A Generalizable Framework for Kidney Stone Composition Characterization Using Dual-Energy CT.

Area of Science:

• Medical Imaging
• Radiology
• Biophysics

Background:

• Classifying non-uric acid (NUA) renal stones using dual-energy CT (DECT) is challenging due to similar CT number ratios (CTRs) and variability with patient size and acquisition protocols.
• Accurate stone characterization is crucial for effective patient management and treatment planning.

Purpose of the Study:

• To develop a generalizable framework for estimating correct CTR thresholds for different renal stone types, DECT protocols, and patient sizes.
• To validate the framework's accuracy on phantom inserts and human urinary stones using two DECT scanners.

Main Methods:

• The framework utilizes generic x-ray spectra and estimates added filtration to match half-value-layer (HVL) measurements.
• It predicts CTRs based on chemical composition and patient size, validated with calcium/iodine inserts and five types of human urinary stones in phantoms of varying sizes.
• Scans were performed at high dose using routine acquisition parameters on a dual-source DECT scanner.

Main Results:

• Predicted CTRs closely matched experimentally measured values for different stone types across phantom sizes (30, 40, 50 cm), with average absolute errors ranging from 1.8% to 2.8%.
• The framework demonstrated high accuracy, with predicted CTR errors for phantom inserts within 6.4%.

Conclusions:

• The developed framework effectively predicts renal stone CTRs for various compositions and patient sizes using readily available HVL measurements.
• Further refinement of this framework holds potential for improved clinical classification of NUA renal stone subtypes.