Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study.
Authors
Affiliations (8)
Affiliations (8)
- Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA. [email protected].
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA. [email protected].
- Siemens Healthineers, Princeton, NJ, 08540, USA.
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA.
- Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA.
- Siemens Healthineers AG, Siemensstr. 3, 91301, Forchheim, Germany.
- Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, 20892, USA. [email protected].
- Siemens Healthineers, 19335, Malvern, PA, USA. [email protected].
Abstract
Conventional approaches to material decomposition in spectral CT face challenges related to precise algorithm calibration across imaged conditions and low signal quality caused by variable object size and reduced dose. In this proof-of-principle study, a deep learning approach to multi-material decomposition was developed to quantify iodine, gadolinium, and calcium in spectral CT. A dual-phase network architecture was trained using synthetic datasets containing computational models of cylindrical and virtual patient phantoms. Classification and quantification performance was evaluated across a range of patient size and dose parameters. The model was found to accurately classify (accuracy: cylinders - 98%, virtual patients - 97%) and quantify materials (mean absolute percentage difference: cylinders - 8-10%, virtual patients - 10-15%) in both datasets. Performance in virtual patient phantoms improved as the hybrid training dataset included a larger contingent of virtual patient phantoms (accuracy: 48% with 0 virtual patients to 97% with 8 virtual patients). For both datasets, the algorithm was able to maintain strong performance under challenging conditions of large patient size and reduced dose. This study shows the validity of a deep-learning based approach to multi-material decomposition trained with in-silico images that can overcome the limitations of conventional material decomposition approaches.