Volumetric modulated arc therapy planning based on virtual monochromatic images: Effect of inaccurate CT numbers on dose distributions

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.

The effects of distance between the imaging isocenter and brain center on the image quality of cone-beam computed tomography for brain stereotactic irradiation

In linear accelerator-based stereotactic irradiation (STI) for brain metastasis, cone-beam computed tomography (CBCT) image quality is essential for ensuring precise patient setup and tumor localization. However, CBCT images may be degraded by the deviation of the CBCT isocenter from the brain center. This study aims to investigate the effects of the distance from the brain center to the CBCT isocenter (DBI) on the image quality in STI. An anthropomorphic phantom was scanned with varying DBI in right, anterior, superior, and inferior directions. Thirty patients undergoing STI were prospectively recruited. Objective metrics, utilizing regions of interest included contrast-to-noise ratio (CNR) at the centrum semiovale, lateral ventricle, and basal ganglia levels, gray and white matter noise at the basal ganglia level, artifact index (AI), and nonuniformity (NU). Two radiation oncologists assessed subjective metrics. In this phantom study, objective measures indicated a degradation in image quality for non-zero DBI. In this patient study, there were significant correlations between the CNR at the centrum semiovale and lateral ventricle levels (rs = - 0.79 and - 0.77, respectively), gray matter noise (rs = 0.52), AI (rs = 0.72), and NU (rs = 0.91) and DBI. However, no significant correlations were observed between the CNR at the basal ganglia level, white matter noise, and subjective metrics and DBI (rs < ± 0.3). Our results demonstrate the effects of DBI on contrast, noise, artifacts in the posterior fossa, and uniformity of CBCT images in STI. Aligning the CBCT isocenter with the brain center can aid in improving image quality.

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.

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.

Dual-energy computed tomography image-based volumetric-modulated arc therapy planning for reducing the effect of contrast-enhanced agent on dose distributions

To compare the effect of a contrast-enhanced (CE) agent on volumetric-modulated arc therapy plans based on four types of images-virtual monochromatic images (VMIs) captured at 70 and 140 keV (namely VMI₇₀ and VMI₁₄₀, respectively), water density image (WDI), and virtual non-contrast image (VNC) generated using a dual-energy computed tomography (DECT) system. A tissue characterization phantom and a multi-energy phantom were scanned, and VMI₇₀, VMI₁₄₀, WDI, and VNC were retrospectively reconstructed. For each image, a lookup table (LUT) was created. For 13 patients with nasopharyngeal cancer, non-CE and CE scans were performed, and volumetric-modulated arc therapy plans were generated on the basis of non-CE VMI₇₀. Subsequently, the doses were re-calculated using the four types of DECT images and their corresponding LUTs. The maximum differences in the physical density estimation were 21.3, 5.2, -3.9, and 0.5% for VMI₇₀, VMI₁₄₀, WDI, and VNC, respectively. Compared with VMI₇₀, the WDI approach significantly reduced (p < 0.05) the dosimetric difference due to the CE agent for the planning target volume (PTV) (D_50%), whereas the difference was significantly increased for D_1%. Except for PTV (D_1%), the differences were significantly lower (p < 0.05) in the treatment plans based on VMI₁₄₀ and VNC than that based on VMI₇₀. For the VNC, the mean difference was less than 0.2% for all dosimetric parameters for the PTV. For patients with NPC, treatment plans based on the VNC derived from CE scan showed the best agreement with those based on the non-CE VMI₇₀. Ideally, the effect of CE agent on dose distribution does not appear in treatment planning procedures.

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.

Volumetric modulated arc therapy treatment planning based on virtual monochromatic images for head and neck cancer: effect of the contrast-enhanced agent on dose distribution

Virtual monochromatic images (VMIs) at a lower energy level can improve image quality but the computed tomography (CT) number of iodine contained in the contrast‐enhanced agent is dramatically increased. We assessed the effect of the use of contrast‐enhanced agent on the dose distributions in volumetric modulated arc therapy (VMAT) planning for head and neck cancer (HNC). Based on the VMIs at 40 keV (VMI_40keV), 60 keV(VMI_60keV), and 77 keV (VMI_77keV) of a tissue characterization phantom, lookup tables (LUTs) were created. VMAT plans were generated for 15 HNC patients based on contrast‐enhanced‐ (CE‐) VMIs at 40‐, 60‐, and 77 keV using the corresponding LUTs, and the doses were recalculated based on the noncontrast‐enhanced‐ (nCE‐) VMIs. For all structures, the difference in CT numbers owing to the contrast‐enhanced agent was prominent as the energy level of the VMI decreased, and the mean differences in CT number between CE‐ and nCE‐VMI was the largest for the clinical target volume (CTV) (125.3, 55.9, and 33.1 HU for VMI_40keV, VMI_60keV, and VMI_77keV, respectively). The mean difference of the dosimetric parameters (D_99%, D_50%, D_1%, D_mean, and D_0.1cc) for CTV and OARs was <1% in the treatment plans based on all VMIs. The maximum difference was observed for CTV in VMI_40keV (2.4%), VMI_60keV (1.9%), and VMI_77keV (1.5%) plans. The effect of the contrast‐enhanced agent was larger in the VMAT plans based on the VMI at a lower energy level for HNC patients. This effect is not desirable in a treatment planning procedure.