Clinical implementation of contrast-enhanced four-dimensional dual-energy computed tomography for target delineation of pancreatic cancer

Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70 keV (VMI70) and 140 keV (VMI140) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI70 and VMI140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

Dual-energy computed tomography-based iodine concentration as a predictor of histopathological response to preoperative chemoradiotherapy for pancreatic cancer

To explore predictors of the histopathological response to preoperative chemoradiotherapy (CRT) in patients with pancreatic cancer (PC) using dual-energy computed tomography-reconstructed images. This retrospective study divided 40 patients who had undergone preoperative CRT (50-60 Gy in 25 fractions) followed by surgical resection into two groups: the response group (Grades II, III and IV, evaluated from surgical specimens) and the nonresponse group (Grades Ia and Ib). The computed tomography number [in Hounsfield units (HUs)] and iodine concentration (IC) were measured at the locations of the aorta, PC and pancreatic parenchyma (PP) in the contrast-enhanced 4D dual-energy computed tomography images. Logistic regression analysis was performed to identify predictors of histopathological response. Univariate analysis did not reveal a significant relation between any parameter and patient characteristics or dosimetric parameters of the treatment plan. The HU and IC values in PP and the differences in HU and IC between the PP and PC (ΔHU and ΔIC, respectively) were significant predictors for distinguishing the response (n = 24) and nonresponse (n = 16) groups (P < 0.05). The IC in PP and ΔIC had a higher area under curve values [0.797 (95% confidence interval, 0.659-0.935) and 0.789 (0.650-0.928), respectively] than HU in PP and ΔHU [0.734 (0.580-0.889) and 0.721 (0.562-0.881), respectively]. The IC value could potentially be used for predicting the histopathological response in patients who have undergone preoperative CRT.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Dose reduction of hippocampus using HyperArc planning in postoperative radiotherapy for primary brain tumors

To compare dosimetric parameters for the hippocampus, organs at risk (OARs), and targets of volumetric modulated arc therapy (VMAT), noncoplanar VMAT (NC-VMAT), and HyperArc (HA) plans in patients undergoing postoperative radiotherapy for primary brain tumors. For 20 patients, HA plans were generated to deliver 40.05 to 60 Gy for the planning target volume (PTV). In addition, doses for the hippocampus and OARs were minimized. The VMAT and NC-VMAT plans were retrospectively generated using the same optimization parameters as those in the HA plans. For the hippocampus, the equivalent dose to be administered in 2 Gy fractions (EQD₂) was calculated assuming α/β = 2. Dosimetric parameters for the PTV, hippocampus, and OARs in the VMAT, NC-VMAT, and HA plans were compared. For PTV, the HA plans provided significantly lower D_max and D_1% than the VMAT and NC-VMAT plans (p < 0.05), whereas the D_99% and D_min were significantly higher (p < 0.05). For the contralateral hippocampus, the dosimetric parameters in the HA plans (8.1 ± 9.6, 6.5 ± 7.2, 5.6 ± 5.8, and 4.8 ± 4.7 Gy for D_20%, D_40%, D_60% and D_80%, respectively) were significantly smaller (p < 0.05) than those in the VMAT and NC-VMAT plans. Except for the optic chiasm, the D_max in the HA plans (brainstem, lens, optic nerves, and retinas) was the smallest (p < 0.05). In addition, the doses in the HA plans for the brain and skin were the smallest (p < 0.05) among the 3 plans. HA planning, instead of coplanar and noncoplanar VMAT, significantly reduces the dosage to which the contralateral hippocampus as well as other OARs are exposed without compromising on target coverage.

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography

PURPOSE

To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality.

MATERIALS AND METHODS

Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60 keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60 keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model.

RESULTS

The contrast-to-noise ratio was significantly improved (p < 0.001) in the generated VMIs (4.1 ± 1.8) compared to the SECT images (2.8 ± 1.1). The mean overall image quality (4.1 ± 0.6) and tumor enhancement (3.6 ± 0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p < 0.001) than that in the SECT images (3.2 ± 0.4 and 2.8 ± 0.4 for overall image quality and tumor enhancement, respectively).

CONCLUSIONS

The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.

A Third-Generation Adaptive Statistical Iterative Reconstruction for Contrast-Enhanced 4-Dimensional Dual-Energy Computed Tomography for Pancreatic Cancer

OBJECTIVES

The objective of this study was to assess the objective and subjective qualities of the contrast-enhanced 4-dimensional dual-energy computed tomography using adaptive statistical iterative reconstruction (ASiR) and ASiR-V.

METHODS

The virtual monochromatic images at 60 keV were reconstructed using filtered back projection, ASiR, and ASiR-V (10%-100%) for 14 patients with pancreatic cancer. The contrast-to-noise ratio (CNR) was calculated, and the subjective measurements were compared based on a 5-point score scale.

