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BACKGROUND: Magnetic resonance-guided radiotherapy with an MR-guided LINAC represents potential clinical benefits in abdominal treatments due to the superior soft-tissue contrast compared to kV-based images in conventional treatment units. However, due to the high cost associated with this technology, only a few centers have access to it. As an alternative, synthetic 4D MRI generation based on artificial intelligence methods could be implemented. Nevertheless, appropriate MRI texture generation from CT images might be challenging and prone to hallucinations, compromising motion accuracy. PURPOSE: To evaluate the feasibility of on-board synthetic motion-resolved 4D MRI generation from prior 4D MRI, on-board 4D cone beam CT (CBCT) images, motion modeling information, and deep learning models using the digital anthropomorphic phantom XCAT. METHODS: The synthetic 4D MRI corresponds to phases from on-board 4D CBCT. Each synthetic MRI volume in the 4D MRI was generated by warping a reference 3D MRI (MRIref, end of expiration phase from a prior 4D MRI) with a deformation field map (DFM) determined by (I) the eigenvectors from the principal component analysis (PCA) motion-modeling of the prior 4D MRI, and (II) the corresponding eigenvalues predicted by a convolutional neural network (CNN) model using the on-board 4D CBCT images as input. The CNN was trained with 1000 deformations of one reference CT (CTref, same conditions as MRIref) generated by applying 1000 DFMs computed by randomly sampling the original eigenvalues from the prior 4D MRI PCA model. The evaluation metrics for the CNN model were root-mean-square error (RMSE) and mean absolute error (MAE). Finally, different on-board 4D-MRI generation scenarios were assessed by changing the respiratory period, the amplitude of the diaphragm, and the chest wall motion of the 4D CBCT using normalized root-mean-square error (nRMSE) and structural similarity index measure (SSIM) for image-based evaluation, and volume dice coefficient (VDC), volume percent difference (VPD), and center-of-mass shift (COMS) for contour-based evaluation of liver and target volumes. RESULTS: The RMSE and MAE values of the CNN model reported 0.012 ± 0.001 and 0.010 ± 0.001, respectively for the first eigenvalue predictions. SSIM and nRMSE were 0.96 ± 0.06 and 0.22 ± 0.08, respectively. VDC, VPD, and COMS were 0.92 ± 0.06, 3.08 ± 3.73 %, and 2.3 ± 2.1 mm, respectively, for the target volume. The more challenging synthetic 4D-MRI generation scenario was for one 4D-CBCT with increased chest wall motion amplitude, reporting SSIM and nRMSE of 0.82 and 0.51, respectively. CONCLUSIONS: On-board synthetic 4D-MRI generation based on predicting actual treatment deformation from on-board 4D-CBCT represents a method that can potentially improve the treatment-setup localization in abdominal radiotherapy treatments with a conventional kV-based LINAC.
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PURPOSE/OBJECTIVE(S): We hypothesized that an in-house developed system using MV and kV image guidance (MKIG) to ensure correct prostate positioning during SBRT could potentially avoid unwanted doses to non-target tissues, leading to reduced toxicities. MATERIALS/METHODS: We built a 3D MKIG platform that accurately tracks prostate implanted fiducials in real-time and clinically translated the system to replace a commercial approach, intrafraction motion review (IMR), which only tracks fiducials in the 2D kV views. From 2017 to 2019, 150 prostate cancer patients were treated with SBRT and monitored by MKIG. The motion trace of the fiducials alerts therapists to interrupt and reposition the prostate when displacement exceeds a 1.5 mm threshold. A comparison cohort of 121 patients was treated with the same dose regimen and treatment technique but managed by IMR. Statistics of intrafractional patient shifts and delivery time were collected to evaluate the workflow efficacy. The incidence of grade ≥2 urinary toxicities was analyzed to assess clinical complications. The median follow-up time was 3.7 years (0.2 to 8.2 years). RESULTS: MKIG treatments had more treatment shifts (1.09 vs. 0.28) and a longer average delivery time per fraction (579±205s vs. 357±117s) than IMR treatments. Three-quarters (75%) of shifts resulting from MKIG were ≤3mm, vs. 51% in IMR, indicating that MKIG detected and corrected smaller deviations. The incidence of grade ≥2 urinary toxicity was lower in the MKIG than IMR cohort: 10.7% vs. 19.8% (p=0.047). On multivariate analysis of late urinary toxicity, only high (>7) pre-RT IPSS (p<0.043) and the use of MKIG were selected (p< 0.029). CONCLUSIONS: Automated and quantitative MKIG introduced minimal workflow impact and was superior to IMR in localizing the prostate during SBRT, which correlated with a clinically significant reduction in late urinary toxicity. Further clinical testing via randomized trial will be required to validate the impact on outcomes.
