Comparing interplay effects in scanned proton therapy of lung cancer: Free breathing with various layer and volume rescanning versus respiratory gating with different gate widths.

PURPOSE: We investigated interplay effects and treatment time (TT) in scanned proton therapy for lung cancer patients. We compared free-breathing (FB) approaches with multiple rescanning strategies and respiratory-gating (RG) methods with various gating widths to identify the superior irradiation technique. METHODS: Plans were created with 4/1, 2/2, and 1/4 layered/volume rescans of FB (L4V1, L2V2, and L1V4), and 50%, 30%, and 10% gating widths of the total respiratory curves (G50, G30, and G10) of the RG plans with L4V1. We calculated 4-dimensional dynamic doses assuming a constant sinusoidal curve for six irradiation methods. The reconstructed doses per fraction were compared with planned doses in terms of dose differences in 99% clinical-target-volume (CTV) (ΔD99%), near-maximum dose differences (ΔD2%) at organs-at-risk (OARs), and TT. RESULTS: The mean/minimum CTV ΔD99% values for FB were -1.0%/-4.9%, -0.8%/-4.3%, and -0.1%/-1.0% for L4V1, L2V2, and L1V4, respectively. Those for RG were -0.3%/-1.7%, -0.1%/-1.0%, and 0.0%/-0.5% for G50, G30, and G10, respectively. The CTV ΔD99% of the RGs with less than 50% gate width and the FBs of L1V4 were within the desired tolerance (±3.0%), and the OARs ΔD2% for RG were lower than those for FB. The mean TTs were 90, 326, 824, 158, 203, and 422 s for L4V1, L2V2, L1V4, G50, G30, and G10, respectively. CONCLUSIONS: FB (L4V1) is the most efficient treatment, but not necessarily the optimal choice due to interplay effects. To satisfy both TT extensions and interplay, RG with a gate width as large as possible within safety limits is desirable.

Annotation-free prediction of treatment-specific tissue outcome from 4D CT perfusion imaging in acute ischemic stroke.

Acute ischemic stroke is a critical health condition that requires timely intervention. Following admission, clinicians typically use perfusion imaging to facilitate treatment decision-making. While deep learning models leveraging perfusion data have demonstrated the ability to predict post-treatment tissue infarction for individual patients, predictions are often represented as binary or probabilistic masks that are not straightforward to interpret or easy to obtain. Moreover, these models typically rely on large amounts of subjectively segmented data and non-standard perfusion analysis techniques. To address these challenges, we propose a novel deep learning approach that directly predicts follow-up computed tomography images from full spatio-temporal 4D perfusion scans through a temporal compression. The results show that this method leads to realistic follow-up image predictions containing the infarcted tissue outcomes. The proposed compression method achieves comparable prediction results to using perfusion maps as inputs but without the need for perfusion analysis or arterial input function selection. Additionally, separate models trained on 45 patients treated with thrombolysis and 102 treated with thrombectomy showed that each model correctly captured the different patient-specific treatment effects as shown by image difference maps. The findings of this work clearly highlight the potential of our method to provide interpretable stroke treatment decision support without requiring manual annotations.

CT respiratory motion synthesis using joint supervised and adversarial learning.

Objective.Four-dimensional computed tomography (4DCT) imaging consists in reconstructing a CT acquisition into multiple phases to track internal organ and tumor motion. It is commonly used in radiotherapy treatment planning to establish planning target volumes. However, 4DCT increases protocol complexity, may not align with patient breathing during treatment, and lead to higher radiation delivery.Approach.In this study, we propose a deep synthesis method to generate pseudo respiratory CT phases from static images for motion-aware treatment planning. The model produces patient-specific deformation vector fields (DVFs) by conditioning synthesis on external patient surface-based estimation, mimicking respiratory monitoring devices. A key methodological contribution is to encourage DVF realism through supervised DVF training while using an adversarial term jointly not only on the warped image but also on the magnitude of the DVF itself. This way, we avoid excessive smoothness typically obtained through deep unsupervised learning, and encourage correlations with the respiratory amplitude.Main results.Performance is evaluated using real 4DCT acquisitions with smaller tumor volumes than previously reported. Results demonstrate for the first time that the generated pseudo-respiratory CT phases can capture organ and tumor motion with similar accuracy to repeated 4DCT scans of the same patient. Mean inter-scans tumor center-of-mass distances and Dice similarity coefficients were 1.97 mm and 0.63, respectively, for real 4DCT phases and 2.35 mm and 0.71 for synthetic phases, and compares favorably to a state-of-the-art technique (RMSim).Significance.This study presents a deep image synthesis method that addresses the limitations of conventional 4DCT by generating pseudo-respiratory CT phases from static images. Although further studies are needed to assess the dosimetric impact of the proposed method, this approach has the potential to reduce radiation exposure in radiotherapy treatment planning while maintaining accurate motion representation. Our training and testing code can be found athttps://github.com/cyiheng/Dynagan.

