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

Abstract

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.

Split-filter dual energy computed tomography radiotherapy: From calibration to image guidance

“…A limited number of studies investigate the clinical value of DECT-based delineations. A PubMed database search with the criteria “DECT 1 ” and “RT 1 “ and “delineation” resulted in only 12 publications, which mainly focused on optimal DECT recons in terms of maximum contrast or signal-to-noise ratios (CNR/SNR) or MAR considerations [12] , [18] , [19] , [20] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] . Tumors and OARs in the brain, HN, lung, liver and pancreas were investigated, and in general VMI < 60 keV had favorable CNR as compared to 120 kV SECT.…”

“…Different source-based DECT acquisitions exist such as performing two helical scans at two different energies (typically at 80 and 140 kV) [5] , two orthogonal sources simultaneously operating at different energies [6] , rapid (<0.5 ms) kV switching of a single source [7] , [8] , and splitting a single-source beam longitudinally using filtration [4] , [9] . The latter technique [10] , [11] , [12] , here termed TwinBeam (TB), is the focus of this study.…”

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

“…Multiple studies have demonstrated that VMI 40keV exhibits the highest contrast-to-noise ratio, which is advantageous for lesion detection. VMI 40keV maximizes the contrast of liver tumors, improves the image quality of multiphase abdominal enhancement scans, and enhances the detection of liver and pancreatic lacerations [ 13 , 14 ]. However, DECT is more expensive than conventional CT, which limits its widespread adoption.…”

A deep-learning method for generating synthetic kV-CT and improving tumor segmentation for helical tomotherapy of nasopharyngeal carcinoma

“…Combining kV-CT images with MV-CT images potentially can achieve dual-energy imaging. A number of studies have reported that utilizing features from dual-energy imaging can improve tumor recognition (Çamlıdağ et al 2019, DiMaso et al 2020, Shen et al 2021. However, the corresponding kV-CT images of the online MV-CT images are not available during each fraction of tomotherapy.…”