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OBJECTIVE: To investigate the feasibility of standardizing RT simulation CT scanner protocols between vendors using target-based image quality (IQ) metrics. METHOD AND MATERIALS: A systematic assessment process in phantom was developed to standardize clinical scan protocols for scanners from different vendors following these steps: (a) images were acquired by varying CTDIvol and using an iterative reconstruction (IR) method (IR: iDose and model-based iterative reconstruction [IMR] of CTp-Philips Big Bore scanner, SAFIRE of CTs-Siemens biograph PETCT scanner), (b) CT exams were classified into body and brain protocols, (c) the rescaled noise power spectrum (NPS) was calculated, (d) quantified the IQ change due to varied CTDIvol and IR, and (e) matched the IR strength level. IQ metrics included noise and texture from NPS, contrast, and contrast-to-noise ratio (CNR), low contrast detectability (d'). Area under curve (AUC) of the receiver operation characteristic curve of d' was calculated and compared. RESULTS: The level of change in the IQ ratio was significant (>0.6) when using IMR. The IQ ratio change was relatively low to moderate when using either iDose in CTp (0.1-0.5) or SAFIRE in CTs (0.1-0.6). SAFIRE-2 in CTs showed a closer match to the reference body protocol when compared to iDose-3 in CTp. In the brain protocol, iDose-3 in CTp could be matched to the low to moderate level of SAFIRE in CTs. The AUC of d' was highest when using IMR in CTp with lower CTDIvol, and SAFIRE in CTs performed better than iDose in CTp CONCLUSION: It is possible to use target-based IQ metrics to evaluate the performance of the system and operations across various scanners in a phantom. This can serve as an initial reference to convert clinical scanned protocols from one CT simulation scanner to another.
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Purpose: In radiation therapy (RT), if an immobilization device is lost or damaged, the patient may need to be brought back for resimulation, device fabrication, and treatment planning, causing additional imaging radiation exposure, inconvenience, cost, and delay. We describe a simulation-free method for replacing lost or damaged RT immobilization devices. Methods and Materials: Replacement immobilization devices were fabricated using existing simulation scans as design templates by computer numerical control (CNC) milling of molds made from extruded polystyrene (XPS). XPS material attenuation and bolusing properties were evaluated, a standard workflow was established, and 12 patients were treated. Setup reproducibility was analyzed postfacto using Dice similarity coefficient (DSC) and mean distance to agreement (MDA) calculations comparing onboard treatment imaging with computed tomography (CT) simulations. Results: Results showed that XPS foam material had less dosimetric impact (attenuation and bolusing) than materials used for our standard immobilization devices. The average direct cost to produce each replacement mold was $242.17, compared with over $2000 for standard resimulation. Hands-on time to manufacture was 86.3 minutes, whereas molds were delivered in as little as 4 hours and mostly within 24 hours, compared with a week or more required for standard resimulation. Each mold was optically scanned after production and was measured to be within 2-mm tolerance (pointwise displacement) of design input. All patients were successfully treated using the CNC-milled foam mold replacements, and pretreatment imaging verified satisfactory clinical setup reproduction for each case. The external body contours from the setup cone beam CT and the original CT simulation with matching superior-inferior extent were compared by calculating the DSC and MDA. DSC average was 0.966 (SD, 0.011), and MDA average was 2.694 mm (SD, 0.986). Conclusions: CNC milling of XPS foam is a quicker and more convenient solution than traditional resimulation for replacing lost or damaged RT immobilization devices. Satisfactory patient immobilization, low dosimetric impact compared with standard immobilization devices, and strong correlation of onboard contours with CT simulations are shown. We share our clinical experience, workflow, and manufacturing guide to help other clinicians who may want to adopt this solution.
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Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson's tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson's tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study's results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.
