kV Cone-Beam CT-Based IGRT

Quantitative use of cone-beam computed tomography in proton therapy: challenges and opportunities

The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.

Artificial intelligence for treatment delivery: image-guided radiotherapy

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.

Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy

OBJECTIVE

We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.

METHODS

A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR).

RESULTS

The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p < 0.05), and the subjective scores of DLR were close to NDCT.

CONCLUSION

The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.

A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning

PURPOSE

To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective.

METHODS

A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R2).

RESULTS

The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the X direction (RMSE =0.019 ∼ 0.145mm, R2 =0.9793∼0.9996) and the Z direction (RMSE = 0.051 ∼ 0.168 mm, R2 = 0.9736∼0.9976) show the best results, while the Y direction ranked behind (RMSE = 0.088 ∼ 0.224 mm, R2 = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and RMSE of 0.015 mm, 0.083 mm, 0.041 mm, and R2 of 0.9998, 0.9931, 0.9984 respectively for X, Y, Z were obtained.

CONCLUSIONS

The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.

Estimation of organ and effective doses of CBCT scans of radiotherapy using size-specific field of view (FOV): a Monte Carlo study

The kV cone beam computed tomography (CBCT) is one of the most common imaging modalities used for image-guided radiation therapy (IGRT) procedures. Additional doses are delivered to patients, thus assessment and optimization of the imaging doses should be taken into consideration. This study aimed to investigate the influence of using fixed and patient-specific FOVs on the patient dose. Monte Carlo simulations were performed to simulate kV beams of the imaging system integrated into Truebeam linear accelerator using BEAMnrc code. Organ and size-specific effective doses resulting from chest and pelvis scanning protocols were estimated with DOSXYZnrc code using a phantom library developed by the National Cancer Institute (NCI) of the US. The library contains 193 (100 male and 93 female) mesh-type computational human adult phantoms, and it covers a large ratio of patient sizes with heights and weights ranging from 150 to 190 cm and 40 to 125 kg. The imaging doses were assessed using variable FOV of three sizes, small (S), medium (M), and large (L) for each scan region. The results show that the FOV and the patient size played a major role in the scan dose. The average percentage differences (PDs) for doses of organs that were fully inside the different FOVs were relatively low, all within 11% for both protocols. However, doses to organs that were scanned partially or near the FOVs were affected significantly. For the chest protocol, the inclusion of the thyroid in the scan field could give a dose of 1-7 mGy/100 mAs to the thyroid, compared to 0.4-1 mGy/100 mAs when it was excluded. Similarly, on average, testes doses could be 6 mGy/100 mAs for the male pelvis protocol compared to 3 mGy/100 mAs when it did not lie in the field irradiated. These dose differences resulted in an average increase of up to 27% in the size-specific effective dose of the protocols. Since changing the field size is possible for CBCT scans, the results suggest that patient-specific scanning protocols could be applied for each scan area in a manner similar to that used for CT scans. Adjustment of the FOV size should be subject to the clinical needs, and assist in improving the treatment accuracy. The patient's height and weight might be considered as the main factors upon which, the selection of the appropriate patient-specific protocol is based. This approach should optimize the imaging doses used for IGRT procedures by minimizing doses of a large ratio of patients.

Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer

This study employed a commercial software velocity to perform deformable registration and dose calculation on deformed CT images, aiming to assess the accuracy of dose delivery during the radiotherapy for lung cancers. A total of 20 patients with lung cancer were enrolled in this study. Adaptive CT (ACT) was generated by deformed the planning CT (pCT) to the CBCT of initial radiotherapy fraction, followed by contour propagation and dose recalculation. There was not significant difference between volumes of GTV and CTV calculated from the ACT and pCT. However, significant differences in dice similarity coefficient (DSC) and coverage ratio (CR) between GTV and CTV were observed, with lower values for GTV volumes below 15 cc. The mean differences in dose corresponding to 95% of the GTV, GTV-P, CTV, and CTV-P between ACT and pCT were - 0.32%, 4.52%, 2.17%, and 4.71%, respectively. For the dose corresponding to 99%, the discrepancies were - 0.18%, 8.35%, 1.92%, and 24.96%, respectively. These differences in dose primarily appeared at the edges of the target areas. Notably, a significant enhancement of dose corresponding to 1 cc for spinal cord was observed in ACT, compared with pCT. There was no statistical difference in the mean dose of lungs and heart. In general, for lung cancer patients, anatomical motion may result in both CTV and GTV moving outside the original irradiation region. The dose difference within the original target area was small, but the difference in the planning target area was considerable.

Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

OBJECTIVES

Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically.

METHODS

A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively.

RESULTS

The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89-9.33% and 14.75-16.67%, respectively, while 95HD and ASD decreased by 33.16-67.81% and 36.05-75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective.

CONCLUSIONS

Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.

Synthetic CT generation based on CBCT using improved vision transformer CycleGAN

Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed method for CT image synthesis, it has notable limitations regarding the inadequate capture of global features. To tackle these challenges, we introduce a refined unsupervised learning model called improved vision transformer CycleGAN (IViT-CycleGAN). Firstly, we integrate a U-net framework that builds upon ViT. Next, we augment the feed-forward neural network by incorporating deep convolutional networks. Lastly, we enhance the stability of the model training process by introducing gradient penalty and integrating an additional loss term into the generator loss. The experiment demonstrates from multiple perspectives that our model-generated synthesizing CT(sCT) has significant advantages compared to other unsupervised learning models, thereby validating the clinical applicability and robustness of our model. In future clinical practice, our model has the potential to assist clinical practitioners in formulating precise radiotherapy plans.

Hypofractionated Radiotherapy in Gynecologic Malignancies-A Peek into the Upcoming Evidence

Radiotherapy (RT) has a fundamental role in the treatment of gynecologic malignancies, including cervical and uterine cancers. Hypofractionated RT has gained popularity in many cancer sites, boosted by technological advances in treatment delivery and image verification. Hypofractionated RT uptake was intensified during the COVID-19 pandemic and has the potential to improve universal access to radiotherapy worldwide, especially in low-resource settings. This review summarizes the rationale, the current challenges and investigation efforts, together with the recent developments associated with hypofractionated RT in gynecologic malignancies. A comprehensive search was undertaken using multiple databases and ongoing trial registries. In the definitive radiotherapy setting for cervical cancers, there are several ongoing clinical trials from Canada, Mexico, Iran, the Philippines and Thailand investigating the role of a moderate hypofractionated external beam RT regimen in the low-risk locally advanced population. Likewise, there are ongoing ultra and moderate hypofractionated RT trials in the uterine cancer setting. One Canadian prospective trial of stereotactic hypofractionated adjuvant RT for uterine cancer patients suggested a good tolerance to this treatment strategy in the acute setting, with a follow-up trial currently randomizing patients between conventional fractionation and the hypofractionated dose regimen delivered in the former trial. Although not yet ready for prime-time use, hypofractionated RT could be a potential solution to several challenges that limit access to and the utilization of radiotherapy for gynecologic cancer patients worldwide.

The Applications and Pitfalls of Cone-Beam Computed Tomography-Based Synthetic Computed Tomography for Adaptive Evaluation in Pencil-Beam Scanning Proton Therapy

PURPOSE

The study evaluates the efficacy of cone-beam computed tomography (CBCT)-based synthetic CTs (sCT) as a potential alternative to verification CT (vCT) for enhanced treatment monitoring and early adaptation in proton therapy.

METHODS

Seven common treatment sites were studied. Two sets of sCT per case were generated: direct-deformed (DD) sCT and image-correction (IC) sCT. The image qualities and dosimetric impact of the sCT were compared to the same-day vCT.

RESULTS

The sCT agreed with vCT in regions of homogeneous tissues such as the brain and breast; however, notable discrepancies were observed in the thorax and abdomen. The sCT outliers existed for DD sCT when there was an anatomy change and for IC sCT in low-density regions. The target coverage exhibited less than a 5% variance in most DD and IC sCT cases when compared to vCT. The Dmax of serial organ-at-risk (OAR) in sCT plans shows greater deviation from vCT than small-volume dose metrics (D0.1cc). The parallel OAR volumetric and mean doses remained consistent, with average deviations below 1.5%.

CONCLUSION

The use of sCT enables precise treatment and prompt early adaptation for proton therapy. The quality assurance of sCT is mandatory in the early stage of clinical implementation.