The impact of motion on onboard MRI-guided pencil beam scanned proton therapy treatments.

Objective.Online magnetic resonance imaging (MRI) guidance could be especially beneficial for pencil beam scanned (PBS) proton therapy of tumours affected by respiratory motion. For the first time to our knowledge, we investigate the dosimetric impact of respiratory motion on MRI-guided proton therapy compared to the scenario without magnetic field.Approach.A previously developed analytical proton dose calculation algorithm accounting for perpendicular magnetic fields was extended to enable 4D dose calculations. For two geometrical phantoms and three liver and two lung patient cases, static treatment plans were optimised with and without magnetic field (0, 0.5 and 1.5 T). Furthermore, plans were optimised using gantry angle corrections (0.5 T +5° and 1.5 T +15°) to reproduce similar beam trajectories compared to the 0 T reference plans. The effect of motion was then considered using 4D dose calculations without any motion mitigation and simulating 8-times volumetric rescanning, with motion for the patient cases provided by 4DCT(MRI) data sets. Each 4D dose calculation was performed for different starting phases and the CTV dose coverageV95%and homogeneityD5%-D95%were analysed.Main results.For the geometrical phantoms with rigid motion perpendicular to the beam and parallel to the magnetic field, a comparable dosimetric effect was observed independent of the magnetic field. Also for the five 4DCT(MRI) cases, the influence of motion was comparable for all magnetic field strengths with and without gantry angle correction. On average, the motion-induced decrease in CTVV95%from the static plan was 17.0% and 18.9% for 1.5 T and 0.5 T, respectively, and 19.9% without magnetic field.Significance.For the first time, this study investigates the combined impact of magnetic fields and respiratory motion on MR-guided proton therapy. The comparable dosimetric effects irrespective of magnetic field strength indicate that the effects of motion for future MR-guided proton therapy may not be worse than for conventional PBS proton therapy.

Motion compensated cone-beam CT reconstruction using ana priorimotion model from CT simulation: a pilot study.

Objective. To combat the motion artifacts present in traditional 4D-CBCT reconstruction, an iterative technique known as the motion-compensated simultaneous algebraic reconstruction technique (MC-SART) was previously developed. MC-SART employs a 4D-CBCT reconstruction to obtain an initial model, which suffers from a lack of sufficient projections in each bin. The purpose of this study is to demonstrate the feasibility of introducing a motion model acquired during CT simulation to MC-SART, coined model-based CBCT (MB-CBCT).Approach. For each of 5 patients, we acquired 5DCTs during simulation and pre-treatment CBCTs with a simultaneous breathing surrogate. We cross-calibrated the 5DCT and CBCT breathing waveforms by matching the diaphragms and employed the 5DCT motion model parameters for MC-SART. We introduced the Amplitude Reassignment Motion Modeling technique, which measures the ability of the model to control diaphragm sharpness by reassigning projection amplitudes with varying resolution. We evaluated the sharpness of tumors and compared them between MB-CBCT and 4D-CBCT. We quantified sharpness by fitting an error function across anatomical boundaries. Furthermore, we compared our MB-CBCT approach to the traditional MC-SART approach. We evaluated MB-CBCT's robustness over time by reconstructing multiple fractions for each patient and measuring consistency in tumor centroid locations between 4D-CBCT and MB-CBCT.Main results. We found that the diaphragm sharpness rose consistently with increasing amplitude resolution for 4/5 patients. We observed consistently high image quality across multiple fractions, and observed stable tumor centroids with an average 0.74 ± 0.31 mm difference between the 4D-CBCT and MB-CBCT. Overall, vast improvements over 3D-CBCT and 4D-CBCT were demonstrated by our MB-CBCT technique in terms of both diaphragm sharpness and overall image quality.Significance. This work is an important extension of the MC-SART technique. We demonstrated the ability ofa priori5DCT models to provide motion compensation for CBCT reconstruction. We showed improvements in image quality over both 4D-CBCT and the traditional MC-SART approach.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation.