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Algoritmos , Tomografía Computarizada por Rayos X , Humanos , Tomografía Computarizada por Rayos X/métodos , Fantasmas de Imagen , Control de Calidad , Artefactos , Dosis de Radiación , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
BACKGROUND: In radiotherapy, real-time tumor tracking can verify tumor position during beam delivery, guide the radiation beam to target the tumor, and reduce the chance of a geometric miss. Markerless kV x-ray image-based tumor tracking is challenging due to the low tumor visibility caused by tumor-obscuring structures. Developing a new method to enhance tumor visibility for real-time tumor tracking is essential. PURPOSE: To introduce a novel method for markerless kV image-based tracking of lung tumors via deep learning-based target decomposition. METHODS: We utilized a conditional Generative Adversarial Network (cGAN), known as Pix2Pix, to build a patient-specific model and generate the synthetic decomposed target image (sDTI) to enhance tumor visibility on the real-time kV projection images acquired by the onboard kV imager equipped on modern linear accelerators. We used 4DCT simulation images to generate the digitally reconstructed radiograph (DRR) and DTI image pairs for model training. We augmented the training dataset by randomly shifting the 4DCT in the superior-inferior, anterior-posterior, and left-right directions during the DRR and DTI generation process. We performed real-time 2D tumor tracking via template matching between the DTI generated from the CT simulation and the sDTI generated from the real-time kV projection images. We validated the proposed method using nine patients' datasets with implanted beacons near the tumor. RESULTS: The sDTI can effectively improve the image contrast around the lung tumors on the kV projection images for the nine patients. With the beacon motion as ground truth, the tracking errors were on average 0.8 ± 0.7 mm in the superior-inferior (SI) direction and 0.9 ± 0.8 mm in the in-plane left-right (IPLR) direction. The percentage of successful tracking, defined as a tracking error less than 2 mm in the SI direction, is 92.2% on the 4312 tested images. The patient-specific model took approximately 12 h to train. During testing, it took approximately 35 ms to generate one sDTI, and 13 ms to perform the tumor tracking using template matching. CONCLUSIONS: Our method offers the potential solution for nearly real-time markerless lung tumor tracking. It achieved a high level of accuracy and an impressive tracking rate. Further development of 3D lung tumor tracking is warranted.
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Aprendizaje Profundo , Tomografía Computarizada Cuatridimensional , Procesamiento de Imagen Asistido por Computador , Neoplasias Pulmonares , Radioterapia Guiada por Imagen , Neoplasias Pulmonares/radioterapia , Neoplasias Pulmonares/diagnóstico por imagen , Humanos , Radioterapia Guiada por Imagen/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Tomografía Computarizada Cuatridimensional/métodosRESUMEN
Background and Purpose: The lack of dedicated tools in commercial planning systems currently restricts efficient review and planning for re-irradiation. The aim of this study was to develop an automated re-irradiation planning framework based on cumulative doses. Materials and Methods: We performed a retrospective study of 14 patients who received spine SBRT re-irradiation near a previously irradiated treatment site. A fully-automated workflow, DART (Dose Accumulation-based Re-irradiation Tool), was implemented within Eclipse by leveraging a combination of a dose accumulation script and a proprietary automated optimization algorithm. First, we converted the prior treatment dose into equivalent dose in 2 Gy fractions (EQD2) and mapped it to the current anatomy, utilizing deformable image registration. Subsequently, the intersection of EQD2 isodose lines with relevant organs at risk defines a series of optimization structures. During plan optimization, the residual allowable dose at a specified tissue tolerance was treated as a hard constraint. Results: All DART plans met institutional physical and cumulative constraints and passed plan checks by qualified medical physicists. DART demonstrated significant improvements in target coverage over clinical plans, with an average increase in PTV D99% and V100% of 2.3 Gy [range -0.3-7.7 Gy] and 3.4 % [range -0.4 %-7.6 %] (p < 0.01, paired t-test), respectively. Moreover, high-dose spillage (>105 %) outside the PTV was reduced by up to 7 cm3. The homogeneity index for DART plans was improved by 19 % (p < 0.001). Conclusions: DART provides a powerful framework to achieve more tailored re-irradiation plans by accounting for dose distributions from the previous treatments. The superior plan quality could improve the therapeutic ratio for re-irradiation patients.