Assessment of dosimetric impact of interfractional 6D setup error in tongue cancer treated with IMRT and VMAT using daily kV-CBCT

Background

This study aimed to evaluate the dosimetric influence of 6-dimensional (6D) interfractional setup error in tongue cancer treated with intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) using daily kilovoltage cone-beam computed tomography (kV-CBCT).

Materials and methods

This retrospective study included 20 tongue cancer patients treated with IMRT (10), VMAT (10), and daily kV-CBCT image guidance. Interfraction 6D setup errors along the lateral, longitudinal, vertical, pitch, roll, and yaw axes were evaluated for 600 CBCTs. Structures in the planning CT were deformed to the CBCT using deformable registration. For each fraction, a reference CBCT structure set with no rotation error was created. The treatment plan was recalculated on the CBCTs with the rotation error (R_Error), translation error (T_Error), and translation plus rotation error (T+R_Error). For targets and organs at risk (OARs), the dosimetric impacts of R_Error, T_Error, and T+R_Error were evaluated without and with moderate correction of setup errors.

Results

The maximum dose variation ΔD (%) for D_98% in clinical target volumes (CTV): CTV-60, CTV-54, planning target volumes (PTV): PTV-60, and PTV-54 was -1.2%, -1.9%, -12.0%, and -12.3%, respectively, in the T+R_Error without setup error correction. The maximum ΔD (%) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54 was -1.0%, -1.7%, -9.2%, and -9.5%, respectively, in the T+R_Error with moderate setup error correction. The dosimetric impact of interfractional 6D setup errors was statistically significant (p < 0.05) for D_98% in CTV-60, CTV-54, PTV-60, and PTV-54.

Conclusions

The uncorrected interfractional 6D setup errors could significantly impact the delivered dose to targets and OARs in tongue cancer. That emphasized the importance of daily 6D setup error correction in IMRT and VMAT.

Fan beam CT-guided online adaptive external radiotherapy of uterine cervical cancer: a dosimetric evaluation

PURPOSE

To discuss the dosimetric advantages and reliability of the accurate delivery of online adaptive radiotherapy(online ART) for uterine cervical cancer(UCC).

METHODS AND MATERIALS

Six UCC patients were enrolled in this study. 95% of the planning target volume (PTV) reached 100% of the prescription dose (50.4 Gy/28fractions/6weeks) was required. The patients were scanned with uRT-Linac 506c KV-FBCT then the target volume (TV) and organs at risk (OARs) were delineated by doctors. The dosimeters designed and obtained a routine plan (Plan0). KV-FBCT was used for image guidance before subsequent fractional treatment. The online ART was processed after registration, which acquired a virtual nonadaptive radiotherapy plan (VPlan) and an adaptive plan (APlan). VPlan was the direct calculation of Plan0 on the fractional image, while APlan required adaptive optimization and calculation. In vivo dose monitoring and three-dimensional dose reconstruction were required during the implementation of APlan.

RESULTS

The inter-fractional volumes of the bladder and rectum changed greatly among the treatments. These changes influenced the primary gross tumor volume (GTVp) and the position deviation of GTVp and PTV and positively affected the prescription dose coverage of TV. GTVp decreased gradually along with dose accumulation. The Dmax, D98, D95, D50, and D2 of APlan were superior to those of VPlan in target dose distribution. APlan had good conformal index, homogeneity index and target coverage. The rectum V40 and Dmax, bladder V40, the small bowel V40 and Dmax of APlan were better than that of VPlan. The APlan's fractional mean γ passing rate was significantly higher than the international standard and the mean γ passing rate of all cases after the three-dimensional reconstruction was higher than 97.0%.

CONCLUSION

Online ART in external radiotherapy of UCC significantly improved the dose distribution and can become an ideal technology to achieve individualized precise radiotherapy.

Image-guided radiotherapy (IGRT) in Lombardy, Italy: a survey by the Lombardy section of the Italian Association of Radiotherapy and Clinical Oncology (AIRO-Lombardy)

BACKGROUND

Image-guided radiation therapy (IGRT) has changed clinical practice. We proposed a survey to radiotherapy centers in Lombardy to picture the current clinical practice of its use.

RESEARCH DESIGN AND METHODS

The survey consisted of 32 multiple-choice questions, divided into five topics: type of hospital, patients treated in 2019, number of LINACs; presence of protocols and staff involved in IGRT; IGRT in stereotaxis; IGRT in non-stereotactic treatments; availability of medical and technical staff.

RESULTS

Twenty-seven directors answered (77%). Most centers (74%) have produced protocols to ensure uniformity in the IGRT process. The most widely used IGRT modality (92%) is cone-beam CT. Daily IGRT control is favored for prostate (100%), head and neck (87%), and lung (78%) neoplasms. The resident doctors can always perform supervised IGRT matching in only six centers. Radiation therapists perform IGRT controls only for some sites in 12 cases (44%) and always in 9 cases (33%). Radiation oncologists are present in real time, in most cases.

CONCLUSIONS

Today, IGRT can be considered standard practice but at the price of more time-consuming procedures. A balance between a fully physician-controlled process and an increased role for specifically trained RTTs is actively being sought.

CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma

This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error (MAE) were used to quantify the image quality. For validations, the contours and treatment plans in CT images were transferred to original CBCT, CBCT_cor, SCT1 and SCT2 for dosimetric comparison. Dose distribution, dosimetric parameters and 3D gamma passing rate were analyzed. Compared with rigidly registered CT (RCT), the MAE of CBCT, CBCT_cor, SCT1 and SCT2 were 346.11 ± 13.58 HU, 145.95 ± 17.64 HU, 105.62 ± 16.08 HU and 83.51 ± 7.71 HU, respectively. Moreover, the average dosimetric parameter differences for the CBCT_cor, SCT1 and SCT2 were 2.7% ± 1.4%, 1.2% ± 1.0% and 0.6% ± 0.6%, respectively. Using the dose distribution of RCT images as reference, the 3D gamma passing rate of the hybrid method was significantly better than the other methods. The effectiveness of CBCT-based sCT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma was confirmed. The image quality and dose accuracy of SCT2 were outperform the simple CycleGAN method. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

New technique and application of truncated CBCT processing in adaptive radiotherapy for breast cancer

OBJECTIVE

A generative adversarial network (TCBCTNet) was proposed to generate synthetic computed tomography (sCT) from truncated low-dose cone-beam computed tomography (CBCT) and planning CT (pCT). The sCT was applied to the dose calculation of radiotherapy for patients with breast cancer.

METHODS

The low-dose CBCT and pCT images of 80 female thoracic patients were used for training. The CBCT, pCT, and replanning CT (rCT) images of 20 thoracic patients and 20 patients with breast cancer were used for testing. All patients were fixed in the same posture with a vacuum pad. The CBCT images were scanned under the Fast Chest M20 protocol with a 50% reduction in projection frames compared with the standard Chest M20 protocol. Rigid registration was performed between pCT and CBCT, and deformation registration was performed between rCT and CBCT. In the training stage of the TCBCTNet, truncated CBCT images obtained from complete CBCT images by simulation were used. The input of the CBCT→CT generator was truncated CBCT and pCT, and TCBCTNet was applied to patients with breast cancer after training. The accuracy of the sCT was evaluated by anatomy and dosimetry and compared with the generative adversarial network with UNet and ResNet as the generators (named as UnetGAN, ResGAN).

RESULTS

The three models could improve the image quality of CBCT and reduce the scattering artifacts while preserving the anatomical geometry of CBCT. For the chest test set, TCBCTNet achieved the best mean absolute error (MAE, 21.18±3.76 HU), better than 23.06±3.90 HU in UnetGAN and 22.47±3.57 HU in ResGAN. When applied to patients with breast cancer, TCBCTNet performance decreased, and MAE was 25.34±6.09 HU. Compared with rCT, sCT by TCBCTNet showed consistent dose distribution and subtle absolute dose differences between the target and the organ at risk. The 3D gamma pass rates were 98.98%±0.64% and 99.69%±0.22% at 2 mm/2% and 3 mm/3%, respectively. Ablation experiments confirmed that pCT and content loss played important roles in TCBCTNet.

CONCLUSIONS

High-quality sCT images could be synthesized from truncated low-dose CBCT and pCT by using the proposed TCBCTNet model. In addition, sCT could be used to accurately calculate the dose distribution for patients with breast cancer.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.

