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

A simple algorithm to derive virtual non-contrast electron density from dual-energy computed tomography data for radiotherapy treatment planning

BACKGROUND

The use of iodinated contrast-enhancing agents in computed tomography (CT) improves the visualization of relevant structures for radiotherapy treatment planning (RTP). However, it can lead to dose calculation errors by incorrectly converting a CT number to electron density.

PURPOSE

This study aimed to propose an algorithm for deriving virtual non-contrast (VNC) electron density from dual-energy CT (DECT) data. This algorithm was developed by extending the formula previously developed by Saito, which enables the calculation of the electron density of human tissue through weighted subtraction of CT numbers acquired from DECT scans.

METHODS

To investigate the feasibility of the proposed VNC algorithm, we performed analytical DECT image simulations at 90 and 150 kV/Sn on virtual phantoms consisting of various tissue/iodine surrogates with known mass densities and elemental compositions. Two different shapes of phantoms made of water-mimicking surrogates were generated as inputs: a circular phantom (33 cm diameter) for calibration and an elliptical phantom (33 cm width and 28 cm height) for validation. The circular phantom was equipped with inserts of human-tissue-mimicking substitutes, pure water, and iodine-enhanced soft-tissue substitutes (2, 5, 10, and 15 mg/mL iodine). The elliptical phantom contained inserts of reference human tissues, iodine-enhanced soft-tissue substitutes (2, 2.5, 5, 7.5, 10, 15, and 20 mg/mL iodine), and a 10-mm-diameter core of 4 mg/mL iodine surrounded by a blood-mimicking base material. The performance of the proposed algorithm was evaluated by comparing the accuracy of VNC electron densities with those of non-contrast (NC) base materials (water- or blood-mimicking surrogates).

RESULTS

The derived algorithm enabled the calculation of VNC electron density in a manner similar to that of unenhanced human tissues by adapting a VNC-specific weighting factor, thereby eliminating the intermediate step of converting CT numbers to electron density. The simulated results showed that the VNC algorithm could almost completely remove the contrast in the electron density image between iodine-enhanced and base materials. The relative deviations of simulated VNC electron density values from the corresponding pre-contrast value were within ± 0.4% for all tested materials, with a root-mean-square error (RMSE) of 0.2%.

CONCLUSIONS

Within the limits of the analytical DECT image simulation used in this study, the simple VNC algorithm could effectively provide accurate VNC electron densities for iodine-enhanced materials. This may allow the contrast agent to be used for CT scans during RTP without compromising dose calculation accuracy.

Generation of deep learning based virtual non-contrast CT using dual-layer dual-energy CT and its application to planning CT for radiotherapy

This paper presents a novel approach for generating virtual non-contrast planning computed tomography (VNC-pCT) images from contrast-enhanced planning CT (CE-pCT) scans using a deep learning model. Unlike previous studies, which often lacked sufficient data pairs of contrast-enhanced and non-contrast CT images, we trained our model on dual-energy CT (DECT) images, using virtual non-contrast CT (VNC CT) images as outputs instead of true non-contrast CT images. We used a deterministic method to convert CE-pCT images into pseudo DECT images for model application. Model training and evaluation were conducted on 45 patients. The performance of our model, 'VNC-Net', was evaluated using various metrics, demonstrating high scores for quantitative performance. Moreover, our model accurately replicated target VNC CT images, showing close correspondence in CT numbers. The versatility of our model was further demonstrated by applying it to pseudo VNC DECT generation, followed by conversion to VNC-pCT. CE-pCT images of ten liver cancer patients and ten left-sided breast cancer patients were used. A quantitative comparison with true non-contrast planning CT (TNC-pCT) images validated the accuracy of the generated VNC-pCT images. Furthermore, dose calculations on CE-pCT and VNC-pCT images from patients undergoing volumetric modulated arc therapy for liver and breast cancer treatment showed the clinical relevance of our approach. Despite the model's overall good performance, limitations remained, particularly in maintaining CT numbers of bone and soft tissue less influenced by contrast agent. Future research should address these challenges to further improve the model's accuracy and applicability in radiotherapy planning. Overall, our study highlights the potential of deep learning models to improve imaging protocols and accuracy in radiotherapy planning.

Virtual unenhanced dual-energy computed tomography for photon radiotherapy: The effect on dose distribution and cone-beam computed tomography based position verification

Background and Purpose

Virtual Unenhanced images (VUE) from contrast-enhanced dual-energy computed tomography (DECT) eliminate manual suppression of contrast-enhanced structures (CES) or pre-contrast scans. CT intensity decreases in high-density structures outside the CES following VUE algorithm application. This study assesses VUE's impact on the radiotherapy workflow of gynecological tumors, comparing dose distribution and cone-beam CT-based (CBCT) position verification to contrast-enhanced CT (CECT) images.

Materials and Methods

A total of 14 gynecological patients with contrast-enhanced CT simulation were included. Two CT images were reconstructed: CECT and VUE. Volumetric Modulated Arc Therapy (VMAT) plans generated on CECT were recalculated on VUE using both the CECT lookup table (LUT) and a dedicated VUE LUT. Gamma analysis assessed 3D dose distributions. CECT and VUE images were retrospectively registered to daily CBCT using Chamfer matching algorithm..

Results

Planning target volume (PTV) dose agreement with CECT was within 0.35% for D2%, Dmean, and D98%. Organs at risk (OARs) D2% agreed within 0.36%. A dedicated VUE LUT lead to smaller dose differences, achieving a 100% gamma pass rate for all subjects. VUE imaging showed similar translations and rotations to CECT, with significant but minor translation differences (<0.02 cm). VUE-based registration outperformed CECT. In 24% of CBCT-CECT registrations, inadequate registration was observed due to contrast-related issues, while corresponding VUE images achieved clinically acceptable registrations.

Conclusions

VUE imaging in the radiotherapy workflow is feasible, showing comparable dose distributions and improved CBCT registration results compared to CECT. VUE enables automated bone registration, limiting inter-observer variation in the Image-Guided Radiation Therapy (IGRT) process.