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PURPOSE: The objective of this study was to develop a linear accelerator (LINAC)-based adaptive radiation therapy (ART) workflow for the head and neck that is informed by automated image tracking to identify major anatomic changes warranting adaptation. In this study, we report our initial clinical experience with the program and an investigation into potential trigger signals for ART. METHODS AND MATERIALS: Offline ART was systematically performed on patients receiving radiation therapy for head and neck cancer on C-arm LINACs. Adaptations were performed at a single time point during treatment with resimulation approximately 3 weeks into treatment. Throughout treatment, all patients were tracked using an automated image tracking system called the Automated Watchdog for Adaptive Radiotherapy Environment (AWARE). AWARE measures volumetric changes in gross tumor volumes (GTVs) and selected normal tissues via cone beam computed tomography scans and deformable registration. The benefit of ART was determined by comparing adaptive plan dosimetry and normal tissue complication probabilities against the initial plans recalculated on resimulation computed tomography scans. Dosimetric differences were then correlated with AWARE-measured volume changes to identify patient-specific triggers for ART. Candidate trigger variables were evaluated using receiver operator characteristic analysis. RESULTS: In total, 46 patients received ART in this study. Among these patients, we observed a significant decrease in dose to the submandibular glands (mean ± standard deviation: -219.2 ± 291.2 cGy, P < 10-5), parotids (-68.2 ± 197.7 cGy, P = .001), and oral cavity (-238.7 ± 206.7 cGy, P < 10-5) with the adaptive plan. Normal tissue complication probabilities for xerostomia computed from mean parotid doses also decreased significantly with the adaptive plans (P = .008). We also observed systematic intratreatment volume reductions (ΔV) for GTVs and normal tissues. Candidate triggers were identified that predicted significant improvement with ART, including parotid ΔV = 7%, neck ΔV = 2%, and nodal GTV ΔV = 29%. CONCLUSIONS: Systematic offline head and neck ART was successfully deployed on conventional LINACs and reduced doses to critical salivary structures and the oral cavity. Automated cone beam computed tomography tracking provided information regarding anatomic changes that may aid patient-specific triggering for ART.
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Tomografía Computarizada de Haz Cónico , Neoplasias de Cabeza y Cuello , Órganos en Riesgo , Selección de Paciente , Planificación de la Radioterapia Asistida por Computador , Carga Tumoral , Humanos , Neoplasias de Cabeza y Cuello/radioterapia , Neoplasias de Cabeza y Cuello/diagnóstico por imagen , Planificación de la Radioterapia Asistida por Computador/métodos , Órganos en Riesgo/efectos de la radiación , Órganos en Riesgo/diagnóstico por imagen , Dosificación Radioterapéutica , Radioterapia Guiada por Imagen/métodos , Glándula Parótida/efectos de la radiación , Glándula Parótida/diagnóstico por imagen , Masculino , Flujo de Trabajo , Persona de Mediana Edad , Anciano , FemeninoRESUMEN
Objective.We report on paraspinal motion and the clinical implementation of our proprietary software that leverages Varian's intrafraction motion review (IMR) capability for quantitative tracking of the spine during paraspinal SBRT. The work is based on our prior development and analysis on phantoms.Approach.To address complexities in patient anatomy, digitally reconstructed radiographs (DRR's) that highlight only the spine or hardware were constructed as tracking reference. Moreover, a high-pass filter and first-pass coarse search were implemented to enhance registration accuracy and stability. For evaluation, 84 paraspinal SBRT patients with sites spanning across the entire vertebral column were enrolled with prescriptions ranging from 24 to 40 Gy in one to five fractions. Treatments were planned and delivered with 9 IMRT beams roughly equally distributed posteriorly. IMR was triggered every 200 or 500 MU for each beam. During treatment, the software grabbed the IMR image, registered it with the corresponding DRR, and displayed the motion result in near real-time on auto-pilot mode. Four independent experts completed offline manual registrations as ground truth for tracking accuracy evaluation.Main results.Our software detected ≥1.5 mm and ≥2 mm motions among 17.1% and 6.6% of 1371 patient images, respectively, in either lateral or longitudinal direction. In the validation set of 637 patient images, 91.9% of the tracking errors compared to manual registration fell within ±0.5 mm in either direction. Given a motion threshold of 2 mm, the software accomplished a 98.7% specificity and a 93.9% sensitivity in deciding whether to interrupt treatment for patient re-setup.Significance.Significant intrafractional motion exists in certain paraspinal SBRT patients, supporting the need for quantitative motion monitoring during treatment. Our improved software achieves high motion tracking accuracy clinically and provides reliable guidance for treatment intervention. It offers a practical solution to ensure accurate delivery of paraspinal SBRT on a conventional Linac platform.