A markerless beam's eye view tumor tracking algorithm based on unsupervised deformable registration learning framework of VoxelMorph in medical image with partial data

PURPOSE

To propose an unsupervised deformable registration learning framework-based markerless beam's eye view (BEV) tumor tracking algorithm for the inferior quality megavolt (MV) images with occlusion and deformation.

METHODS

Quality assurance (QA) plans for thorax phantom were delivered to the linear accelerator with artificially treatment offsets. Electronic portal imaging device (EPID) images (682 in total) and corresponding digitally reconstructed radiograph (DRR) were gathered as the moving and fixed image pairs, which were randomly divided into training and testing set in a ratio of 0.7:0.3 to train a non-rigid registration model with Voxelmorph. Simultaneously, 533 pairs of patient images from 21 lung tumor plans were acquired for tumor tracking investigation to offer quantifiable tumor motion data. Tracking error and image similarity measures were employed to evaluate the algorithm's accuracy.

RESULTS

The tracking algorithm can handle image registration with non-rigid deformation and losses ranging from 10 % to 80 %. The tracking errors in the phantom study were below 3 mm in about 86.8 % of cases, and below 2 mm in about 80 % of cases. The normalized mutual information (NMI) changes from 1.182 ± 0.024 to 1.198 ± 0.024 (p < 0.005). The patient target had an average translation of 3.784 mm and a maximum comprehensive displacement value of 7.455 mm. NMI of patient images changes from 1.209 ± 0.027 to 1.217 ± 0.026 (p < 0.005), indicating the presence of non-negligible non-rigid deformation.

CONCLUSIONS

The study provides a robust markerless tumor tracking algorithm for multi-modal, partial data and inferior quality image processing.

Technical note: Characterization of novel iterative reconstructed cone beam CT images for dose tracking and adaptive radiotherapy on L-shape linacs

BACKGROUND

Cone-beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT).

PURPOSE

This study aims to characterize iCBCT image quality, establish image value (HU)-to-relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites.

MATERIAL AND METHODS

Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On-Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp-Davis-Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site-specific HU-RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK-based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison.

RESULTS

Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU-RED calibration from CT showed 0.5%-2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site-specific HU-RED calibration curves.

CONCLUSION

iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU-RED calibration curve from CT.

Deep learning-based low-dose CT for adaptive radiotherapy of abdominal and pelvic tumors

The shape and position of abdominal and pelvic organs change greatly during radiotherapy, so image-guided radiation therapy (IGRT) is urgently needed. The world’s first integrated CT-linac platform, equipped with fan beam CT (FBCT), can provide a diagnostic-quality FBCT for achieve adaptive radiotherapy (ART). However, CT scans will bring the risk of excessive scanning radiation dose. Reducing the tube current of the FBCT system can reduce the scanning dose, but it will lead to serious noise and artifacts in the reconstructed images. In this study, we proposed a deep learning method, Content-Noise Cycle-Consistent Generative Adversarial Network (CNCycle-GAN), to improve the image quality and CT value accuracy of low-dose FBCT images to meet the requirements of adaptive radiotherapy. We selected 76 patients with abdominal and pelvic tumors who received radiation therapy. The patients received one low-dose CT scan and one normal-dose CT scan in IGRT mode during different fractions of radiotherapy. The normal dose CT images (NDCT) and low dose CT images (LDCT) of 70 patients were used for network training, and the remaining 6 patients were used to validate the performance of the network. The quality of low-dose CT images after network restoration (RCT) were evaluated in three aspects: image quality, automatic delineation performance and dose calculation accuracy. Taking NDCT images as a reference, RCT images reduced MAE from 34.34 ± 5.91 to 20.25 ± 4.27, PSNR increased from 34.08 ± 1.49 to 37.23 ± 2.63, and SSIM increased from 0.92 ± 0.08 to 0.94 ± 0.07. The P value is less than 0.01 of the above performance indicators indicated that the difference were statistically significant. The Dice similarity coefficients (DCS) between the automatic delineation results of organs at risk such as bladder, femoral heads, and rectum on RCT and the results of manual delineation by doctors both reached 0.98. In terms of dose calculation accuracy, compared with the automatic planning based on LDCT, the difference in dose distribution between the automatic planning based on RCT and the automatic planning based on NDCT were smaller. Therefore, based on the integrated CT-linac platform, combined with deep learning technology, it provides clinical feasibility for the realization of low-dose FBCT adaptive radiotherapy for abdominal and pelvic tumors.

State-of-the-Art Imaging Techniques in Metastatic Spinal Cord Compression

Metastatic Spinal Cord Compression (MSCC) is a debilitating complication in oncology patients. This narrative review discusses the strengths and limitations of various imaging modalities in diagnosing MSCC, the role of imaging in stereotactic body radiotherapy (SBRT) for MSCC treatment, and recent advances in deep learning (DL) tools for MSCC diagnosis. PubMed and Google Scholar databases were searched using targeted keywords. Studies were reviewed in consensus among the co-authors for their suitability before inclusion. MRI is the gold standard of imaging to diagnose MSCC with reported sensitivity and specificity of 93% and 97% respectively. CT Myelogram appears to have comparable sensitivity and specificity to contrast-enhanced MRI. Conventional CT has a lower diagnostic accuracy than MRI in MSCC diagnosis, but is helpful in emergent situations with limited access to MRI. Metal artifact reduction techniques for MRI and CT are continually being researched for patients with spinal implants. Imaging is crucial for SBRT treatment planning and three-dimensional positional verification of the treatment isocentre prior to SBRT delivery. Structural and functional MRI may be helpful in post-treatment surveillance. DL tools may improve detection of vertebral metastasis and reduce time to MSCC diagnosis. This enables earlier institution of definitive therapy for better outcomes.

Synthetic CT generation based on CBCT using respath-cycleGAN

PURPOSE

Cone-beam computed tomography (CBCT) plays an important role in radiotherapy, but the presence of a large number of artifacts limits its application. The purpose of this study was to use respath-cycleGAN to synthesize CT (sCT) similar to planning CT (pCT) from CBCT for future clinical practice.

METHODS

The method integrates the respath concept into the original cycleGAN, called respath-cycleGAN, to map CBCT to pCT. Thirty patients were used for training, and 15 for testing.

RESULTS

The mean absolute error (MAE), root mean square error (RMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and spatial non-uniformity (SNU) were calculated to assess the quality of sCT generated from CBCT. Compared with CBCT images, the MAE improved from 197.72 to 140.7, RMSE from 339.17 to 266.51, and PSNR from 22.07 to 24.44, while SSIM increased from 0.948 to 0.964. Both visually and quantitatively, sCT with respath is superior to sCT without respath. We also performed a generalization test of the head-and-neck (H&N) model on a pelvic dataset. The results again showed that our model was superior.

CONCLUSION

We developed a respath-cycleGAN method to synthesize CT with good quality from CBCT. In future clinical practice, this method may be used to develop radiotherapy plans. This article is protected by copyright. All rights reserved.

A model of radiation-induced temporomandibular joint damage in mice

PURPOSE

A small animal radiation research platform (SARRP) equipped with a miniature beam system, an image-guided positioning system, and a dose planning system was used to develop and evaluate a mouse model of radiation-induced temporomandibular damage.

METHODS

Left jaw disks of adult male C57BL/6 mice and C3H mice were targeted using the SARRP for image-guided irradiation. The total radiation dose was 75 Gy. Experiment 1 (Scoping study): Mice in the C57BL/6 mouse test and control groups were sacrificed at 1, 3, 6, 9, 12, 15, and 18 weeks after irradiation, whereas mice in the C3H test and control groups were sacrificed at 1, 3, 6, 9, and 12 weeks after irradiation. Experiment 2 (Full -scale validation study): Mice in the C57BL/6 mouse test and control groups were sacrificed at 1, 3 and 6 weeks after irradiation. Histopathological analysis of the temporomandibular skeletal muscle in each group was performed using hematoxylin and eosin (H&E) and Masson staining; the temporal mandibular bone was examined through H&E staining.

RESULTS

SARRP delivered the rated dose to the temporomandibular joints of C57BL/6 and C3H mice. C3H and C57BL/6 mice in the test group showed different degrees of osteocytic necrosis and osteoporosis at different time points. H&E staining of skeletal muscle tissue showed slight fibrosis in the C57BL/6 test at 3 and 6 weeks time point.