RESULTS

The ASiR-V yielded a significantly higher CNR than ASiR (P < 0.05). The subjective image quality (peak) was significantly improved (P < 0.01) with ASiR (50%) (3.8, 3.5, and 4.0; overall image quality, tumor delineation, and noise, respectively) and with ASiR-V (50%) (3.9, 3.5, and 4.2, respectively) compared with the filtered back projection (3.2, 3.2, and 3.0, respectively).

CONCLUSIONS

The ASiR-V yielded higher CNR than ASiR and provided the highest subjective scores regarding the overall image quality.

Improvement of image quality and assessment of respiratory motion for hepatocellular carcinoma with portal vein tumor thrombosis using contrast-enhanced four-dimensional dual-energy computed tomography

To assess the objective and subjective image quality, and respiratory motion of hepatocellular carcinoma with portal vein tumor thrombosis (PVTT) using the contrast-enhanced four-dimensional dual-energy computed tomography (CE-4D-DECT). For twelve patients, the virtual monochromatic image (VMI) derived from the CE-4D-DECT with the highest contrast to noise ratio (CNR) was determined as the optimal VMI (O-VMI). To assess the objective and subjective image quality, the CNR and five-point score of the O-VMI were compared to those of the standard VMI at 77 keV (S-VMI). The respiratory motion of the PVTT and diaphragm was measured based on the exhale and inhale phase images. The VMI at 60 keV yielded the highest CNR (4.8 ± 1.4) which was significantly higher (p = 0.02) than that in the S-VMI (3.8 ± 1.2). The overall image quality (4.0 ± 0.6 vs 3.1 ± 0.5) and tumor conspicuity (3.8 ± 0.8 vs 2.8 ± 0.6) of the O-VMI determined by three radiation oncologists was significantly higher (p < 0.01) than that of the S-VMI. The diaphragm motion in the L-R (3.3 ± 2.5 vs 1.2 ± 1.1 mm), A-P (6.7 ± 4.0 vs 1.6 ± 1.3mm) and 3D (8.8 ± 3.5 vs 13.1 ± 4.9 mm) directions were significantly larger (p < 0.05) compared to the tumor motion. The improvement of objective and subjective image quality was achieved in the O-VMI. Because the respiratory motion of the diaphragm was larger than that of the PVTT, we need to be pay attention for localizing target in radiotherapy.

Status and innovations in pre-treatment CT imaging for proton therapy

Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.

Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

OBJECTIVE

Determination of the optimal energy level of virtual monochromatic image (VMI) for brain metastases in contrast-enhanced dual-energy CT (DECT) for radiosurgery and assessment of the subjective and objective image quality of VMI at the optimal energy level.

METHODS

20 patients (total of 42 metastases) underwent contrast-enhanced DECT. Spectral image analysis of VMIs at energy levels ranging from 40 to 140 keV in 1 keV increments was performed to determine the optimal VMI (VMI_opt) as the one corresponding to the highest contrast-to-noise ratio (CNR) between brain parenchyma and the metastases. The objective and subjective values of VMI_opt were compared to those of the VMI with 120 kVp equivalent, defined as reference VMI (VMI_ref, 77 keV). The objective measurement parameters included mean HU value and SD of tumor and brain parenchyma, absolute lesion contrast (LC), and CNR. The subjective measurements included five-point scale assessment of "overall image quality" and "tumor delineation" by three radiation oncologists.

RESULTS

The VMI at 63 keV was defined as VMI_opt. The LC and CNR of VMI_opt were significantly (p < 0.01) higher than those of VMI_ref (LC: 37.4 HU vs 24.7 HU; CNR: 1.1 vs 0.8, respectively). Subjective analysis rated VMI_opt significantly (p < 0.01) superior to VMI_ref with respect to the overall image quality (3.2 vs 2.9, respectively) and tumor delineation (3.5 vs 2.9, respectively).

CONCLUSION

The VMI at 63 keV derived from contrast-enhanced DECT yielded the highest CNR and improved the objective and subjective image quality for radiosurgery, compared to VMI_ref.

ADVANCES IN KNOWLEDGE

This paper investigated for the first time the optimal energy level of VMI in DECT for brain metastases. The findings will lead to improvement in tumor visibility with optimal VMI and consequently supplement accuracy delineation of brain metastases.

Stereotactic body radiation therapy planning for liver tumors using functional images from dual-energy computed tomography

PURPOSE

This study aimed to generate a functional image of the liver using dual-energy computed tomography (DECT) and a functional-image-based stereotactic body radiation therapy plan to minimize the dose to the volume of the functional liver (V_fl).