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Radiocirugia , Humanos , Radiocirugia/métodos , Programas Informáticos , Movimiento (Física) , Planificación de la Radioterapia Asistida por ComputadorRESUMEN
BACKGROUND: Quality assurance of deformable image registration (DIR) is challenging because the ground truth is often unavailable. In addition, current approaches that rely on artificial transformations do not adequately resemble clinical scenarios encountered in adaptive radiotherapy. PURPOSE: We developed an atlas-based method to create a variety of patient-specific serial digital phantoms with CBCT-like image quality to assess the DIR performance for longitudinal CBCT imaging data in adaptive lung radiotherapy. METHODS: A library of deformations was created by extracting the longitudinal changes observed between a planning CT and weekly CBCT from an atlas of lung radiotherapy patients. The planning CT of an inquiry patient was first deformed by mapping the deformation pattern from a matched atlas patient, and subsequently appended with CBCT artifacts to imitate a weekly CBCT. Finally, a group of digital phantoms around an inquiry patient was produced to simulate a series of possible evolutions of tumor and adjacent normal structures. We validated the generated deformation vector fields (DVFs) to ensure numerically and physiologically realistic transformations. The proposed framework was applied to evaluate the performance of the DIR algorithm implemented in the commercial Eclipse treatment planning system in a retrospective study of eight inquiry patients. RESULTS: The generated DVFs were inverse consistent within less than 3 mm and did not exhibit unrealistic folding. The deformation patterns adequately mimicked the observed longitudinal anatomical changes of the matched atlas patients. Worse Eclipse DVF accuracy was observed in regions of low image contrast or artifacts. The structure volumes exhibiting a DVF error magnitude of equal or more than 2 mm ranged from 24.5% (spinal cord) to 69.2% (heart) and the maximum DVF error exceeded 5 mm for all structures except the spinal cord. Contour-based evaluations showed a high degree of alignment with dice similarity coefficients above 0.8 in all cases, which underestimated the overall DVF accuracy within the structures. CONCLUSIONS: It is feasible to create and augment digital phantoms based on a particular patient of interest using multiple series of deformation patterns from matched patients in an atlas. This can provide a semi-automated procedure to complement the quality assurance of CT-CBCT DIR and facilitate the clinical implementation of image-guided and adaptive radiotherapy that involve longitudinal CBCT imaging studies.
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Tomografía Computarizada de Haz Cónico Espiral , Humanos , Estudios Retrospectivos , Tomografía Computarizada de Haz Cónico/métodos , Planificación de la Radioterapia Asistida por Computador/métodos , Fantasmas de Imagen , Pulmón/diagnóstico por imagen , Procesamiento de Imagen Asistido por Computador/métodos , AlgoritmosRESUMEN
To investigate automation of the preparation of the region of interest (ROI) for surface-guided radiotherapy (SGRT) of the whole breast with two algorithms based on contour anatomies: using the body contour, and using the breast contour. The patient dataset used for modeling consisted of 39 breast cancer patients previously treated with SGRT. The patient's anatomical structures (body and ipsilateral breast) were retrieved from the planning system, and the clinical ROI (cROI) drawn by the planners was retrieved from the SGRT system for comparison. For the body-contour-based algorithm, a convolutional neural network (MobileNet-v2) was utilized to train a synthetic human model dataset to predict body joint locations. With the body joint location knowledge, an automated ROI (aROIbody ) can be created based on: (1) the superior-inferior (S-I) borders defined by the joint locations, (2) the left-right (L-R) borders defined with 3/4 of chest width, and (3) a curation of the ROI to avoid the ipsilateral armpit. For the breast-contour-based algorithm, an aROIbreast was created by first defining the ROI in the S-I direction with the ipsilateral breast boundaries. Other steps are the same as with the body-contour-based algorithm. Among the 39 patients, 24 patients were used to fine-tune the algorithm parameters, and the remaining 15 patients were used to evaluate the quality of the aROIs against the cROIs. A blinded evaluation was performed by three SGRT expert physicists to rate the acceptability and the quality (1-10 scale) of the aROIs and cROIs, and the dice similarity coefficient (DSC) was also calculated to compare the similarity between the aROIs and cROIs. The results showed that the average acceptability was 14/15 (range: 13/15-15/15) for cROIs, 13.3/15 (range: 13/15-14/15) for aROIbody , and 14.6/15 (range: 14/15-15/15) for aROIbreast . The average quality was 7.4 ± 0.8 for cROIs, 8.1 ± 1.2 for aROIbody , and 8.2 ± 0.9 for aROIbreast . The DSC with cROIs was 0.81 ± 0.06 for aROIbody , and 0.83 ± 0.04 for aROIbreast . The ROI creation time was â¼120 s for clinical, 1.3 s for aROIbody , and 1.2 s for aROIbreast . The proposed automated algorithms can improve the ROI compliance with the SGRT protocol, with a shortened preparation time. It is ready to be integrated into the clinical workflow for automated ROI preparation.