CONCLUSION

We established a model of radiation-induced damage in the temporomandibular joint of C57BL/6 mice and demonstrated that the observed physiological and histological changes correspond to radiation damage observed in humans. Furthermore, the SARRP can deliver precise radiation doses.

Comparing treatment uncertainty for ultra- vs. standard-hypofractionated breast radiation therapy based on in-vivo dosimetry

Background and purpose

Postoperative ultrahypofractionated radiation therapy (UHFRT) in 5 fractions (fx) for breast cancer patients is as effective and safe as conventionally hypofractionated RT (HFRT) in 15 fx, liberating time for higher-level daily online Image-Guided Radiation Therapy (IGRT) corrections. In this retrospective study, treatment uncertainties occurring in patients treated with 5fx (5fx-group) were evaluated using electronic portal imaging device (EPID)-based in-vivo dosimetry (EIVD) and compared with the results from patients treated with conventionally HFRT (15fx-group) to validate the new technique and to evaluate if the shorter treatment schedule could have a positive effect on the treatment uncertainties.

Materials and methods

EPID-based integrated transit dose images were acquired for each treatment fraction in the 5fx-group (203 patients) and on the first 3 days of treatment and weekly thereafter in the 15fx-group (203 patients). A total of 1015 EIVD measurements in the 5fx-group and 1144 in the 15fx-group were acquired. Of the latter group, 755 had been treated with online IGRT correction (i.e., Online-IGRT 15fx-group).

Results

In the 15fx-group 12.0% of fractions failed (FFs) compared to 3.8% in the 5fx-group and 6.9% in the online-IGRT 15fx-group. Causes for FFs in the 15fx-group compared with the 5fx-group were patient positioning (7.4% vs. 2.2%), technical issues (3.1% vs. 1.2%) and breast swelling (1.4% vs. 0.5%). In the online-IGRT 15fx-group, 2.5% were attributed to patient positioning, 3.8% to technical issues and 0.5% to breast swelling.

Conclusions

EIVD demonstrated that UHFRT for breast cancer results in less FFs compared to standard HFRT. A large proportion of this decrease could be explained by using daily online IGRT.

Please Place Your Seat in the Full Upright Position: A Technical Framework for Landing Upright Radiation Therapy in the 21st Century

Delivering radiotherapy to patients in an upright position can allow for increased patient comfort, reduction in normal tissue irradiation, or reduction of machine size and complexity. This paper gives an overview of the requirements for the delivery of contemporary arc and modulated radiation therapy to upright patients. We explore i) patient positioning and immobilization, ii) simulation imaging, iii) treatment planning and iv) online setup and image guidance. Treatment chairs have been designed to reproducibly position seated patients for treatment and can be augmented by several existing immobilisation systems or promising emerging technologies such as soft robotics. There are few solutions for acquiring CT images for upright patients, however, cone beam computed tomography (CBCT) scans of upright patients can be produced using the imaging capabilities of standard Linacs combined with an additional patient rotation device. While these images will require corrections to make them appropriate for treatment planning, several methods indicate the viability of this approach. Treatment planning is largely unchanged apart from translating gantry rotation to patient rotation, allowing for a fixed beam with a patient rotating relative to it. Rotation can be provided by a turntable during treatment delivery. Imaging the patient with the same machinery as used in treatment could be advantageous for online plan adaption. While the current focus is using clinical linacs in existing facilities, developments in this area could also extend to lower-cost and mobile linacs and heavy ion therapy.

A novel analytical method for computing dose from kilovoltage beams used in Image-Guided radiation therapy

PURPOSE

A modified convolution/superposition algorithm is proposed to compute dose from the kilovoltage beams used in IGRT. The algorithm uses material-specific energy deposition kernels instead of water-energy deposition kernels.

METHODS

Monte Carlo simulation was used to model the Elekta XVI unit and determine dose deposition characteristics of its kilovoltage beams. The dosimetric results were compared with ion chamber measurements. The dose from the kilovoltage beams was then computed using convolution/superposition along with material-specific energy deposition kernels and compared with Monte Carlo and measurements. The material-specific energy deposition kernels were previously generated using Monte Carlo.

RESULTS

The obtained gamma indices (using 2%/2mm criteria for 95% of points) were lower than 1 in almost all instances which indicates good agreement between simulated and measured depth doses and profiles. The comparisons of the algorithm with measurements in a homogeneous solid water slab phantom, and that with Monte Carlo in a head and neck CT dataset produced acceptable results. The calculated point doses were within 4.2% of measurements in the homogeneous phantom. Gamma analysis of the calculated vs. Monte Carlo simulations in the head and neck phantom resulted in 94% of points passing with a 2%/2mm criteria.

CONCLUSIONS

The proposed method offers sufficient accuracy in kilovoltage beams dose calculations and has the potential to supplement the conventional megavoltage convolution/superposition algorithms for dose calculations in low energy range.

Evaluation of kV-CBCT based 3D dose calculation accuracy and its validation using delivery fluence derived dose metrics in Head and Neck Cancer

PURPOSE

The purpose of this study is to evaluate the dosimetric impact of Hounsfield unit (HU) variations in kilovoltage cone-beam computed tomography (kV-CBCT) based 3D dose calculation accuracy in the treatment planning system and its validation using measured treatment delivery dose (MTDD) derived dose metrics for Volumetric Modulated Arc Therapy (VMAT) and Intensity Modulated Radiotherapy (IMRT) plans in Head and Neck (HN) Cancer.

METHODS

CBCT dose calculation accuracy was evaluated for 8 VMAT plans on inhomogeneous phantom and 40 VMAT and IMRT plans of HN Cancer patients and validated using ArcCHECK diode array MTDD derived 3D dose metric on CT and CBCT.

RESULTS

The mean percentage dose difference between CBCT and CT in TPS (ΔD(CBCT-CT)_TPS) and 3DVH (ΔD(CBCT-CT)_3DVH) were compared for the corresponding evaluation dose metrics (D_98%, D_95%, D_50%, D_2%, D_max, D_1cc, D_0.03cc, D_mean) of all PTVs and OARs in phantom and patients. ΔD(CBCT-CT)_TPS and ΔD(CBCT-CT)_3DVH for all evaluation dose points of all PTVs and OARs were less than 2.55% in phantom and 2.4% in HN patients. The Pearson correlation coefficient (r) between ΔD(CBCT-CT)_TPS and ΔD(CBCT-CT)_3DVH for all dose points in all PTVs and OARs showed a strong to moderate correlation in phantom and patients with p < 0.001.

CONCLUSIONS

This study evaluated and validated the potential feasibility of kV-CBCT for treatment plan 3D dose reconstruction in clinical decision making for Adaptive radiotherapy on CT in Head and Neck cancer.

Stereotactic Body Radiation Therapy in Patients with Oligometastatic Disease: Clinical State of the Art and Perspectives

Stereotactic body radiation therapy (SBRT) is a form of radiation therapy (RT) in which a small number of high doses of radiation are delivered to a target volume using highly sophisticated equipment. Stereotactic body radiation therapy is crucial in two cancer stages: early primary cancer and oligometastatic disease, with the goal of inducing complete cancer remission in both. This treatment method is commonly used to treat a variety of disease types. Over the years, a growing body of clinical evidence on the use of SBRT for the treatment of primary and metastatic tumors has accumulated, with efficacy and safety demonstrated in randomized clinical trials. This article will review the technical and clinical aspects of SBRT according to disease type and clinical indication.