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics.

Morphological and dynamic analysis of the normal aortic valve with 4D computed tomography.

OBJECTIVES: To evaluate the precise dimensions of the normal aortic root, especially the true aortic annulus, during the cardiac cycle using an innovative reconstruction method based on multiphase cardiac computed tomography and to assess the feasibility and the reproducibility of this method for aortic root analysis. METHODS: Between January 2019 and June 2021, 30 optimal consecutive ECG-gated multiphase cardiac computed tomography of patients with normal tricuspid aortic valve were analysed using an in-house software. Aortic annulus border was pinpointed on 9 reconstructed planes and the 3D coordinates of the 18 consecutive points were interpolated into a 3D curve using a cubic spline. Three additional planes were generated at the level of the left ventricular outflow tract, the level of the Valsalva sinus and the level of the sinotubular junction. This procedure was repeated for all the 10 temporal phases of the RR interval. RESULTS: The aortic annulus mean 3D and 2D areas were 7.67 ± 1.51 and 5.16 ± 1.40 cm2, respectively. The mean 2D diameter was 2.51 ± 0.23 cm. The mean global area expansion was 11.8 ± 3.5% and the mean perimeter expansion of 7.1 ± 2.6%. During the cardiac cycle, the left ventricle outflow tract expands, reaching its maximum surface at the end of diastole, followed by the aortic annulus, the Valsalva sinuses and the sinotubular junction. The aorta changes from a clover-shaped cone during diastole to more cylindrical shape during systole. Compared to the 3D measurements, the analysis of the virtual basal ring significantly underestimates the annulus area, perimeter, and mean diameter. CONCLUSIONS: 4D morphometric analysis enables to have a precise and reproducible evaluation of the aortic annulus. The aortic annulus and root are deformable structures that undergo a unique expansion sequence during the cardiac cycle which should be considered for procedural planning.

A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer.

OBJECTIVE: This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. METHODS: 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. RESULTS: The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p < 0.01) and 0.42% (p < 0.01). Furthermore, CLCGAN significantly decreased the absolute mean differences of CT value in lungs, bones, and soft tissues. The dose calculation results revealed a significant improvement in 4D-sCT compared to 4D-CBCT. CLCGAN was the most accurate in dose calculations for left lung (V5Gy), right lung (V5Gy), right lung (V20Gy), PTV (D98%), and spinal cord (D2%), with the relative dose difference were reduced by 6.84%, 3.84%, 1.46%, 0.86%, 3.32% compared to 4D-CBCT. CONCLUSIONS: Based on the satisfactory results obtained in terms of image quality, CT value measurement, it can be concluded that CLCGAN-based corrected 4D-CBCT can be utilized for dose calculation in lung cancer.

Analysis of respiratory-induced motion trajectories of individual liver segments in patients with hepatocellular carcinoma.

PURPOSE: To analyze the respiratory-induced motion trajectories of each liver segment for hepatocellular carcinoma (HCC) to derive a more accurate internal margin and optimize treatment protocol selection. MATERIALS AND METHODS: Ten-phase-gated four-dimensional computed tomography (4DCT) scans of 14 patients with HCC were analyzed. For each patient, eight representative regions of interest (ROI) were delineated on each liver segment in all 10 phases. The coordinates of the center of gravity of each ROI were obtained for each phase, and then the respiratory motion in the left-right (LR), anteroposterior (AP), and craniocaudal (CC) directions was analyzed. Two sets of motion in each direction were also compared in terms of only two extreme phases and all 10 phases. RESULTS: Motion of less than 5 mm was detected in 12 (86%) and 10 patients (71%) in the LR and AP directions, respectively, while none in the CC direction. Motion was largest in the CC direction with a maximal value of 19.5 mm, with significant differences between liver segment 7 (S7) and other segments: S1 (p < 0.036), S2 (p < 0.041), S3 (p < 0.016), S4 (p < 0.041), and S5 (p < 0.027). Of the 112 segments, hysteresis >1 mm was observed in 4 (4%), 2 (2%), and 15 (13%) in the LR, AP, and CC directions, respectively, with a maximal value of 5.0 mm in the CC direction. CONCLUSION: A significant amount of respiratory motion was detected in the CC direction, especially in S7, and S8. Despite the small effect of hysteresis, it can be observed specifically in the right lobe. Therefore, caution is required when using 4DCT to determine IM using only end-inspiration and end-expiration. Understanding the respiratory motion in individual liver segments can be helpful when selecting an appropriate treatment protocol.