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Neoplasias de la Mama , Radioterapia Guiada por Imagen , Humanos , Femenino , Neoplasias de la Mama/radioterapia , Algoritmos , Mama/diagnóstico por imagen , Redes Neurales de la Computación , Planificación de la Radioterapia Asistida por Computador/métodosRESUMEN
Purpose: Deep inspiration breath hold (DIBH) is an effective technique to spare the heart in treating left-sided breast cancer. Surface-guided radiation therapy (SGRT) is increasingly applied in DIBH setup and motion monitoring. Patient-specific breathing behavior, either thoracically driven or abdominally driven (A-DIBH), should be unaltered, online identified, and monitored accordingly to ensure reproducible heart-sparing treatment. Methods and Materials: Sixty patients with left-sided breast cancer treated with SGRT were analyzed: 20 A-DIBH patients with vertical chest elevation (VCE ≤ 5 mm) were prospectively identified, and 40 control patients were retrospectively and randomly selected for comparison. At simulation, both free-breathing (FB) and DIBH computed tomography (CT) were acquired, guided by a motion surrogate placed around the xiphoid process. For SGRT treatment setups, the region of interest (ROI) was defined on the CT chest surface, and the surrogate-based setup was a backup. For all 60 patients, the VCE was measured as the average of the FB-to-DIBH elevations at the breast and xiphoid process, together with abdominal elevation. In the 40-patient control group, A-DIBH patients (VCE ≤ 5 mm) were identified. Of the 20 A-DIBH patients, 10 were treated with volumetric modulated arc therapy plans, and 10 patients were treated with tangent plans. Clinical DIBH plans were recalculated on FB CT to compare maximum dose (DMax), 5% of the maximum dose (D5%), mean dose (DMean), and V30Gy, V20Gy, and V5Gy of the heart and lungs and their significance. Results: In the 20 A-DIBH patients, VCE = 3 ± 2 mm, surrogate motion (9 ± 6 mm), and abdomen motion of 14 ± 5 mm are found. Heart dose reduction from FB to DIBH is significant (P < .01): ∆DMax = -8.4 ± 9.8 Gy, ∆D5% = -2.4 ± 4.4 Gy, and ∆DMean = -0.6 ± 0.9 Gy. Six out of 40 control patients (15%) are found to have VCE ≤ 5 mm. Conclusions: A-DIBH (VCE ≤ 5 mm) patient population is significant (15%), and they should be identified in the SGRT workflow and monitored accordingly. A new abdominal ROI or an abdominal surrogate should be used instead of the conventional chest-only ROI. Patient-specific DIBH should be preserved for higher reproducibility to ensure heart sparing.
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Background and purpose: Patients with brain metastases (BMs) are surviving longer and returning for multiple courses of stereotactic radiosurgery. BMs are monitored after radiation with follow-up magnetic resonance (MR) imaging every 2-3 months. This study investigated whether it is possible to automatically track BMs on longitudinal imaging and quantify the tumor response after radiotherapy. Methods: The METRO process (MEtastasis Tracking with Repeated Observations was developed to automatically process patient data and track BMs. A longitudinal intrapatient registration method for T1 MR post-Gd was conceived and validated on 20 patients. Detections and volumetric measurements of BMs were obtained from a deep learning model. BM tracking was validated on 32 separate patients by comparing results with manual measurements of BM response and radiologists' assessments of new BMs. Linear regression and residual analysis were used to assess accuracy in determining tumor response and size change. Results: A total of 123 irradiated BMs and 38 new BMs were successfully tracked. 66 irradiated BMs were visible on follow-up imaging 3-9 months after radiotherapy. Comparing their longest diameter changes measured manually vs. METRO, the Pearson correlation coefficient was 0.88 (p < 0.001); the mean residual error was -8 ± 17%. The mean registration error was 1.5 ± 0.2 mm. Conclusions: Automatic, longitudinal tracking of BMs using deep learning methods is feasible. In particular, the software system METRO fulfills a need to automatically track and quantify volumetric changes of BMs prior to, and in response to, radiation therapy.