Dose assessment for daily cone-beam CT in lung radiotherapy patients and its combination with treatment planning

With the increased use of X-ray imaging for patient alignment in external beam radiation therapy, particularly with cone-beam computed tomography (CBCT), the additional dose received by patients has become of greater consideration. In this study, we analysed the radiation dose from CBCT for clinical lung radiotherapy and assessed its relative contribution when combined with radiation treatment planning for a variety of lung radiotherapy techniques. The Monte Carlo simulation program ImpactMC was used to calculate the 3D dose delivered by a Varian TrueBeam linear accelerator to patients undergoing thorax CBCT imaging. The concomitant dose was calculated by simulating the daily CBCT irradiation of ten lung cancer patients. Each case was planned with a total dose of 50-60 Gy to the target lesion in 25-30 fractions using the 3DCRT or IMRT plan and retrospectively planned using VMAT. For each clinical case, the calculated CBCT dose was summed with the planned dose, and the dose to lungs, heart, and spinal cord were analysed according to conventional dose conformity metrics. Our results indicate greater variations in dose to the heart, lungs, and spinal cord based on planning technique, (3DCRT, IMRT, VMAT) than from the inclusion of daily cone-beam imaging doses over 25-30 fractions. The average doses from CBCT imaging per fraction to the lungs, heart and spinal cord were 0.52 ± 0.10, 0.49 ± 0.15 and 0.39 ± 0.08 cGy, respectively. Lung dose variations were related to the patient's size and body composition. Over a treatment course, this may result in an additional mean absorbed dose of 0.15-0.2 Gy. For lung V5, the imaging dose resulted in an average increase of ~ 0.6% of the total volume receiving 5 Gy. The increase in V20 was more dependent on the planning technique, with 3DCRT increasing by 0.11 ± 0.09% with imaging and IMRT and VMAT increasing by 0.17 ± 0.05% and 0.2 ± 0.06%, respectively. In this study, we assessed the concomitant dose for daily CBCT lung cancer patients undergoing radiotherapy. The additional radiation dose to the normal lungs from daily CBCT was found to range from 0.15 to 0.2 Gy when the patient was treated with 25-30 fractions. Consideration of potential variation in relative biological effectiveness between kilovoltage imaging and megavoltage treatment dose was outside the scope of this study. Regardless of this, our results show that the assessment of imaging dose can be incorporated into the treatment planning process and the relative effect on overall dose distribution was small compared to the difference among planning techniques.

Multi-resolution residual deep neural network for improving pelvic CBCT image quality

PURPOSE

Cone-beam computed tomography (CBCT) is frequently used for accurate image guided radiation therapy (IGRT). However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images.

METHODS

In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multi-resolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training, 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models.

RESULTS

The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multi-resolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure (SSIM) between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT.

CONCLUSIONS

The sCT images generated using our proposed multi-resolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation and adaptive radiotherapy planning. This article is protected by copyright. All rights reserved.

Evaluation of plan robustness on the dosimetry of volumetric arc radiotherapy (VMAT) with set-up uncertainty in Nasopharyngeal carcinoma (NPC) radiotherapy

Purpose

To evaluate the sensitivity to set up the uncertainty of VMAT plans in Nasopharyngeal carcinoma (NPC) treatment by proposing a plan robustness evaluation method.

Methods

10 patients were selected for this study. A 2-arc volumetric-modulated arc therapy (VMAT) plan was generated for each patient using Varian Eclipse (13.6 Version) treatment planning system (TPS). 5 uncertainty plans (U-plans) were recalculated based on the first 5 times set-up errors acquired from cone-beam computer tomography (CBCT). The dose differences of the original plan and perturbed plan corresponded to the plan robustness for the structure. Tumor control probability (TCP) and normal tissues complication probability (NTCP) were calculated for biological evaluation.

Results

The mean dose differences of D_98% and D_95% (ΔD_98% and ΔD_95%) of PTVp were respectively 3.30 Gy and 2.02 Gy. The ΔD_98% and ΔD_95% of CTVp were 1.12 Gy and 0.58 Gy. The ΔD_98% and ΔD_95% of CTVn were 1.39 Gy and 1.03 Gy, distinctively lower than those in PTVn (2.8 Gy and 2.0 Gy). The CTV-to-PTV margin increased the robustness of CTVs. The ΔD_98% and ΔD_95% of GTVp were 0.56 Gy and 0.33 Gy. GTVn exhibited strong robustness with little variation of D_98% (0.64 Gy) and D_95% (0.39 Gy). No marked mean dose variations of D_mean were seen. The mean reduction of TCP (ΔTCP) in GTVp and CTVp were respectively 0.4% and 0.3%. The mean ΔTCPs of GTVn and CTVn were 0.92% and 1.3% respectively. The CTV exhibited the largest ΔTCP (2.2%). In OARs, the brain stem exhibited weak robustness due to their locations in the vicinity of PTV. Bilateral parotid glands were sensitive to set-up uncertainty with a mean reduction of NTCP (ΔNTCP) of 6.17% (left) and 7.70% (right). The D_max of optical nerves and lens varied slightly.

Conclusion

VMAT plans had a strong sensitivity to set-up uncertainty in NPC radiotherapy, with increasing risk of underdose of tumor and overdose of vicinal OARs. We proposed an effective method to evaluate the plan robustness of VMAT plans. Plan robustness and complexity should be taken into account in photon radiotherapy.

Generating synthesized computed tomography from CBCT using a conditional generative adversarial network for head and neck cancer patients

Purpose: To overcome the imaging artifacts and Hounsfield unit inaccuracy limitations of cone-beam computed tomography, a conditional generative adversarial network is proposed to synthesize high-quality computed tomography-like images from cone-beam computed tomography images. Methods: A total of 120 paired cone-beam computed tomography and computed tomography scans of patients with head and neck cancer who were treated during January 2019 and December 2020 retrospectively collected; the scans of 90 patients were assembled into training and validation datasets, and the scans of 30 patients were used in testing datasets. The proposed method integrates a U-Net backbone architecture with residual blocks into a conditional generative adversarial network framework to learn a mapping from cone-beam computed tomography images to pair planning computed tomography images. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio were used to assess the performance of this method compared with U-Net and CycleGAN. Results: The synthesized computed tomography images produced by the conditional generative adversarial network were visually similar to planning computed tomography images. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio calculated from test images generated by conditional generative adversarial network were all significantly different than CycleGAN and U-Net. The mean absolute error, root-mean-square error, structural similarity index, and peak signal-to-noise ratio values between the synthesized computed tomography and the reference computed tomography were 16.75 ± 11.07 Hounsfield unit, 58.15 ± 28.64 Hounsfield unit, 0.92 ± 0.04, and 30.58 ± 3.86 dB in conditional generative adversarial network, 20.66 ± 12.15 Hounsfield unit, 66.53 ± 29.73 Hounsfield unit, 0.90 ± 0.05, and 29.29 ± 3.49 dB in CycleGAN, and 16.82 ± 10.99 Hounsfield unit, 58.68 ± 28.34 Hounsfield unit, 0.92 ± 0.04, and 30.48 ± 3.83 dB in U-Net, respectively. Conclusions: The synthesized computed tomography generated from the cone-beam computed tomography-based conditional generative adversarial network method has accurate computed tomography numbers while keeping the same anatomical structure as cone-beam computed tomography. It can be used effectively for quantitative applications in radiotherapy.

Planning CT-guided robust and fast cone-beam CT scatter correction using a local filtration technique

PURPOSE

Cone-beam CT (CBCT) has been widely utilized in image-guided radiotherapy. Planning CT (pCT)-aided CBCT scatter correction could further enhance image quality and extend CBCT application to dose calculation and adaptive planning. Nevertheless, existing pCT-based approaches demand accurate registration between pCT and CBCT, leading to limited imaging performance and increased computational cost when large anatomical discrepancies exist. In this work, we proposed a robust and fast CBCT scatter correction method using local filtration technique and rigid registration between pCT and CBCT (LF-RR).

METHODS

First of all, the pCT was rigidly registered with CBCT, then forward projection was performed on registered pCT to create scatter-free projections. The raw scatter signals were obtained via subtracting the scatter-free projections from the measured CBCT projections. Based on frequency and intensity threshold criteria, reliable scatter signals were selected from the raw scatter signals, and further filtered for global scatter estimation via local filtration technique. Finally, corrected CBCT was reconstructed with the projections generated by subtracting the scatter estimation from the raw CBCT projections using FDK algorithm. The LF-RR method was evaluated via comparison with another pCT-based scatter correction method based on Median and Gaussian filters (MG method).

RESULTS

Proposed method was first validated on an anthropomorphic pelvis phantom, and showed satisfied performance on scatter removal even when anatomical mismatches were intentionally created on pCT. The quantitative analysis was further performed on four clinical CBCT images. Compared with the uncorrected CBCT, CBCT corrected by MG with rigid registration (MG-RR), MG with deformable registration (MG-DR), and LF-RR reduced the CT number error from 79 ± 35 to 25 ± 18 , 17 ± 13 and 7 ± 3 HU for adipose and from 115 ± 61 to 36 ± 22 , 30 ± 24 , 7 ± 3 HU for muscle, respectively. After correction, the spatial non-uniformity (SNU) of CBCT corrected with MG-RR, MG-DR and LF-RR was 51 ± 13 , 60 ± 21 , and 21 ± 9 HU for adipose, and 50 ± 22 , 57 ± 41 , and 25 ± 6 HU for muscle, respectively. Meanwhile, the contrast-to-noise ratio (CNR) between muscle and adipose was increased by a factor of 2.70, 2.89 and 2.56, respectively. Using the LF-RR method, the scatter correction of 656 projections can be finished within 10 s and the corrected volumetric images (200 slices) can be obtained within 2 min.

