Investigating a novel split-filter dual-energy CT technique for improving pancreas tumor visibility for radiation therapy

Purpose: Tumor delineation using conventional CT images can be a challenge for pancreatic adenocarcinoma where contrast between the tumor and surrounding healthy tissue is low. This work investigates the ability of a split-filter dual-energy CT (DECT) system to improve pancreatic tumor contrast and contrast-to-noise ratio (CNR) for radiation therapy treatment planning.

Materials and methods: Multiphasic scans of 20 pancreatic tumors were acquired using a split-filter DECT technique with iodinated contrast medium, OMNIPAQUE^TM . Analysis was performed on the pancreatic and portal venous phases for several types of DECT images. Pancreatic gross target volume (GTV) contrast and CNR were calculated and analyzed from mixed 120 kVp-equivalent images and virtual monoenergetic images (VMI) at 57 and 40 keV. The role of iterative reconstruction on DECT images was also investigated. Paired t-tests were used to assess the difference in GTV contrast and CNR among the different images.

Results: The VMIs at 40 keV had a 110% greater image noise compared to the mixed 120 kVp-equivalent images (P < 0.0001). VMIs at 40 keV increased GTV contrast from 15.9 ± 19.9 HU to 93.7 ± 49.6 HU and CNR from 1.37 ± 2.05 to 3.86 ± 2.78 in comparison to the mixed 120 kVp-equivalent images. The iterative reconstruction algorithm investigated decreased noise in the VMIs by about 20% and improved CNR by about 30%.

Conclusions: Pancreatic tumor contrast and CNR were significantly improved using VMIs reconstructed from the split-filter DECT technique, and the use of iterative reconstruction further improved CNR. This gain in tumor contrast may lead to more accurate tumor delineation for radiation therapy treatment planning.