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Over 4 million survivors of breast cancer live in the United States, 35% of whom were treated before 2009. Approximately half of patients with breast cancer receive radiation therapy, which exposes the untreated contralateral breast to radiation and increases the risk of a subsequent contralateral breast cancer (CBC). Radiation oncology has strived to reduce unwanted radiation dose, but it is unknown whether a corresponding decline in actual dose received to the untreated contralateral breast has occurred. The purpose of this study was to evaluate trends in unwanted contralateral breast radiation dose to inform risk assessment of second primary cancer in the contralateral breast for long-term survivors of breast cancer. Individually estimated radiation absorbed doses to the four quadrants and areola central area of the contralateral breast were estimated for 2,132 women treated with radiation therapy for local/regional breast cancers at age <55 years diagnosed between 1985 and 2008. The two inner quadrant doses and two outer quadrant doses were averaged. Trends in dose to each of the three areas of the contralateral breast were evaluated in multivariable models. The population impact of reducing contralateral breast dose on the incidence of radiation-associated CBC was assessed by estimating population attributable risk fraction (PAR) in a multivariable model. The median dose to the inner quadrants of the contralateral breast was 1.70 Gy; to the areola, 1.20 Gy; and to the outer quadrants, 0.72 Gy. Ninety-two percent of patients received ≥1 Gy to the inner quadrants. For each calendar year of diagnosis, dose declined significantly for each location, most rapidly for the inner quadrants (0.04 Gy/year). Declines in dose were similar across subgroups defined by age at diagnosis and body mass index. The PAR for CBC due to radiation exposure >1 Gy for women <40 years of age was 17%. Radiation dose-reduction measures have reduced dose to the contralateral breast during breast radiation therapy. Reducing the dose to the contralateral breast to <1 Gy could prevent an estimated 17% of subsequent radiation-associated CBCs for women treated under 40 years of age. These dose estimates inform CBC surveillance for the growing number of breast cancer survivors who received radiation therapy as young women in recent decades. Continued reductions in dose to the contralateral breast could further reduce the incidence of radiation-associated CBC.
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Neoplasias de la Mama , Neoplasias Inducidas por Radiación , Neoplasias Primarias Secundarias , Femenino , Humanos , Estados Unidos , Persona de Mediana Edad , Neoplasias de la Mama/radioterapia , Neoplasias de la Mama/epidemiología , Neoplasias Inducidas por Radiación/epidemiología , Neoplasias Inducidas por Radiación/etiología , Factores de Riesgo , Neoplasias Primarias Secundarias/etiología , Neoplasias Primarias Secundarias/complicaciones , Dosis de RadiaciónRESUMEN
To compare the setup accuracy of optical surface image (OSI) versus orthogonal x-ray images (2DkV) using cone beam computed tomography (CBCT) as ground truth for radiotherapy of left breast cancer in deep-inspiration breath-hold (DIBH). Ten left breast DIBH patients treated with volumetric modulated arc therapy (VMAT) were studied retrospectively. OSI, 2DkV, and CBCT were acquired weekly at treatment setup. OSI, 2DkV, and CBCT were registered to planning CT or planning DRR based on a breast surface region of interest (ROI), bony anatomy (chestwall and sternum), and both bony anatomy and breast surface, respectively. These registrations provided couch shifts for each imaging system. The setup errors, or the difference in couch shifts between OSI and CBCT were compared to those between 2DkV and CBCT. A second OSI was acquired during last beam delivery to evaluate intrafraction motion. The median absolute setup errors were (0.21, 0.27, 0.23 cm, 0.6°, 1.3°, 1.0°) for OSI, and (0.26, 0.24, 0.18 cm, 0.9°, 1.0°, 0.6°) for 2DkV in vertical, longitudinal and lateral translations, and in rotation, roll and pitch, respectively. None of the setup errors was significantly different between OSI and 2DkV. For both systems, the systematic and random setup errors were ≤0.6 cm and ≤1.5° in all directions. Nevertheless, larger setup errors were observed in some sessions in both systems. There was no correlation between OSI and CBCT whereas there was modest correlation between 2DkV and CBCT. The intrafraction motion in DIBH detected by OSI was small with median absolute translations <0.2 cm, and rotations ≤0.4°. Though OSI showed comparable and small setup errors as 2DkV, it showed no correlation with CBCT. We concluded that to achieve accurate setup for both bony anatomy and breast surface, daily 2DkV can't be omitted following OSI for left breast patients treated with DIBH VMAT.
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Neoplasias de la Mama , Radioterapia de Intensidad Modulada , Humanos , Femenino , Estudios Retrospectivos , Rayos X , Tomografía Computarizada de Haz Cónico/métodos , Neoplasias de la Mama/diagnóstico por imagen , Neoplasias de la Mama/radioterapia , Planificación de la Radioterapia Asistida por Computador/métodos , Contencion de la RespiraciónRESUMEN
BACKGROUND: X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy. PURPOSE: This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging. METHODS: Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information. RESULTS: The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (â¼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001). CONCLUSION: The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.
Asunto(s)
Algoritmos , Redes Neurales de la Computación , Humanos , Movimiento (Física)RESUMEN
BACKGROUND: Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging. PURPOSE: To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR). METHODS: Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung. RESULTS: Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom. CONCLUSIONS: The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.