MATERIAL AND METHODS

A normalized iodine density (NID) map was generated for fifteen patients with liver tumors. The volume of liver with an NID < 0.46 was defined as V_fl, and the ratio between V_fl and the total volume of the liver (FLR) was calculated. The relationship between the FLR and Fibrosis-4 (FIB-4) was assessed. For patients with 15% < FLR < 85%, functional volumetric modulated-arc therapy plans (F-VMAT) were retrospectively generated to preserve V_fl, and compared to the clinical plans (C-VMAT).

RESULTS

FLR showed a significantly strong correlation with FIB-4 (r = -0.71, p < 0.01). For ten generated F-VMAT plans, the dosimetric parameters of D_99%, D_50%, D_1% and the conformity index were comparable to those of the C-VMAT (p > 0.05). For V_fl, F-VMAT plans achieved lower V_5Gy (122.4 ± 31.7 vs 181.1 ± 57.3 cc), V_10Gy (44.4 ± 22.2 vs 98.2 ± 33.3 cc), V_15Gy (22.6 ± 20.3 vs 49.8 ± 33.7 cc), V_20Gy (11.6 ± 14.1 vs 24.9 ± 25.1 cc), and D_mean (3.9 ± 2.3 vs 5.8 ± 3.0 Gy) values than the C-VMAT plans (p < 0.01).

CONCLUSIONS

The functional image derived from DECT was successfully used, allowing for a reduction in the dose to the V_fl without compromising target coverage.

Dual-energy computed tomography for evaluation of breast cancer: value of virtual monoenergetic images reconstructed with a noise-reduced monoenergetic reconstruction algorithm

PURPOSE

To evaluate the image quality and lesion visibility of virtual monoenergetic images (VMIs) reconstructed using a new monoenergetic reconstruction algorithm (nMERA) for evaluation of breast cancer.

MATERIALS AND METHODS

Forty-two patients with 46 breast cancers who underwent 4-phasic breast contrast-enhanced computed tomography (CT) using dual-energy CT (DECT) were enrolled. We selected the peak enhancement phase of the lesion in each patient. The selected phase images were generated by 120-kVp-equivalent linear blended (M120) and monoenergetic reconstructions from 40 to 80 keV using the standard reconstruction algorithm (sMERA: 40, 50, 60, 70, 80) and nMERA (40 +, 50 +, 60 +, 70 +, 80 +). The contrast-to-noise ratio (CNR) was calculated and objectively analyzed. Two independent readers subjectively scored tumor visibility and image quality each on a 5-point scale.

RESULTS

The CNR at 40 + and tumor visibility scores at 40 + and 50 + were significantly higher than those on M120. The CNR at 50 + was not significantly different from that on M120. However, the overall image quality score at 40 + was significantly lower than that at 50 + and on M120 (40 + vs M120, P < 0.0001 and 40 + vs 50 +, P = 0.0001).

CONCLUSIONS

VMI reconstructed with nMERA at 50 keV is preferable for evaluation of patients with breast cancer.

Dosimetric effect of rotational setup errors in stereotactic radiosurgery with HyperArc for single and multiple brain metastases

Purpose

In stereotactic radiosurgery (SRS) with single‐isocentric treatments for brain metastases, rotational setup errors may cause considerable dosimetric effects. We assessed the dosimetric effects on HyperArc plans for single and multiple metastases.

Methods

For 29 patients (1–8 brain metastases), HyperArc plans with a prescription dose of 20–24 Gy for a dose that covers 95% (D_95%) of the planning target volume (PTV) were retrospectively generated (Ref‐plan). Subsequently, the computed tomography (CT) used for the Ref‐plan and cone‐beam CT acquired during treatments (Rot‐CT) were registered. The HyperArc plans involving rotational setup errors (Rot‐plan) were generated by re‐calculating doses based on the Rot‐CT. The dosimetric parameters between the two plans were compared.

Results

The dosimetric parameters [D_99%, D_95%, D_1%, homogeneity index, and conformity index (CI)] for the single‐metastasis cases were comparable (P > 0.05), whereas the D_95% for each PTV of the Rot‐plan decreased 10.8% on average, and the CI of the Rot‐plan was also significantly lower than that of the Ref‐plan (Ref‐plan vs Rot‐plan, 0.93 ± 0.02 vs 0.75 ± 0.14, P < 0.01) for the multiple‐metastases cases. In addition, for the multiple‐metastases cases, the Rot‐plan resulted in significantly higher V_10Gy (P = 0.01), V_12Gy (P = 0.02), V_14Gy (P = 0.02), and V_16Gy (P < 0.01) than those in the Ref‐plan.

Conclusion

The rotational setup errors for multiple brain metastases cases caused non‐negligible underdosage for PTV and significant increases of V_10Gy to V_16Gy in SRS with HyperArc.