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Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector

Publication ,  Conference
Holbrook, MD; Clark, DP; Badea, CT
Published in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
January 1, 2021

While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ∼2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD. While the benefits of photon counting (PC) CT are significant, the performance of current PCDs is limited by physical effects distorting the spectral response. In this work, we examine a deep learning (DL) approach for spectral correction and material decomposition of PCCT data using multi-energy CT data sets acquired with an energy integrating detector (EID) for spectral calibration. We use a convolutional neural network (CNN) with a U-net structure and compare image domain and projection domain approaches to spectral correction and material decomposition. We trained our networks using noisy, spectrally distorted PCD projections as input data while the labels were derived from multi-energy EID data. For this study, we have scanned: 1) phantoms containing vials of iodine and calcium in water and 2) mice injected with an iodine-based liposomal contrast agent. Our results show that the image-based approach corrects best for spectral distortions and provides the lowest errors in material maps (RMSE in iodine: 0.34 mg/mL) compared with the projection-based approach (0.74 mg/mL) and image domain decomposition without correction (2.67 mg/mL). Both DL methods are, however, affected by a loss of spatial resolution (from 3 lp/mm in EID labels to ∼2.2 lp/mm in corrected reconstructions). In summary, we demonstrate that multi-energy EID acquisitions can provide training data for DL-based spectral distortion correction. This data-driven correction does not require intimate knowledge of the spectral response or spectral distortions of the PCD.

Duke Scholars

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

Publication Date

January 1, 2021

Volume

11595
 

Citation

APA
Chicago
ICMJE
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Holbrook, M. D., Clark, D. P., & Badea, C. T. (2021). Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 11595). https://doi.org/10.1117/12.2581124
Holbrook, M. D., D. P. Clark, and C. T. Badea. “Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector.” In Progress in Biomedical Optics and Imaging - Proceedings of SPIE, Vol. 11595, 2021. https://doi.org/10.1117/12.2581124.
Holbrook MD, Clark DP, Badea CT. Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector. In: Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.
Holbrook, M. D., et al. “Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector.” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 11595, 2021. Scopus, doi:10.1117/12.2581124.
Holbrook MD, Clark DP, Badea CT. Deep learning based spectral distortion correction and decomposition for photon counting ct using calibration provided by an energy integrated detector. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. 2021.

Published In

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

DOI

ISSN

1605-7422

Publication Date

January 1, 2021

Volume

11595