Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT

Lalonde, Arthur; Xie, Y.; Burgdorf, Brendan; O’Reilly, Shannon; Ingram, W. Scott; Yin, Lingshu; Zou, W.; Dong, Lei; Bouchard, Hugo; Teo, B.K.

doi:10.1088/1361-6560/ab1e9d

Cited by 17 publications

(15 citation statements)

References 24 publications

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“…Thus, the reported advantages on RSP maps of other DECT scanners are not achievable on the Twinbeam. This finding is consistent with other authors who found inferior performance of the Twinbeam system for virtual monoenergetic and virtual non‐contrast imaging 50,51 . Using images with reduced stopping power uncertainty could allow for reduced margins when designing treatment plans, possibly opening pathways to dose escalation and increased organ‐at‐risk sparing 52 .…”

Section: Discussionsupporting

confidence: 91%

“…This finding is consistent with other authors who found inferior performance of the Twinbeam system for virtual monoenergetic and virtual noncontrast imaging. 50,51 Using images with reduced stopping power uncertainty could allow for reduced margins when designing treatment plans, possibly opening pathways to dose escalation and increased organ-at-risk sparing. 52 If dose could be calculated on daily CBCT using the proposed method, the margins typically added for setup uncertainties could be reduced since the patient would not be moved between "simulation" and treatment.…”

Section: Discussionmentioning

confidence: 99%

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Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

et al. 2020

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In intensity-modulated proton therapy (IMPT), protons are used to deliver highly conformal dose distributions, targeting tumors, and sparing organs-at-risk. However, due to uncertainties in both patient setup and relative stopping power (RSP) calculation, margins are added to the treatment volume during treatment planning, leading to higher doses to normal tissues. Cone-beam computed tomography (CBCT) images are taken daily before treatment; however, the poor image quality of CBCT limits the use of these images for online dose calculation. In this work, we use a deep-learning-based method to predict RSP maps from daily CBCT images, allowing for online dose calculation in a step toward adaptive radiation therapy. Methods: Twenty-three head-and-neck cancer patients were simulated using a Siemens TwinBeam dual-energy CT (DECT) scanner. Mixed-energy scans (equivalent to a 120 kVp single-energy CT scan) were converted to RSP maps for treatment planning. Cone-beam computed tomography images were taken on the first day of treatment, and the planning RSP maps were registered to these images. A deep learning network based on a cycle-GAN architecture, relying on a compound loss function designed for structural and contrast preservation, was then trained to create an RSP map from a CBCT image. Leave-one-out and holdout cross validations were used for evaluation, and mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used to quantify the differences between the CT-based and CBCT-based RSP maps. The proposed method was compared to a deformable image registration-based method which was taken as the ground truth and two other deep learning methods. For one patient who underwent resimulation, the new planning RSP maps and CBCT images were used for further evaluation and validation. Results: The CBCT-based RSP generation method was evaluated on 23 head-and-neck cancer patients. From leave-one-out testing, the MAE between CT-based and CBCT-based RSP was 0.06 AE 0.01 and the ME was −0.01 AE 0.01. The proposed method statistically outperformed the comparison DL methods in terms of MAE and ME when compared to the planning CT. In terms of dose comparison, the mean gamma passing rate at 3%/3 mm was 94% when three-dimensional (3D) gamma index was calculated per plan and 96% when gamma index was calculated per field. Conclusions: The proposed method provides sufficiently accurate RSP map generation from CBCT images, allowing for evaluation of daily dose based on CBCT and possibly allowing for CBCTguided adaptive treatment planning for IMPT.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

et al. 2020

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“…In addition, the DL method can provide benefits for particle therapy, where dose calculations using CT number‐to‐stopping power conversion could be more sensitive to uncertainties in the CT numbers. According to a recent study that compared VNC DECT and CE images for proton therapy, the error in the stopping power ratio due to the range error was reduced to <1 mm in the VNC DECT images, whereas it was 3.2 mm in the CE images . However, our findings suggest that non‐negligible errors can occur in VNC DECT ‐based dose calculations when the beam passes through a bone structure.…”

Section: Discussionmentioning

confidence: 49%

“…According to a recent study that compared VNC DECT and CE images for proton therapy, the error in the stopping power ratio due to the range error was reduced to <1 mm in the VNC DECT images, whereas it was 3.2 mm in the CE images. 37 However, our findings suggest that nonnegligible errors can occur in VNC DECT -based dose calculations when the beam passes through a bone structure. Although the proposed DL method provided promising results in treatment planning, the VNC DL images should be used for dose calculation, not for other tasks.…”