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Tomografía Computarizada de Haz Cónico , Neoplasias Pulmonares , Humanos , Tomografía Computarizada de Haz Cónico/métodos , Movimiento (Física) , Pulmón , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/radioterapia , Radiografía , Fantasmas de ImagenRESUMEN
Objective.To develop and clinically implement a fully automated treatment planning system (TPS) for volumetric modulated arc therapy (VMAT).Approach.We solve two constrained optimization problems sequentially. The tumor coverage is maximized at the first step while respecting all maximum/mean dose clinical criteria. The second step further reduces the dose at the surrounding organs-at-risk as much as possible. Our algorithm optimizes the machine parameters (leaf positions and monitor units) directly and the resulting mathematical non-convexity is handled using thesequential convex programmingby solving a series of convex approximation problems. We directly integrate two novel convex surrogate metrics to improve plan delivery efficiency and reduce plan complexity by promoting aperture shape regularity and neighboring aperture similarity. The entire workflow is automated using the Eclipse TPS application program interface scripting and provided to users as a plug-in, requiring the users to solely provide the contours and their preferred arcs. Our program provides the optimal machine parameters and does not utilize the Eclipse optimization engine, however, it utilizes the Eclipse final dose calculation engine. We have tested our program on 60 patients of different disease sites and prescriptions for stereotactic body radiotherapy (paraspinal (24 Gy × 1, 9 Gy × 3), oligometastis (9 Gy × 3), lung (18 Gy × 3, 12 Gy × 4)) and retrospectively compared the automated plans with the manual plans used for treatment. The program is currently deployed in our clinic and being used in our daily clinical routine to treat patients.Main results.The automated plans found dosimetrically comparable or superior to the manual plans. For paraspinal (24 Gy × 1), the automated plans especially improved tumor coverage (the average PTV (Planning Target Volume) 95% from 96% to 98% and CTV100% from 95% to 97%) and homogeneity (the average PTV maximum dose from 120% to 116%). For other sites/prescriptions, the automated plans especially improved the duty cycle (23%-39.4%).Significance.This work proposes a fully automated approach to the mathematically challenging VMAT problem. It also shows how the capabilities of the existing (Food and Drug Administration)FDA-approved commercial TPS can be enhanced using an in-house developed optimization algorithm that completely replaces the TPS optimization engine. The code and pertained models along with a sample dataset will be released on our ECHO-VMAT GitHub (https://github.com/PortPy-Project/ECHO-VMAT).
Asunto(s)
Neoplasias , Radioterapia de Intensidad Modulada , Humanos , Radioterapia de Intensidad Modulada/métodos , Estudios Retrospectivos , Planificación de la Radioterapia Asistida por Computador/métodos , Dosificación Radioterapéutica , Neoplasias/radioterapia , Algoritmos , Órganos en RiesgoRESUMEN
BACKGROUND AND PURPOSE: Anatomic changes during head and neck radiotherapy can impact dose delivery, necessitate adaptive replanning, and indicate patient-specific response to treatment. We have developed an automated system to track these changes through longitudinal MRI scans to aid identification and clinical intervention. The purpose of this article is to describe this tracking system and present results from an initial cohort of patients. MATERIALS AND METHODS: The Automated Watchdog in Adaptive Radiotherapy Environment (AWARE) was developed to process longitudinal MRI data for radiotherapy patients. AWARE automatically identifies and collects weekly scans, propagates radiotherapy planning structures, computes structure changes over time, and reports important trends to the clinical team. AWARE also incorporates manual structure review and revision from clinical experts and dynamically updates tracking statistics when necessary. AWARE was applied to patients receiving weekly T2-weighted MRI scans during head and neck radiotherapy. Changes in nodal gross tumor volume (GTV) and parotid gland delineations were tracked over time to assess changes during treatment and identify early indicators of treatment response. RESULTS: N = 91 patients were tracked and analyzed in this study. Nodal GTVs and parotids both shrunk considerably throughout treatment (-9.7 ± 7.7% and -3.7 ± 3.3% per week, respectively). Ipsilateral parotids shrunk significantly faster than contralateral (-4.3 ± 3.1% vs. -2.9 ± 3.3% per week, p = 0.005) and increased in distance from GTVs over time (+2.7 ± 7.2% per week, p < 1 × 10-5 ). Automatic structure propagations agreed well with manual revisions (Dice = 0.88 ± 0.09 for parotids and 0.80 ± 0.15 for GTVs), but for GTVs the agreement degraded 4-5 weeks after the start of treatment. Changes in GTV volume observed by AWARE as early as one week into treatment were predictive of large changes later in the course (AUC = 0.79). CONCLUSION: AWARE automatically identified longitudinal changes in GTV and parotid volumes during radiotherapy. Results suggest that this system may be useful for identifying rapidly responding patients as early as one week into treatment.