Which Localizing Strategy is the Most Cost-Effective in Reoperative Primary Hyperparathyroidism?

INTRODUCTION: 2%-10% of patients with primary hyperparathyroidism (PHPT) who undergo parathyroidectomy develop persistent/recurrent disease. The aim of this study was to determine which preoperative localization method is most cost-effective in reoperative PHPT. METHODS: Clinical decision analytic models comparing cost-effectiveness of localizing studies in reoperative PHPT were constructed using TreeAge Pro. Cost and probability assumptions were varied via Probabilistic Sensitivity Analysis (PSA) to test the robustness of the base case models. RESULTS: Base case analysis of model 1 revealed ultrasound (US)-guided fine-needle aspiration with PTH assay as most cost-effective after localizing US. This was confirmed on PSA of model 1. Model 2 showed four-dimensional computed tomography (4D-CT) as most cost-effective after negative US. If not localized by US, on PSA, 4D-CT was the next most cost-effective test. CONCLUSIONS: US-guided FNA with PTH is the most cost-effective confirmatory test after US localization. 4D-CT should be considered as the next best test after negative US.

Four-Dimensional Computed Tomography for Parathyroid Adenoma Localization: A Pre-Operative Imaging Protocol.

OBJECTIVE: Primary hyperparathyroidism (PHPT) affects approximately 0.86% of the population, with surgical resection as the treatment of choice. A 4D computed tomography (CT) is a highly effective tool in localizing parathyroid adenomas; however, there is currently no defined role for 4D CT when stratified against ultrasonography (USG) and nuclear medicine Technetium Sestamibi SPECT/CT (SES) imaging. STUDY DESIGN: Retrospective Study. SETTING: University Hospital. METHODS: All patients who underwent parathyroidectomy for PHPT between 2014 and 2019 at a single institution were reviewed. Patients who had a 4D CT were included. We compared outcomes of 4D CT as a second line imaging modality to those of USG and SES as first line modalities. An imaging algorithm was proposed based on these findings. RESULTS: There were 84 patients identified who had a 4D CT after unsuccessful first line imaging. A 4D CT localized parathyroid adenoma to the correct quadrant in 64% of cases, and to the correct laterality in 75% of cases. Obese patients had significantly lower rates of adenoma localization with USG (33.4%), compared to non-obese patients (67.5%; P = .006). In determining multigland disease the sensitivity of 4D CT was 86%, while the specificity was 87%. CONCLUSIONS: A 4D CT has impressive rates of accurate localization of parathyroid adenomas; however due to the radiation exposure involved, it should remain a second line imaging modality. PHPT patients should first be evaluated with USG, with 4D CT used if this is unsuccessful and patients are greater than 40 years old, have a high BMI, or are having revision surgery.

Real-time estimation of lung deformation from body surface using a general CoordConv CNN.

BACKGROUND AND OBJECTIVE: Estimating the three-dimensional (3D) deformation of the lung is important for accurate dose delivery in radiotherapy and precise surgical guidance in lung surgery navigation. Additional 4D-CT information is often required to eliminate the effect of individual variations and obtain a more accurate estimation of lung deformation. However, this results in increased radiation dose. Therefore, we propose a novel method that estimates lung tissue deformation from depth maps and two CT phases per patient. METHODS: The method models the 3D motion of each voxel as a linear displacement along a direction vector, with a variable amplitude and phase that depend on the voxel location. The direction vector and amplitude are derived from the registration of the CT images at the end-of-exhale (EOE) and the end-of-inhale (EOI) phases. The voxel phase is estimated by a neural network. Coordinate convolution (CoordConv) is used to fuse multimodal data and embed absolute position information. The network takes the front and side views as well as the previous phase views as inputs to enhance accuracy. RESULTS: We evaluate the proposed method on two datasets: DIR-Lab and 4D-Lung, and obtain average errors of 2.11 mm and 1.36 mm, respectively. The method achieves real-time performance of less than 7 ms per frame on a NVIDIA GeForce 2080Ti GPU. CONCLUSION: Compared with previous methods, our method achieves comparable or even better accuracy with less CT phases.

Technical note: Towards more realistic 4DCT(MRI) numerical lung phantoms.

