Image quality and dose evaluation of MVCT TomoTherapy acquisitions: A phantom study

Evaluation of the Treatment Planning and Delivery for Hip Implant Cases on Tomotherapy

Purpose

The metal present in the implant creates artifacts during the treatment simulation, which impacts the treatment planning and delivery of the prescribed dose to the target and sparing normal tissues. This retrospective study evaluated the uncertainties in the planning and delivery of doses for prosthesis cases with dedicated phantom.

Materials and Methods

In this retrospective study, 11 patients with a hip prosthesis having cervix carcinoma were selected. Two treatment plans were generated on treatment planning system (TPS) for each case. Plan_No_Res was without any beam restriction, and Plan_exit_only was the plan with restricted beam entry through the metallic implant. An indigenous phantom was utilized to verify the accuracy of the treatment. In the phantom, some groves were present, which could be filled by implants that mimic the patient's geometries, like left, right and bilateral femur implants. The delivered doses were recorded using optically stimulated luminescence dosimeters (OSLDs), which were placed at different positions in the phantom. The plans were further calculated using megavoltage computed tomography (MVCT) scans acquired during treatment.

Results

The patient data showed no significant dose changes between the two planning methods. The treatment time increases from 412.18 ± 86.65 to 427.36 ± 104.80 with P = 0.03 for Plan_No_Res and Plan_exit_only, respectively. The difference between planned and delivered doses of various points across phantom geometries was within ± 9.5% in each case as left, right, and bilateral implant. The variations between OSLDs and MVCT calculated doses were also within ± 10.8%.

Conclusion

The study showed the competency of tomotherapy planning for hip prosthesis cases. The phantom measurements demonstrate the errors in dosimetry near the implant material, suggesting the need for precise methods to deal with artifact-related issues.

The effects of mega-voltage CT scan parameters on offline adaptive radiation therapy

TomoTherapy involves image-guided radiation therapy (IGRT) using Mega-voltage CT (MVCT) for each treatment session. The acquired MVCT images can be utilized for the retrospective assessment of dose distribution. The TomoTherapy provides 18 distinct imaging conditions that can be selected based on a combination of algorithms, acquisition pitch, and slice interval. We investigated the accuracy of dose calculation and deformable image registration (DIR) depending on MVCT scan parameters and their effects on adaptive radiation therapy (ART). We acquired image values for density calibration tables (IVDTs) under 18 different MVCT conditions and compared them. The planning CT (pCT) was performed using a thoracic phantom, and an esophageal intensity-modulated radiation therapy (IMRT) plan was created. MVCT images of the thoracic phantom were acquired under each of the 18 conditions, and dose recalculation was performed. DIR was performed on the MVCT images acquired under each condition. The accuracy of DIR, depending on the MVCT scan parameters, was compared using the mean distance to agreement (MDA) and Dice similarity coefficient (DSC). The dose distribution calculated on the MVCT images was deformed using deformed vector fields (DVF). No significant differences were observed in the results of the 18 IVDTs. The esophageal IMRT plan also showed a small dose difference. Regarding verifying the DIR accuracy, the MDA increased, and the DSC decreased as the acquisition pitch and slice interval increased. The difference between the dose distributions after dose mapping was comparable to that before DIR. The MVCT scan parameters had little effect on ART.

A qualitative study of improving megavoltage computed tomography image quality and maintaining dose accuracy using cycleGAN-based image synthesis

BACKGROUND

Due to inconsistent positioning, tumor shrinking, and weight loss during fractionated treatment, the initial plan was no longer appropriate after a few fractional treatments, and the patient will require adaptive helical tomotherapy (HT) to overcome the issue. Patients are scanned with megavoltage computed tomography (MVCT) before each fractional treatment, which is utilized for patient setup and provides information for dose reconstruction. However, the low contrast and high noise of MVCT make it challenging to delineate treatment targets and organs at risk (OAR).

PURPOSE

This study developed a deep-learning-based approach to generate high-quality synthetic kilovoltage computed tomography (skVCT) from MVCT and meet clinical dose requirements.

METHODS

Data from 41 head and neck cancer patients were collected; 25 (2995 slices) were used for training, and 16 (1898 slices) for testing. A cycle generative adversarial network (cycleGAN) based on attention gate and residual blocks was used to generate MVCT-based skVCT. For the 16 patients, kVCT-based plans were transferred to skVCT images and electron density profile-corrected MVCT images to recalculate the dose. The quantitative indices and clinically relevant dosimetric metrics, including the mean absolute error (MAE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), gamma passing rates, and dose-volume-histogram (DVH) parameters (Dmax , Dmean , Dmin ), were used to assess the skVCT images.

