Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

dc.contributor.author

Clark, Darin P

dc.contributor.author

Schwartz, Fides R

dc.contributor.author

Marin, Daniele

dc.contributor.author

Ramirez-Giraldo, Juan C

dc.contributor.author

Badea, Cristian T

dc.date.accessioned

2022-01-27T18:08:57Z

dc.date.available

2022-01-27T18:08:57Z

dc.date.issued

2020-09

dc.date.updated

2022-01-27T18:08:56Z

dc.description.abstract

Purpose

Data completion is commonly employed in dual-source, dual-energy computed tomography (CT) when physical or hardware constraints limit the field of view (FoV) covered by one of two imaging chains. Practically, dual-energy data completion is accomplished by estimating missing projection data based on the imaging chain with the full FoV and then by appropriately truncating the analytical reconstruction of the data with the smaller FoV. While this approach works well in many clinical applications, there are applications which would benefit from spectral contrast estimates over the larger FoV (spectral extrapolation)-e.g. model-based iterative reconstruction, contrast-enhanced abdominal imaging of large patients, interior tomography, and combined temporal and spectral imaging.

Methods

To document the fidelity of spectral extrapolation and to prototype a deep learning algorithm to perform it, we assembled a data set of 50 dual-source, dual-energy abdominal x-ray CT scans (acquired at Duke University Medical Center with 5 Siemens Flash scanners; chain A: 50 cm FoV, 100 kV; chain B: 33 cm FoV, 140 kV + Sn; helical pitch: 0.8). Data sets were reconstructed using ReconCT (v14.1, Siemens Healthineers): 768 × 768 pixels per slice, 50 cm FoV, 0.75 mm slice thickness, "Dual-Energy - WFBP" reconstruction mode with dual-source data completion. A hybrid architecture consisting of a learned piecewise linear transfer function (PLTF) and a convolutional neural network (CNN) was trained using 40 scans (five scans reserved for validation, five for testing). The PLTF learned to map chain A spectral contrast to chain B spectral contrast voxel-wise, performing an image domain analog of dual-source data completion with approximate spectral reweighting. The CNN with its U-net structure then learned to improve the accuracy of chain B contrast estimates by copying chain A structural information, by encoding prior chain A, chain B contrast relationships, and by generalizing feature-contrast associations. Training was supervised, using data from within the 33-cm chain B FoV to optimize and assess network performance.

Results

Extrapolation performance on the testing data confirmed our network's robustness and ability to generalize to unseen data from different patients, yielding maximum extrapolation errors of 26 HU following the PLTF and 7.5 HU following the CNN (averaged per target organ). Degradation of network performance when applied to a geometrically simple phantom confirmed our method's reliance on feature-contrast relationships in correctly inferring spectral contrast. Integrating our image domain spectral extrapolation network into a standard dual-source, dual-energy processing pipeline for Siemens Flash scanner data yielded spectral CT data with adequate fidelity for the generation of both 50 keV monochromatic images and material decomposition images over a 30-cm FoV for chain B when only 20 cm of chain B data were available for spectral extrapolation.

Conclusions

Even with a moderate amount of training data, deep learning methods are capable of robustly inferring spectral contrast from feature-contrast relationships in spectral CT data, leading to spectral extrapolation performance well beyond what may be expected at face value. Future work reconciling spectral extrapolation results with original projection data is expected to further improve results in outlying and pathological cases.
dc.identifier.issn

0094-2405

dc.identifier.issn

2473-4209

dc.identifier.uri

https://hdl.handle.net/10161/24254

dc.language

eng

dc.publisher

Wiley

dc.relation.ispartof

Medical physics

dc.relation.isversionof

10.1002/mp.14324

dc.subject

Humans

dc.subject

Tomography, X-Ray Computed

dc.subject

Phantoms, Imaging

dc.subject

Algorithms

dc.subject

X-Rays

dc.subject

Deep Learning

dc.title

Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography.

dc.type

Journal article

duke.contributor.orcid

Schwartz, Fides R|0000-0002-3598-7082

duke.contributor.orcid

Badea, Cristian T|0000-0002-1850-2522

pubs.begin-page

4150

pubs.end-page

4163

pubs.issue

9

pubs.organisational-group

Duke

pubs.organisational-group

Pratt School of Engineering

pubs.organisational-group

School of Medicine

pubs.organisational-group

Clinical Science Departments

pubs.organisational-group

Institutes and Centers

pubs.organisational-group

Biomedical Engineering

pubs.organisational-group

Radiology

pubs.organisational-group

Radiology, Abdominal Imaging

pubs.organisational-group

Duke Cancer Institute

pubs.publication-status

Published

pubs.volume

47

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Clark_DL_Extrapol_MedPhys_2020.pdf
Size:
3.66 MB
Format:
Adobe Portable Document Format
Description:
Published version