Evaluation of a commercial deformable image registration algorithm for dual-energy CT processing.

PURPOSE: Several dual-energy computed tomography (DECT) techniques require a deformable image registration to correct for motion between the acquisition of low and high energy data. However, current DECT software does not provide tools to assess registration accuracy or allow the user to export deformed images, presenting a unique challenge for image registration quality assurance (QA). This work presents a methodology to evaluate the accuracy of DECT deformable registration and to quantify the impact of registration errors on end-product images. METHODS: The deformable algorithm implemented in Siemen Healthineers's Syngo was evaluated using a deformable abdomen phantom and a rigid phantom to mimic sliding motion in the thorax. Both phantoms were imaged using sequential 80 and 140 kVp scans with motion applied between the two scans. Since Syngo does not allow the export of the deformed images, this study focused on quantifying the accuracy of various end-product, dual-energy images resulting from processing of deformed images. RESULTS: The Syngo algorithm performed well for the abdomen phantom with a mean registration error of 0.4 mm for landmark analysis, Dice similarity coefficients (DSCs) > 0.90 for five organs contoured, and mean iodine concentrations within 0.2 mg/mL of values measured on static images. For rigid sliding motion, the algorithm performed poorer and resulted in noticeable registration errors toward the superior and inferior scan extents and DSCs as low as 0.41 for iodine rods imaged in the phantom. Additionally, local iodine concentration errors in areas of misregistration exceeded 3 mg/mL. CONCLUSIONS: This work represents the first methodology for DECT image registration QA using commercial software. Our data support the clinical use of the Syngo algorithm for abdominal sites with limited motion (i.e., pancreas and liver). However, dual-energy images generated with this algorithm should be used with caution for quantitative measurements in areas with sliding motion.

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, OMNIPAQUETM . 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.