RESULTS

The MAE, PSNR, and SSIM of MVCT were 109.6 ± 12.3 HU, 27.5 ± 1.1 dB, and 91.9% ± 1.7%, respectively, while those of skVCT were 60.6 ± 9.0 HU, 34.0 ± 1.9 dB, and 96.5% ± 1.1%. The image quality and contrast were enhanced, and the noise was reduced. The gamma passing rates improved from 98.31% ± 1.11% to 99.71% ± 0.20% (2 mm/2%) and 99.77% ± 0.18% to 99.98% ± 0.02% (3 mm/3%). No significant differences (p > 0.05) were observed in DVH parameters between kVCT and skVCT.

CONCLUSION

With training on a small data set (2995 slices), the model successfully generated skVCT with improved image quality, and the dose calculation accuracy was similar to that of MVCT. MVCT-based skVCT can increase treatment accuracy and offer the possibility of implementing adaptive radiotherapy.

Evaluation of the MVCT-based radiomic features as prognostic factor in patients with head and neck squamous cell carcinoma

BACKGROUND

Megavoltage computed tomography (MVCT) images acquired during each radiotherapy session may be useful for delta radiomics. However, no studies have examined whether the MVCT-based radiomics has prognostic power. Therefore, the purpose of this study was to examine the prognostic power of the MVCT-based radiomics for head and neck squamous cell carcinoma (HNSCC) patients.

METHODS

100 HNSCC patients who received definitive radiotherapy were analyzed and divided into two groups: training (n = 70) and test (n = 30) sets. MVCT images obtained using TomoTherapy for the first fraction of radiotherapy and planning kilovoltage CT (kVCT) images obtained using Aquilion LB CT scanner were analyzed. Primary gross tumor volume (GTV) was propagated from kVCT to MVCT images using rigid registration, and 107 radiomic features were extracted from the GTV in MVCT and kVCT images. Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to examine the association between overall survival (OS) and rad score calculated for each patient by weighting the feature value through the coefficient when features were selected. Then, the predictive values of MVCT-based and kVCT-based rad score and patient-, treatment-, and tumor-specific factors were evaluated.

RESULTS

C-indices of the rad score for MVCT- and kVCT-based radiomics were 0.667 and 0.685, respectively. The C-indices of 6 clinical factors were 0.538-0.622. The 3-year OS was significantly different between high- and low-risk groups according to the MVCT-based rad score (50% vs. 83%; p < 0.01).

CONCLUSIONS

Our results suggested that MVCT-based radiomics had stronger prognostic power than any single clinical factor and was a useful prognostic factor when predicting OS in HNSCC patients.

Investigation of total skin helical tomotherapy using a 3D-printed total skin bolus

OBJECTIVE

To investigate the effectiveness of using a 3D-printed total skin bolus in total skin helical tomotherapy for the treatment of mycosis fungoides.

MATERIALS AND METHODS

A 65-year-old female patient with a 3-year history of mycosis fungoides underwent treatment using an in-house desktop fused deposition modelling printer to create a total skin bolus made of a 5-mm-thick flexible material, which increased the skin dose through dose building. The patient's scan was segmented into upper and lower sections, with the division line placed 10 cm above the patella. The prescription was to deliver 24 Gy over 24 fractions, given 5 times per week. The plan parameters consisted of a field width of 5 cm, pitch of 0.287 and modulation factor of 3. The complete block was placed 4 cm away from the planned target region to reduce the area of the internal organs at risk, especially the bone marrow. Dose delivery accuracy was verified using point dose verification with a "Cheese" phantom (Gammex RMI, Middleton, WI), 3D plane dose verification with ArcCHECK (Model 1220, Sun Nuclear, Melbourne, FL), and multipoint film dose verification. Megavoltage computed tomography guidance was also utilized to ensure the accuracy of the setup and treatment.

RESULTS

A 5-mm-thick 3D-printed suit was used as a bolus to achieve a target volume coverage of 95% of the prescribed dose. The conformity index and homogeneity index of the lower segment were slightly better than those of the upper segment. As the distance from the skin increased, the dose to the bone marrow gradually decreased, and the dose to other organs at risk remained within clinical requirements. The point dose verification deviation was less than 1%, the 3D plane dose verification was greater than 90%, and the multipoint film dose verification was less than 3%, all of which confirmed the accuracy of the delivered dose. The total treatment time was approximately 1.5 h, which included 0.5 h of wearing the 3D-printed suit and 1 h with the beam on. Patients only experienced mild fatigue, nausea or vomiting, low-grade fever, and grade III bone marrow suppression.

CONCLUSION

The use of a 3D-printed suit for total skin helical tomotherapy can result in a uniform dose distribution, short treatment time, simple implementation process, good clinical outcomes, and low toxicity. This study presents an alternative treatment approach that can potentially yield improved clinical outcomes for mycosis fungoides.

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network

BACKGROUND

Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation.

PURPOSE

In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning.

METHODS

MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs).