BACKGROUND: Numerical 4D phantoms, together with associated ground truth motion, offer a flexible and comprehensive data set for realistic simulations in radiotherapy and radiology in target sites affected by respiratory motion. PURPOSE: We present an openly available upgrade to previously reported methods for generating realistic 4DCT lung numerical phantoms, which now incorporate respiratory ribcage motion and improved lung density representation throughout the breathing cycle. METHODS: Density information of reference CTs, toget her with motion from multiple breathing cycle 4DMRIs have been combined to generate synthetic 4DCTs (4DCT(MRI)s). Inter-subject correspondence between the CT and MRI anatomy was first established via deformable image registration (DIR) of binary masks of the lungs and ribcage. Ribcage and lung motions were extracted independently from the 4DMRIs using DIR and applied to the corresponding locations in the CT after post-processing to preserve sliding organ motion. In addition, based on the Jacobian determinant of the resulting deformation vector fields, lung densities were scaled on a voxel-wise basis to more accurately represent changes in local lung density. For validating this process, synthetic 4DCTs, referred to as 4DCT(CT)s, were compared to the originating 4DCTs using motion extracted from the latter, and the dosimetric impact of the new features of ribcage motion and density correction were analyzed using pencil beam scanned proton 4D dose calculations. RESULTS: Lung density scaling led to a reduction of maximum mean lung Hounsfield units (HU) differences from 45 to 12 HU when comparing simulated 4DCT(CT)s to their originating 4DCTs. Comparing 4D dose distributions calculated on the enhanced 4DCT(CT)s to those on the original 4DCTs yielded 2%/2 mm gamma pass rates above 97% with an average improvement of 1.4% compared to previously reported phantoms. CONCLUSIONS: A previously reported 4DCT(MRI) workflow has been successfully improved and the resulting numerical phantoms exhibit more accurate lung density representations and realistic ribcage motion.

Comparative evaluation of a surface-based respiratory monitoring system against a pressure sensor for 4DCT image reconstruction in phantoms.

Four-dimensional computed tomography (4DCT), which relies on breathing-induced motion, requires realistic surrogate information of breathing variations to reconstruct the tumor trajectory and motion variability of normal tissues accurately. Therefore, the SimRT surface-guided respiratory monitoring system has been installed on a Siemens CT scanner. This work evaluated the temporal and spatial accuracy of SimRT versus our commonly used pressure sensor, AZ-733 V. A dynamic thorax phantom was used to reproduce regular and irregular breathing patterns acquired by SimRT and Anzai. Various parameters of the recorded breathing patterns, including mean absolute deviations (MAD), Pearson correlations (PC), and tagging precision, were investigated and compared to ground-truth. Furthermore, 4DCT reconstructions were analyzed to assess the volume discrepancy, shape deformation and tumor trajectory. Compared to the ground-truth, SimRT more precisely reproduced the breathing patterns with a MAD range of 0.37 ± 0.27 and 0.92 ± 1.02 mm versus Anzai with 1.75 ± 1.54 and 5.85 ± 3.61 mm for regular and irregular breathing patterns, respectively. Additionally, SimRT provided a more robust PC of 0.994 ± 0.009 and 0.936 ± 0.062 for all investigated breathing patterns. Further, the peak and valley recognition were found to be more accurate and stable using SimRT. The comparison of tumor trajectories revealed discrepancies up to 7.2 and 2.3 mm for Anzai and SimRT, respectively. Moreover, volume discrepancies up to 1.71 ± 1.62% and 1.24 ± 2.02% were found for both Anzai and SimRT, respectively. SimRT was validated across various breathing patterns and showed a more precise and stable breathing tracking, (i) independent of the amplitude and period, (ii) and without placing any physical devices on the patient's body. These findings resulted in a more accurate temporal and spatial accuracy, thus leading to a more realistic 4DCT reconstruction and breathing-adapted treatment planning.

Deep learning based automatic internal gross target volume delineation from 4D-CT of hepatocellular carcinoma patients.