Section: Discussionmentioning

confidence: 66%

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Koike¹,

Ohira²,

Akino

et al. 2019

Medical Physics

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Purpose The aim of this study was to develop a deep learning (DL) method for generating virtual noncontrast (VNC) computed tomography (CT) images from contrast‐enhanced (CE) CT images (VNCDL) and to evaluate its performance in dose calculations for head and neck radiotherapy in comparison with VNC images derived from a dual‐energy CT (DECT) scanner (VNCDECT). Methods This retrospective study included data for 61 patients who underwent head and neck radiotherapy. All planning CT images were obtained with a single‐source DECT scanner (80 and 140 kVp) with rapid kVp switching. The DL‐based method used a pair of virtual monochromatic images (VMIs) at 70 keV with and without contrast materials. VMIs without contrast materials were used as reference true noncontrast (TNC) images. Deformable image registration was used between the TNC and CE images. We used the data of 45 patients, chosen randomly, for training (7922 paired images), and data from the other 16 patients as test data. We generated the VNCDL images with a densely connected convolutional network. As the VNCDECT images, we used VMIs with the iodine signal suppressed, reconstructed from the CE images of the 16 test patients. The CT numbers of the tumor, common carotid artery, internal jugular vein, muscle, fat, bone marrow, cortical bone, and mandible of each VNC image were compared with those of the TNC image. The dose of the reference TNC plan was recalculated using the CE, VNCDL, and VNCDECT images. Difference maps of the dose distributions and dose–volume histograms were evaluated. Results The mean prediction time for the VNCDL images was 3.4 s per patient, and the mean number of slices was 204. The absolute differences in CT numbers of the VNCDL images were significantly smaller than those of the VNCDECT images for the bone marrow (8.0 ± 6.5 vs 175.1 ± 40.9 HU; P < 0.001) and mandible (20.3 ± 19.3 vs 106.2 ± 80.5 HU; P = 0.002). The DL‐based model provided the dose distribution most similar to that of the TNC plan. With the VNCDECT plans, dose errors >1.0% were observed in bone regions. The dose–volume histogram analysis showed that the VNCDL plans yielded the smallest errors for the primary target, although dose differences were <1.0% for all the approaches. For the maximum dose to the mandible, the mean ± SD errors for the CE, VNCDL, and VNCDECT plans were –0.13% ± 0.23% (range: −0.46% to 0.31%; P = 0.037), –0.01% ± 0.22% (range: −0.40% to 0.36%; P = 1.0), and 0.53% ± 0.47% (range: −0.21% to 1.41%; P < 0.001), respectively. Conclusions In this study, we developed a method based on DL that can rapidly generate VNC images from CE images without a DECT scanner. Compared with the DECT approach, the DL‐based method improved the prediction accuracy of CT numbers in bone regions. Consequently, there was greater agreement between the VNCDL and TNC plan dose distributions than with the CE and VNCDECT plans, achieved by suppressing the contrast material signals while retaining the CT numbers of bone structures.

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“…Such VNC approach requires the attenuation coe cients of contrast enhanced agents of two DECT spectra. It has disadvantages of high complication coe cient, high requirements on machine performance, and high cost [11] . VNC approach is widely applied in diagnostic imaging [12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Application of Virtual Noncontrast CT Generation Technology From Intravenous Enhanced CT Based on Deep Learning in Proton Radiotherapy

sui

Gao

Shang

et al. 2020

Preprint

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Objective: The aim of this study is to generate virtual noncontrast (VNC) computed tomography (CT) from intravenous enhanced CT by using Unet convolutional neural network (CNN). The differences among enhanced, VNC, and noncontrast CT in proton dose calculation were compared. Methods: A total of 30 groups of CT images of patients who received enhanced and noncontrast CT were selected. Enhanced and noncontrast CT were registered. Among these patients, 20 groups of the CT images were chosen as the training set. Enhanced CT images were used as the input, and the corresponding noncontrast CT images were used as output to train the Unet neural network. The remaining 10 groups of CT images were chosen as the test set. VNC images were generated by the trained Unet neural network. The same proton radiotherapy plan for esophagus cancer was designed based on three images. Proton dose distributions in enhanced, VNC, and noncontrast CT were calculated. The relative dose differences in enhanced CT with VNC and noncontrast CT were analyzed. Results: The mean absolute error (MAE) of the CT values between enhanced and noncontrast CT was 32.3 ± 2.6 HU. The MAE of the CT values between VNC and noncontrast CT was 6.7 ± 1.3 HU. The mean values of the enhanced CT in the great vessel, heart, lung, liver, and spinal cord were significantly higher than those of noncontrast CT, with the differences of 97, 83, 42, 40, and 10 HU, respectively. The mean values of the VNC CT showed no significant difference with noncontrast CT. The differences among enhanced, VNC, and noncontrast CT in terms of the average relative proton dose for clinical target volume(CTV), heart, great vessels, and lung were also investigated. The average relative proton doses of the contrast CT for these organs were significantly lower than those of noncontrast CT. The largest difference was observed in the great vessel, while the differences in other organs were relatively small. The γ-passing rates of the enhanced and VNC CT were calculated by 2% dose difference and 2 mm distance to agreement. Results showed that the mean γ-passing rate of VNC CT was significantly higher than that of enhanced CT (p<0.05). Conclusions: The proton radiotherapy design based on enhanced CT increased the range error, thereby resulting in calculation errors of the proton dose. Therefore, a technology that can be used to generate VNC CT from enhanced CT based on Unet neural network was proposed. The proton dose calculated based on VNC CT images was essentially consistent with that based on noncontrast CT.

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Influence of intravenous contrast agent on dose calculation in proton therapy using dual energy CT

Cited by 17 publications

References 24 publications

Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

Deep learning‐based virtual noncontrast CT for volumetric modulated arc therapy planning: Comparison with a dual‐energy CT‐based approach

Application of Virtual Noncontrast CT Generation Technology From Intravenous Enhanced CT Based on Deep Learning in Proton Radiotherapy

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