RESULTS

MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively.

CONCLUSIONS

MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow.

Clinical implementation of kVCT-guided tomotherapy with ClearRT

A helical fan-beam kilovoltage computed tomography (kVCT) was recently introduced into Tomotherapy units. This study aims to share the initial experience of kVCT in clinical workflow, compare its performance with that of the existing megavoltage computed tomography (MVCT), and explore its potential in adaptive planning. We retrospectively enrolled 23 patients who underwent both MVCT and kVCT scans. The clinical performance data regarding image acquisition time, nominal dose length product (DLP), registration time and registration corrections were extracted and compared. Image quality was scored by six experienced radiation therapists and quantified based on phantom measurements. CT number stability and the implementation of adaptive radiotherapy were dosimetrically evaluated by performing the dose recalculation on kVCT. Compared to MVCT, kVCT significantly reduced DLP (except the highest kVp protocol), image acquisition and registration time. KVCT obtained higher scores than MVCT on all criteria except artifacts. Phantom measurements also revealed a better image performance characterization of kVCT except for image uniformity. The CT number variation could lead to a dose difference of 0.5% for D95% of target and D_mean of organ-at-risk. For the treatment planning with kVCT, a systematic dose difference (> 1%) in PTV dose metrics was observed at regions with large longitudinal density discontinuities compared to the reference plans. The new kVCT imaging provides enhanced soft-tissue visualization. The improved efficiency with kVCT-guided treatment will allow more patients to be treated each day. In most cases, the dose calculation accuracy of kVCT images is acceptable except for regions with severe artifacts.

Performance evaluation of image reconstruction algorithms for a megavoltage computed tomography system on a helical tomotherapy unit

Objective. To evaluate the impact of image reconstruction algorithm selection, as well as imaging mode and the reconstruction interval, on image quality metrics for megavoltage computed tomography (MVCT) image acquisition for use in image-guided (IGRT) and adaptive radiotherapy (ART) on a next-generation helical tomotherapy system.Approach. A CT image quality phantom was scanned across all available acquisition modes for filtered back projection (FBP) and both iterative reconstruction (IR) algorithms available on the system. Image quality metrics including noise, uniformity, contrast, spatial resolution, and mean CT number were compared. Analysis of DICOM data was performed using ImageJ software and Python code. ANOVA single factor and Tukey's honestly significant difference post-hoc tests were utilized for statistical analysis.Main Results. Application of both IR algorithms noticeably improved noise and image contrast when compared to the FBP algorithm available on all previous-generation helical tomotherapy systems. Use of the FBP algorithm improved image uniformity and spatial resolution in the axial plane, though values for the IR algorithms were well within tolerances recommended for IGRT and/or MVCT-based ART implementation by the American Association of Physicists in Medicine (AAPM). Additionally, longitudinal resolution showed little dependence on the reconstruction algorithm, while a negligible variation in mean CT number was observed regardless of the reconstruction algorithm or acquisition parameters. Statistical analysis confirmed the significance of these results.Significance. An overall improvement in image quality for metrics most important to IGRT and ART-mainly image noise and contrast-was evident in the application of IR when compared to FBP. Furthermore, since other imaging parameters remain identical regardless of the reconstruction algorithm, this improved image quality does not come at the expense of additional patient dose or an increased scan acquisition time for otherwise identical parameters. These improvements are expected to enhance fidelity in IGRT and ART implementation.

TomoMQA: Automated analysis program for MVCT quality assurance of helical tomotherapy

Purpose

In this study, we developed a simple but useful computer program, called TomoMQA, to offer an automated quality assurance for mega‐voltage computed tomography (MVCT) images generated via helical tomotherapy.

Methods

TomoMQA is written in MATLAB and contains three steps for analysis: (a) open the DICOM dataset folder generated via helical tomotherapy (i.e., TomoTherapy^® and Radixact™), (b) call the baseline data for the consistency test and click the “Analysis” button (or click the “Analysis” button without the baseline data and export the results as the baseline data), and (c) print an analyzed report. The overall procedure for the QA analysis included in TomoMQA is referred from the TG‐148 recommendation. Here, the tolerances for MVCT QA were implemented from TG‐148 recommended values as default; however, it can be modified by a user manually.

Results

To test the performance of the TomoMQA program, 15 MVCTs were prepared from five helical tomotherapy machines (1 of TomoTherapy^® HD, 2 of TomoTherapy^® HDA, and 2 of Radixact™) in 3 months and the QA procedures were performed using TomoMQA. From our results, the evaluation revealed that the developed program can successfully perform the MVCT QA analysis irrespective of the type of helical tomotherapy equipment.

Conclusion

We successfully developed a new automated analysis program for MVCT QA of a helical tomotherapy platform, called TomoMQA. The developed program will be made freely downloadable from the TomoMQA‐dedicated website.