CONCLUSION

We developed a fast and robust pCT-based CBCT scatter correction method which exploits the local-filtration technique to promote the accuracy of scatter estimation and is resistant to pCT-to-CBCT registration uncertainties. Both phantom and patient studies showed the superiority of the proposed correction in imaging accuracy and computational efficiency, indicating promisingfuture clinical application.

Generating synthetic CT from low-dose cone-beam CT by using generative adversarial networks for adaptive radiotherapy

OBJECTIVE

To develop high-quality synthetic CT (sCT) generation method from low-dose cone-beam CT (CBCT) images by using attention-guided generative adversarial networks (AGGAN) and apply these images to dose calculations in radiotherapy.

METHODS

The CBCT/planning CT images of 170 patients undergoing thoracic radiotherapy were used for training and testing. The CBCT images were scanned under a fast protocol with 50% less clinical projection frames compared with standard chest M20 protocol. Training with aligned paired images was performed using conditional adversarial networks (so-called pix2pix), and training with unpaired images was carried out with cycle-consistent adversarial networks (cycleGAN) and AGGAN, through which sCT images were generated. The image quality and Hounsfield unit (HU) value of the sCT images generated by the three neural networks were compared. The treatment plan was designed on CT and copied to sCT images to calculated dose distribution.

RESULTS

The image quality of sCT images by all the three methods are significantly improved compared with original CBCT images. The AGGAN achieves the best image quality in the testing patients with the smallest mean absolute error (MAE, 43.5 ± 6.69), largest structural similarity (SSIM, 93.7 ± 3.88) and peak signal-to-noise ratio (PSNR, 29.5 ± 2.36). The sCT images generated by all the three methods showed superior dose calculation accuracy with higher gamma passing rates compared with original CBCT image. The AGGAN offered the highest gamma passing rates (91.4 ± 3.26) under the strictest criteria of 1 mm/1% compared with other methods. In the phantom study, the sCT images generated by AGGAN demonstrated the best image quality and the highest dose calculation accuracy.

CONCLUSIONS

High-quality sCT images were generated from low-dose thoracic CBCT images by using the proposed AGGAN through unpaired CBCT and CT images. The dose distribution could be calculated accurately based on sCT images in radiotherapy.

Deep learning based synthetic-CT generation in radiotherapy and PET: A review

Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods.

Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients

Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.

Multitask 3D CBCT-to-CT translation and organs-at-risk segmentation using physics-based data augmentation

PURPOSE

In current clinical practice, noisy and artifact-ridden weekly cone beam computed tomography (CBCT) images are only used for patient setup during radiotherapy. Treatment planning is performed once at the beginning of the treatment using high-quality planning CT (pCT) images and manual contours for organs-at-risk (OARs) structures. If the quality of the weekly CBCT images can be improved while simultaneously segmenting OAR structures, this can provide critical information for adapting radiotherapy mid-treatment as well as for deriving biomarkers for treatment response.

METHODS

Using a novel physics-based data augmentation strategy, we synthesize a large dataset of perfectly/inherently registered pCT and synthetic-CBCT pairs for locally advanced lung cancer patient cohort, which are then used in a multitask three-dimensional (3D) deep learning framework to simultaneously segment and translate real weekly CBCT images to high-quality pCT-like images.

RESULTS

We compared the synthetic CT and OAR segmentations generated by the model to real pCT and manual OAR segmentations and showed promising results. The real week 1 (baseline) CBCT images which had an average mean absolute error (MAE) of 162.77 HU compared to pCT images are translated to synthetic CT images that exhibit a drastically improved average MAE of 29.31 HU and average structural similarity of 92% with the pCT images. The average DICE scores of the 3D OARs segmentations are: lungs 0.96, heart 0.88, spinal cord 0.83, and esophagus 0.66.

CONCLUSIONS

We demonstrate an approach to translate artifact-ridden CBCT images to high-quality synthetic CT images, while simultaneously generating good quality segmentation masks for different OARs. This approach could allow clinicians to adjust treatment plans using only the routine low-quality CBCT images, potentially improving patient outcomes. Our code, data, and pre-trained models will be made available via our physics-based data augmentation library, Physics-ArX, at https://github.com/nadeemlab/Physics-ArX.

Automated algorithm for calculation of setup corrections and planning target volume margins for offline image-guided radiotherapy protocols

Purpose

Each radiotherapy center should have a site‐specific planning target volume (PTV) margins and image‐guided (IG) radiotherapy (IGRT) correction protocols to compensate for the geometric errors that can occur during treatment. This study developed an automated algorithm for the calculation and evaluation of these parameters from cone beam computed tomography (CBCT)‐based IG‐intensity modulated radiotherapy (IG‐IMRT) treatment.

Methods and materials

A MATLAB algorithm was developed to extract the setup errors in three translational directions (x, y, and z) from the data logged by the CBCT system during treatment delivery. The algorithm also calculates the resulted population setup error and PTV margin based on the van Herk margin recipe and subsequently estimates their respective values for no action level (NAL) and extended no action level (eNAL) offline correction protocols. The algorithm was tested on 25 head and neck cancer (HNC) patients treated using IG‐IMRT.

Results

The algorithms calculated that the HNC patients require a PTV margin of 3.1, 2.7, and 3.2 mm in the x‐, y‐, and z‐direction, respectively, without IGRT. The margin can be reduced to 2.0, 2.2, and 3.0 mm in the x‐, y‐, and z‐direction, respectively, with NAL and 1.6, 1.7, and 2.2 mm in the x‐, y‐, and z‐direction, respectively, with eNAL protocol. The results obtained were verified to be the same with the margins calculated using an Excel spreadsheet. The algorithm calculates the weekly offline setup error correction values automatically and reduces the risk of input data error observed in the spreadsheet.

Conclusions

In conclusion, the algorithm provides an automated method for optimization and reduction of PTV margin using logged setup errors from CBCT‐based IGRT.

A feasibility study to estimate optimal rigid-body registration using combinatorial rigid registration optimization (CORRO)

Purpose

Clinical image pairs provide the most realistic test data for image registration evaluation. However, the optimal registration is unknown. Using combinatorial rigid registration optimization (CORRO) we demonstrate a method to estimate the optimal alignment for rigid‐registration of clinical image pairs.

Methods

Expert selected landmark pairs were selected for each CT/CBCT image pair for six cases representing head and neck, thoracic, and pelvic anatomic regions. Combination subsets of a k number of landmark pairs (k‐combination set) were generated without repeat to form a large set of k‐combination sets (k‐set) for k = 4,8,12. The rigid transformation between the image pairs was calculated for each k‐combination set. The mean and standard deviation of these transformations were used to derive final registration for each k‐set.

Results

The standard deviation of registration output decreased as the k‐size increased for all cases. The joint entropy evaluated for each k‐set of each case was smaller than those from two commercially available registration programs indicating a stronger correlation between the image pair after CORRO was used. A joint histogram plot of all three algorithms showed high correlation between them. As further proof of the efficacy of CORRO the joint entropy of each member of 30 000 k‐combination sets in k = 4 were calculated for one of the thoracic cases. The minimum joint entropy was found to exist at the estimated mean of registration indicating CORRO converges to the optimal rigid‐registration results.

Conclusions

We have developed a methodology called CORRO that allows us to estimate optimal alignment for rigid‐registration of clinical image pairs using a large set landmark point. The results for the rigid‐body registration have been shown to be comparable to results from commercially available algorithms for all six cases. CORRO can serve as an excellent tool that can be used to test and validate rigid registration algorithms.

Simple calculation using anatomical features on pre-treatment verification CT for bladder volume estimation during radiation therapy for rectal cancer

Background

Despite detailed instruction for full bladder, patients are unable to maintain consistent bladder filling during a 5-week pelvic radiation therapy (RT) course. We investigated the best bladder volume estimation procedure for verifying consistent bladder volume.

