# Development of a deep learning based approach for multi-material decomposition in spectral CT: a proof of principle in silico study

## TL;DR

This study introduces a deep learning method for accurately identifying and measuring multiple materials in spectral CT scans using simulated data.

## Contribution

A novel deep learning approach for multi-material decomposition in spectral CT is developed and validated using in silico datasets.

## Key findings

- The model accurately classified and quantified iodine, gadolinium, and calcium in synthetic datasets.
- Performance improved with increased inclusion of virtual patient phantoms in training.
- The algorithm maintained strong performance under challenging imaging conditions like large patient size and reduced dose.

## 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.