Investigating a novel split‐filter dual‐energy <scp>CT</scp> technique for improving pancreas tumor visibility for radiation therapy

PurposeDual‐energy computed tomography (DECT) using TwinBeam CT (TBCT) is a new option for radiation oncology simulators. TBCT scanning provides virtual monoenergetic images which are attractive in treatment planning since lower energies offer better contrast for soft tissues, and higher energies reduce noise. A protocol is needed to achieve optimal performance of this feature. In this study, we investigated the TBCT scan schema with the head‐and‐neck radiotherapy workflow at our clinic and selected the optimal energy with best contrast‐noise‐ratio (CNR) in organs‐at‐risks (OARs) delineation for head‐and‐neck treatment planning.Methods and materialsWe synthesized monochromatic images from 40 keV to 190 keV at 5 keV increments from data acquired by TBCT. We collected the Hounsfield unit (HU) numbers of OARs (brainstem, mandible, spinal cord, and parotid glands), the HU numbers of marginal regions outside OARs, and the noise levels for each monochromatic image. We then calculated the CNR for the different OARs at each energy level to generate a serial of spectral curves for each OAR. Based on these spectral curves of CNR, the mono‐energy corresponding to the max CNR was identified for each OAR of each patient.ResultsComputed tomography scans of ten patients by TBCT were used to test the optimal monoenergetic image for the CNR of OAR. Based on the maximized CNR, the optimal energy values were 78.5 ± 5.3 keV for the brainstem, 78.0 ± 4.2 keV for the mandible, 78.5 ± 5.7 keV for the parotid glands, and 78.5 ± 5.3 keV for the spinal cord. Overall, the optimal energy for the maximum CNR of these OARs in head‐and‐neck cancer patients was 80 keV.ConclusionWe have proposed a clinically feasible protocol that selects the optimal energy level of the virtual monoenergetic image in TBCT for OAR delineation based on the CNR in head‐and‐neck OAR. This protocol can be applied in TBCT simulation.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Optimal virtual monoenergetic image in “TwinBeam” dual‐energy CT for organs‐at‐risk delineation based on contrast‐noise‐ratio in head‐and‐neck radiotherapy

Wang

Ghavidel

Beitler

et al. 2019

“…The image acquisition has been thoroughly described in a previous study investigating pancreas tumors. 10 For this study, all patients were imaged with a dual-phase imaging protocol. The arterial and portal venous phase scans were acquired using patient-specific delays based on automatic bolus tracking of the abdominal aorta.…”

Section: A | Patient Population and Ct Simulationmentioning

confidence: 99%

“…Much like a recent study that investigated the delineation of pancreas tumors using TwinBeam, this work investigates the gross target volume (GTV) contrast and contrast-to-noise ratio (CNR). 10 This work investigates several types of liver tumors, unlike recent DECT studies that have solely investigated hypo-or hypervascular liver tumors. 1,3,4,6,7 Liver metastases, hepatocellular…”

mentioning

confidence: 99%

Investigating split‐filter dual‐energy CT for improving liver tumor visibility for radiation therapy

DiMaso

Miller

Lawless

et al. 2020

Self Cite

Accurate liver tumor delineation is crucial for radiation therapy, but liver tumor volumes are difficult to visualize with conventional single-energy CT. This work investigates the use of split-filter dual-energy CT (DECT) for liver tumor visibility by quantifying contrast and contrast-to-noise ratio (CNR). Methods: Split-filter DECT contrast-enhanced scans of 20 liver tumors including cholangiocarcinomas, hepatocellular carcinomas, and liver metastases were acquired. Analysis was performed on the arterial and venous phases of mixed 120 kVp-equivalent images and VMIs at 57 keV and 40 keV gross target volume (GTV) contrast and CNR were calculated. Results: For the arterial phase, liver GTV contrast was 12.1 ± 10.0 HU and 43.1 ± 32.3 HU (P < 0.001) for the mixed images and 40 keV VMIs. Image noise increased on average by 116% for the 40 keV VMIs compared to the mixed images. The average CNR did not change significantly (1.6 ± 1.5, 1.7 ± 1.4, 2.4 ± 1.7 for the mixed, 57 keV and 40 keV VMIs (P > 0.141)). For individual cases, however, CNR increases of up to 607% were measured for the 40 keV VMIs compared to the mixed image. Venous phase 40 keV VMIs demonstrated an average increase of 35.4 HU in GTV contrast and 121% increase in image noise. Average CNR values were also not statistically different, but for individual cases CNR increases of up to 554% were measured for the 40 keV VMIs compared to the mixed image. Conclusions: Liver tumor contrast was significantly improved using split-filter DECT 40 keV VMIs compared to mixed images. On average, there was no statistical difference in CNR between the mixed images and VMIs, but for individual cases, CNR was greatly increased for the 57 keV and 40 keV VMIs. Therefore, although not universally successful for our patient cohort, split-filter DECT VMIs may provide substantial gains in tumor visibility of certain liver cases for radiation therapy treatment planning.

“…Dual‐energy computed tomography (DECT) imaging uses attenuation information acquired with two different energy spectra; the high and low energy data, along with the known attenuation properties of basis materials, can be processed in order to obtain quantitative material information (e.g., iodine and water material images) 1 . DECT imaging has many applications in both diagnostic imaging and radiation therapy, including material differentiation, contrast enhancement, 2 virtual noncontrast 3 and virtual monoenergetic imaging, perfusion imaging, 4 and metal artifact reduction 5 …”

Section: Introductionmentioning

confidence: 99%

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

Huang

Lawless

Matrosic

et al. 2020

Self Cite

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.