BACKGROUND: The location and morphology of the liver are significantly affected by respiratory motion. Therefore, delineating the gross target volume (GTV) based on 4D medical images is more accurate than regular 3D-CT with contrast. However, the 4D method is also more time-consuming and laborious. This study proposes a deep learning (DL) framework based on 4D-CT that can achieve automatic delineation of internal GTV. METHODS: The proposed network consists of two encoding paths, one for feature extraction of adjacent slices (spatial slices) in a specific 3D-CT sequence, and one for feature extraction of slices at the same location in three adjacent phase 3D-CT sequences (temporal slices), a feature fusion module based on an attention mechanism was proposed for fusing the temporal and spatial features. Twenty-six patients' 4D-CT, each consisting of 10 respiratory phases, were used as the dataset. The Hausdorff distance (HD95), Dice similarity coefficient (DSC), and volume difference (VD) between the manual and predicted tumor contour were computed to evaluate the model's segmentation accuracy. RESULTS: The predicted GTVs and IGTVs were compared quantitatively and visually with the ground truth. For the test dataset, the proposed method achieved a mean DSC of 0.869 ± 0.089 and an HD95 of 5.14 ± 3.34 mm for all GTVs, with under-segmented GTVs on some CT slices being compensated by GTVs on other slices, resulting in better agreement between the predicted IGTVs and the ground truth, with a mean DSC of 0.882 ± 0.085 and an HD95 of 4.88 ± 2.84 mm. The best GTV results were generally observed at the end-inspiration stage. CONCLUSIONS: Our proposed DL framework for tumor segmentation on 4D-CT datasets shows promise for fully automated delineation in the future. The promising results of this work provide impetus for its integration into the 4DCT treatment planning workflow to improve hepatocellular carcinoma radiotherapy.

Institutional experience report on the target contouring workflow in the radiotherapy department for stereotactic arrhythmia radioablation delivered on conventional linear accelerators.

PURPOSE: In stereotactic arrhythmia radioablation (STAR), the target is defined using multiple imaging studies and a multidisciplinary team consisting of electrophysiologist, cardiologist, cardiac radiologist, and radiation oncologist collaborate to identify the target and delineate it on the imaging studies of interest. This report describes the workflow employed in our radiotherapy department to transfer the target identified based on electrophysiology and cardiology imaging to the treatment planning image set. METHODS: The radiotherapy team was presented with an initial target in cardiac axes orientation, contoured on a wideband late gadolinium-enhanced (WB-LGE) cardiac magnetic resonance (CMR) study, which was subsequently transferred to the computed tomography (CT) scan used for treatment planning-i.e., the average intensity projection (AIP) image set derived from a 4D CT-via an axial CMR image set, using rigid image registration focused on the target area. The cardiac and the respiratory motion of the target were resolved using ciné-CMR and 4D CT imaging studies, respectively. RESULTS: The workflow was carried out for 6 patients and resulted in an internal target defined in standard anatomical orientation that encompassed the cardiac and the respiratory motion of the initial target. CONCLUSION: An image registration-based workflow was implemented to render the STAR target on the planning image set in a consistent manner, using commercial software traditionally available for radiation therapy.

Dosimetric comparison of four-dimensional computed tomography based internal target volume against variations in respiratory motion during treatment between volumetric modulated arc therapy and three-dimensional conformal radiotherapy in lung stereotactic body radiotherapy.

This study focused on the dosimetric impact of variations in respiratory motion during lung stereotactic body radiotherapy (SBRT). Dosimetric comparisons between volumetric modulated arc therapy (VMAT) and three-dimensional conformal radiotherapy (3DCRT) were performed using four-dimensional computed tomography (4DCT)-based internal target volumes (ITV). We created retrospective plans for ten patients with lung cancer who underwent SBRT using 3DCRT and VMAT techniques. A Delta4 Phantom + (ScandiDos, Uppsala, Sweden) was used to evaluate the dosimetric robustness of 4DCT-based ITV against variations in respiratory motion during treatment. We analyzed respiratory motion during treatment. Dose-volume histogram parameters were evaluated for the 95% dose (D95%) to the planning target volume (PTV) contoured on CT images obtained under free breathing. The correlations between patient respiratory parameters and dosimetric errors were also evaluated. In the phantom study, the average PTV D95% dose differences for all fractions were - 2.9 ± 4.4% (- 16.0 - 1.2%) and - 2.0 ± 2.8% (- 11.2 - 0.7%) for 3DCRT and VMAT, respectively. The average dose difference was < 3% for both 3DCRT and VMAT; however, in 5 out of 42 fractions in 3DCRT, the difference in PTV D95% was > 10%. Dosimetric errors were correlated with respiratory amplitude and velocity, and differences in respiratory amplitude between 4DCT and treatment days were the main factors causing dosimetric errors. The overall average dose error of the PTV D95% was small; however, both 3DCRT and VMAT cases exceeding 10% error were observed. Larger errors occurred with amplitude variation or baseline drift, indicating limited robustness of 4DCT-based ITV.