Methods

We reviewed 462 patients who underwent pelvic RT. Biofeedback using a bladder scanner was conducted before simulation and during treatment. Exact bladder volume was calculated by bladder inner wall contour based on CT images (V_ctsim). Bladder volume was estimated either by bladder scanner (V_scan) or anatomical features from the presacral promontory to the bladder base and dome in the sagittal plane of CT (V_ratio). The feasibility of V_ratio was validated using daily megavoltage or kV cone-beam CT before treatment.

Results

Mean V_ctsim was 335.6 ± 147.5 cc. Despite a positive correlation between V_ctsim and V_scan (R² = 0.278) and between V_ctsim and V_ratio (R² = 0.424), V_ratio yielded more consistent results than V_scan, with a mean percentage error of 26.3 (SD 19.6, p < 0.001). The correlation between V_ratio and V_ctsim was stronger than that between V_scan and V_ctsim (Z-score: − 7.782, p < 0.001). An accuracy of V_ratio was consistent in megavoltage or kV cone-beam CT during treatment. In a representative case, we can dichotomize for clinical scenarios with or without bowel displacement, using a ratio of 0.8 resulting in significant changes in bowel volume exposed to low radiation doses.

Conclusions

Bladder volume estimation using personalized anatomical features based on pre-treatment verification CT images was useful and more accurate than physician-dependent bladder scanners.

Trial registration

Retrospectively registered.

Comparison of radiographer interobserver image registration variability using cone beam CT and MR for cervix radiotherapy

OBJECTIVES

The aim of this study was to assess the consistency of therapy radiographers performing image registration using cone beam computed tomography (CBCT)-CT, magnetic resonance (MR)-CT, and MR-MR image guidance for cervix cancer radiotherapy and to assess that MR-based image guidance is not inferior to CBCT standard practice.

METHODS

10 patients receiving cervix radiation therapy underwent daily CBCT guidance and magnetic resonance (MR) imaging weekly during treatment. Offline registration of each MR image, and corresponding CBCT, to planning CT was performed by five radiographers. MR images were also registered to the earliest MR interobserver variation was assessed using modified Bland-Altman analysis with clinically acceptable 95% limits of agreement (LoA) defined as ±5.0 mm.

RESULTS

30 CBCT-CT, 30 MR-CT and 20 MR-MR registrations were performed by each observer. Registration variations between CBCT-CT and MR-CT were minor and both strategies resulted in 95% LoA over the clinical threshold in the anteroposterior direction (CBCT-CT ±5.8 mm, MR-CT ±5.4 mm). MR-MR registrations achieved a significantly improved 95% LoA in the anteroposterior direction (±4.3 mm). All strategies demonstrated similar results in lateral and longitudinal directions.

CONCLUSION

The magnitude of interobserver variations between CBCT-CT and MR-CT were similar, confirming that MR-CT radiotherapy workflows are comparable to CBCT-CT image-guided radiotherapy. Our results suggest MR-MR radiotherapy workflows may be a superior registration strategy.

ADVANCES IN KNOWLEDGE

This is the first publication quantifying interobserver registration of multimodality image registration strategies for cervix radical radiotherapy patients.

Impact of hydrogel peri-rectal spacer insertion on prostate gland intra-fraction motion during 1.5 T MR-guided stereotactic body radiotherapy

Background

The assessment of organ motion is a crucial feature for prostate stereotactic body radiotherapy (SBRT). Rectal spacer may represent a helpful device in order to outdistance rectal wall from clinical target, but its impact on organ motion is still a matter of debate. MRI-Linac is a new frontier in radiation oncology as it allows a superior visualization of the real-time anatomy of the patient and the current highest level of adaptive radiotherapy.

Methods

We present data regarding a total of 100 fractions in 20 patients who underwent MRI-guided prostate SBRT for low-to-intermediate risk prostate cancer with or without spacer. Translational and rotational shifts were computed on the pre- and post-treatment MRI acquisitions referring to the delivery position for antero-posterior, latero-lateral and cranio-caudal direction, and assessed using the Mann-Whitney U-Test.

Results

All patients were treated with a five sessions schedule (35 Gy/5fx) using MRI-Linac for a median fraction treatment time of 50 min (range, 46–65). In the entire study sample, median rotational displacement was 0.1° in cranio-caudal, − 0.002° in latero-lateral and 0.01° in antero-posterior direction; median translational shift was 0.11 mm in cranio-caudal, − 0.24 mm in latero-lateral and − 0.22 mm in antero-posterior. A significant difference between spacer and no-spacer patients in terms of rotational shifts in the antero-posterior direction (p = 0.033) was observed; also for translational shifts a positive trend was detected in antero-posterior direction (p = 0.07), although with no statistical significance. We observed statistically significant differences in the pre-treatment planning phase in favor of the spacer cohort for several rectum dose constraints: rectum V32Gy < 5% (p = 0.001), V28 Gy < 10% (p = 0.001) and V18Gy < 35% (p = 0.039). Also for bladder V35 Gy < 1 cc, the use of spacer provided a dosimetric advantage compared to the no-spacer subpopulation (p = 0.04). Furthermore, PTV V33.2Gy > 95% was higher in the spacer cohort compared to the no-spacer one (p = 0.036).

Conclusion

In our experience, the application of rectal hydrogel spacer for prostate SBRT resulted in a significant impact on rotational antero-posterior shifts contributing to limit prostate intra-fraction motion. Further studies with larger sample size and longer follow-up are required to confirm this ideally favorable effect and to assess any potential impact on clinical outcomes.

A single neural network for cone-beam computed tomography-based radiotherapy of head-and-neck, lung and breast cancer

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A deep learning network facilitated dose calculation from CBCT.

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A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

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Generation of synthetic-CT can be achieved within 10 s, facilitating online adaptive radiotherapy scenarios.

Background and purpose Adaptive radiotherapy based on cone-beam computed tomography (CBCT) requires high CT number accuracy to ensure accurate dose calculations. Recently, deep learning has been proposed for fast CBCT artefact corrections on single anatomical sites. This study investigated the feasibility of applying a single convolutional network to facilitate dose calculation based on CBCT for head-and-neck, lung and breast cancer patients.

Materials and Methods Ninety-nine patients diagnosed with head-and-neck, lung or breast cancer undergoing radiotherapy with CBCT-based position verification were included in this study. The CBCTs were registered to planning CT according to clinical procedures. Three cycle-consistent generative adversarial networks (cycle-GANs) were trained in an unpaired manner on 15 patients per anatomical site generating synthetic-CTs (sCTs). Another network was trained with all the anatomical sites together. Performances of all four networks were compared and evaluated for image similarity against rescan CT (rCT). Clinical plans were recalculated on rCT and sCT and analysed through voxel-based dose differences and γ-analysis.

Results A sCT was generated in 10 s. Image similarity was comparable between models trained on different anatomical sites and a single model for all sites. Mean dose differences <0.5% were obtained in high-dose regions. Mean gamma (3%, 3 mm) pass-rates >95% were achieved for all sites.

Conclusion Cycle-GAN reduced CBCT artefacts and increased similarity to CT, enabling sCT-based dose calculations. A single network achieved CBCT-based dose calculation generating synthetic CT for head-and-neck, lung, and breast cancer patients with similar performance to a network specifically trained for each anatomical site.

Set-up error and dosimetric analysis of HexaPOD evo RT 6D couch combined with cone beam CT image-guided intensity-modulated radiotherapy for primary malignant tumor of the cervical spine

Purpose

To investigate the set‐up error and consequent dosimetric change in HexaPOD evo RT 6D couch under image‐guided intensity‐modulated radiotherapy (IG‐IMRT) for primary malignant tumor of the cervical spine.

Methods

Ten cases with primary malignant tumor of the cervical spine were treated with intensity‐modulated radiotherapy (IMRT) in our hospital from August 2013 to November 2014. The X‐ray volumetric images (XVI) were scanned and obtained by cone‐beam CT (CBCT). The six directions (6D) of set‐up errors of translation and rotation were obtained by planned CT image registration. HexaPOD evo RT 6D couch made online correction of the set‐up error, and then the CBCT was conducted to obtain the residual error.

