a PCXD. Given
the computational efficiency with which the ANN can be applied to projection data,
the proposed scheme can be readily integrated into existing CT reconstruction pipelines.
Type
Journal articleSubject
AnimalsMice, Inbred C57BL
Mice
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Artifacts
Phantoms, Imaging
Algorithms
Neural Networks (Computer)
Photons
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https://hdl.handle.net/10161/16500Published Version (Please cite this version)
10.1088/0031-9155/61/16/6132Publication Info
Touch, Mengheng; Clark, Darin P; Barber, William; & Badea, Cristian T (2016). A neural network-based method for spectral distortion correction in photon counting
x-ray CT. Physics in medicine and biology, 61(16). 10.1088/0031-9155/61/16/6132. Retrieved from https://hdl.handle.net/10161/16500.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Cristian Tudorel Badea
Professor in Radiology
Our lab's research focus lies primarily in developing novel quantitative imaging systems,
reconstruction algorithms and analysis methods. My major expertise is in preclinical
CT.
Currently, we are particularly interested in developing novel strategies for spectral
CT imaging using nanoparticle-based contrast agents for theranostics (i.e. therapy
and diagnostics).
We are also engaged in developin
Darin Clark
Assistant Professor in Radiology
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