Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.
Katharina NiepelSebastian TattenbergRaanan MarantsGuyue HuThomas BortfeldJoost VerburgAtchar SudhyadhomGuillaume LandryKatia ParodiPublished in: Physics in medicine and biology (2024)
Objective. Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton range in-vivo . To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed. Approach. A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues. Results. The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous. Significance. Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute to in-vivo dose verification measurements.
Keyphrases
- dual energy
- computed tomography
- image quality
- gene expression
- monte carlo
- contrast enhanced
- positron emission tomography
- high density
- endothelial cells
- randomized controlled trial
- machine learning
- deep learning
- magnetic resonance
- optical coherence tomography
- case report
- systematic review
- risk assessment
- density functional theory
- pet ct
- liquid chromatography