Results

We performed set‐up error and dosimetric analysis. First, for the set‐up error analysis, the average error in three translation directions of 6D set‐up error of the primary tumor of the cervical spine was <2 mm, whereas the single maximum error (absolute value) is 7.0 mm. Among average errors of rotation direction, Rotation X (RX) direction 0.67° ± 0.04°, Rotation Y (RY) direction 1.06° ± 0.06°, Rotation Z (RZ) direction 0.78° ± 0.05°; and the single maximum error in three rotation directions were 2.8°, 3.8°, and 2.9°, respectively. On three directions (X, Y, Z axis), the extended distance from clinical target volume (CTV) to planning target volume (PTV) was 3.45, 3.17, and 3.90 mm by calculating, respectively. Then, for the dosimetric analysis, the parameters, including plan sum PTV D98 and D95, planning gross tumor volume D98 and D95, V100% of the plan sum were significantly lower than the treatment plan. Moreover, Dmax of the spinal cord was significantly higher than the treatment plan.

Conclusion

6D set‐up error correction system should be used for accurate position calibration of precise radiotherapy for patients with malignant tumor of the cervical spine.

Synthetic CT generation from CBCT images via deep learning

PURPOSE

Cone-beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on-treatment CBCT) for accurate patient setup in image-guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U-net-based approach that synthesizes CT-like images with accurate numbers from planning CT, while keeping the same anatomical structure as on-treatment CBCT.

METHODS

We formulated the CT synthesis problem under a deep learning framework, where a deep U-net architecture was used to take advantage of the anatomical structure of on-treatment CBCT and image intensity information of planning CT. U-net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U-net (sCTU-net), we include on-treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU-net, we use another seven independent patient cases (560 slices) for testing.

RESULTS

We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same-day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU.

CONCLUSIONS

The proposed sCTU-net can synthesize CT-quality images with accurate CT numbers from on-treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

Evaluation of a commercial DIR platform for contour propagation in prostate cancer patients treated with IMRT/VMAT

Purpose

To assess the performance and limitations of contour propagation with three commercial deformable image registration (DIR) algorithms using fractional scans of CT‐on‐rails (CTOR) and Cone Beam CT (CBCT) in image guided prostate therapy patients treated with IMRT/VMAT.

Methods

Twenty prostate cancer patients treated with IMRT/VMAT were selected for analysis. A total of 453 fractions across those patients were analyzed. Image data were imported into MIM (MIM Software, Inc., Cleveland, OH) and three DIR algorithms (DIR Profile, normalized intensity‐based (NIB) and shadowed NIB DIR algorithms) were applied to deformably register each fraction with the planning CT. Manually drawn contours of bladder and rectum were utilized for comparison against the DIR propagated contours in each fraction. Four metrics were utilized in the evaluation of contour similarity, the Hausdorff Distance (HD), Mean Distance to Agreement (MDA), Dice Similarity Coefficient (DSC), and Jaccard indices. A subfactor analysis was performed per modality (CTOR vs. CBCT) and time (fraction). Point estimates and 95% confidence intervals were assessed via a Linear Mixed Effect model for the contour similarity metrics.

Results

No statistically significant differences were observed between the DIR Profile and NIB algorithms. However, statistically significant differences were observed between the shadowed NIB and NIB algorithms for some of the DIR evaluation metrics. The Hausdorff Distance calculation showed the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 14.82 mm vs. 8.34 mm for bladder and 15.87 mm vs. 11 mm for rectum, respectively. Similarly, the Mean Distance to Agreement calculation comparing the NIB propagated contours vs. shadowed NIB propagated contours against the manual contours were 2.43 mm vs. 0.98 mm for bladder and 2.57 mm vs. 1.00 mm for rectum, respectively. The Dice Similarity Coefficients comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.844 against 0.936 for bladder and 0.772 against 0.907 for rectum, respectively. The Jaccard indices comparing the NIB propagated contours and shadowed NIB propagated contours against the manual contours were 0.749 against 0.884 for bladder and 0.637 against 0.831 for rectum, respectively. The shadowed NIB DIR, which showed the closest agreement with the manual contours performed significantly better than the DIR Profile in all the comparisons. The OAR with the greatest agreement varied substantially across patients and image guided radiation therapy (IGRT) modality. Intra‐patient variability of contour metric evaluation was insignificant across all the DIR algorithms. Statistical significance at α = 0.05 was observed for manual vs. deformably propagated contours for bladder for all the metrics except Hausdorff Distance (P = 0.01 for MDA, P = 0.02 for DSC, P = 0.01 for Jaccard), whereas the corresponding values for rectum were: P = 0.03 for HD, P = 0.01 for MDA, P < 0.01 for DSC, P < 0.01 for Jaccard. The performance of the different metrics varied slightly across the fractions of each patient, which indicates that weekly contour propagation models provide a reasonable approximation of the daily contour propagation models.

Conclusion

The high variance of Hausdorff Distance across all automated methods for bladder indicates widely variable agreement across fractions for all patients. Lower variance across all modalities, methods, and metrics were observed for rectum. The shadowed NIB propagated contours were substantially more similar to the manual contours than the DIR Profile or NIB contours for both the CTOR and CBCT imaging modalities. The relationship of each algorithm to similarity with manual contours is consistent across all observed metrics and organs. Screening of image guidance for substantial differences in bladder and rectal filling compared with the planning CT reference could aid in identifying fractions for which automated DIR would prove insufficient.

Characterizing a deformable registration algorithm for surface-guided breast radiotherapy

PURPOSE

Surface-guided radiation therapy (SGRT) is a nonionizing imaging approach for patient setup guidance, intra-fraction monitoring, and automated breath-hold gating of radiation treatments. SGRT employs the premise that the external patient surface correlates to the internal anatomy, to infer the treatment isocenter position at time of treatment delivery. Deformations and posture variations are known to impact the correlation between external and internal anatomy. However, the degree, magnitude, and algorithm dependence of this impact are not intuitive and currently no methods exist to assess this relationship. The primary aim of this work was to develop a framework to investigate and understand how a commercial optical surface imaging system (C-RAD, Uppsala, Sweden), which uses a nonrigid registration algorithm, handles rotations and surface deformations.

METHODS

A workflow consisting of a female torso phantom and software-introduced transformations to the corresponding digital reference surface was developed. To benchmark and validate the approach, known rigid translations and rotations were first applied. Relevant breast radiotherapy deformations related to breast size, hunching/arching back, distended/deflated abdomen, and an irregular surface to mimic a cover sheet over the lower part of the torso were investigated. The difference between rigid and deformed surfaces was evaluated as a function of isocenter location.

RESULTS

For all introduced rigid body transformations, C-RAD computed isocenter shifts were determined within 1 mm and 1˚. Additional translational shifts to correct for rotations as a function of isocenter location were determined with the same accuracy. For yaw setup errors, the difference in shift corrections between a plan with an isocenter placed in the center of the breast (BrstIso) and one located 12 cm superiorly (SCFIso) was 2.3 mm/1˚ in lateral direction. Pitch setup errors resulted in a difference of 2.1 mm/1˚ in vertical direction. For some of the deformation scenarios, much larger differences up to 16 mm and 7˚ in the calculated shifts between BrstIso and SCFIso were observed that could lead to large unintended gaps or overlap between adjacent matched fields if uncorrected.

CONCLUSIONS

The methodology developed lends itself well for quality assurance (QA) of SGRT systems. The deformable C-RAD algorithm determined accurate shifts for rigid transformations, and this was independent of isocenter location. For surface deformations, the position of the isocenter had considerable impact on the registration result. It is recommended to avoid off-axis isocenters during treatment planning to optimally utilize the capabilities of the deformable image registration algorithm, especially when multiple isocenters are used with fields that share a field edge.

Image quality improvement for medium and large field of view Elekta XVI scans

Cone-beam computed tomography (CBCT) has become the standard imaging technique for many sites treated with Radiotherapy. The Elekta X-ray Volumetric Imaging (XVI) system allows for the acquisition of CBCTs with three different diameters; small (27 cm), medium (41 cm) and large (50 cm) (Elekta in XVI Corrective Maintenance Manual R5.0, Elekta, Stockholm, 2013). Images are used to ensure accurate patient positioning as well as checking for changes in the patient contour or internal geometry, and image quality must be high enough to achieve these goals. This paper describes two simple adjustments which can lead to improved image quality for medium and large field of view (MFOV and LFOV) scans. The first involves measuring a default distance in the XVI software (kV Source to Detector Distance) to improve spatial resolution and geometric scaling of both MFOV and LFOV scans. The second involves correcting a uniformity issue seen at our centre with LFOV scans, allowing us to use this FOV for assessing patient contour changes on large patients. The implementation and effects of both adjustments are explored in this paper, and workflows are proposed for optimising both parameters.