Developing a MLC modifier program to improve fiducial detection for MV/kV imaging during hypofractionated prostate volumetric modulated arc therapy.

PURPOSE: To develop an Eclipse plug-in (MLC_MODIFIER) that automatically modifies control points to expose fiducials obscured by MLC during VMAT, thereby facilitating tracking using periodic MV/kV imaging. METHOD: Three-dimensional fiducial tracking was performed during VMAT by pairing short-arc (3°) MV digital tomosynthesis (DTS) images to triggered kV images. To evaluate MLC_MODIFIER efficacy, two cohorts of patients were considered. For first 12 patients, plans were manually edited to expose one fiducial marker. Next for 15 patients, plans were modified using MLC_MODIFIER script. MLC_MODIFIER evaluated MLC apertures at appropriate angles for marker visibility. Angles subtended by control points were compressed and low-dose "imaging" control points were inserted and exposed one marker with 1 cm margin. Patient's images were retrospectively reviewed to determine rate of MV registration failures. Failure categories were poor DTS image quality, MLC blockage of fiducials, or unknown reasons. Dosimetric differences in rectum, bladder, and urethra D1 cc, PTV maximum dose, and PTV dose homogeneity (PTV HI) were evaluated. Statistical significance was evaluated using Fisher's exact and Student's t test. RESULT: Overall MV registration failures, failures due to poor image quality, MLC blockage, and unknown reasons were 33% versus 8.9% (P < 0.0001), 8% versus 6.4% (P < 0.05), 13.6% versus 0.1% (P < 0.0001), and 7.6% versus 2.4% (P < 0.0001) for manually edited and MLC_MODIFIER plans, respectively. PTV maximum and HI increased on average from unmodified plans by 2.1% and 0.3% (P < 0.004) and 22.0% and 3.3% (P < 0.004) for manually edited and MLC_MODIFIED plans, respectively. Changes in bladder, rectum, and urethra D1CC were similar for each method and less than 0.7%. CONCLUSION: Increasing fiducial visibility via an automated process comprised of angular compression of control points and insertion of additional "imaging" control points is feasible. Degradation of plan quality is minimal. Fiducial detection and registration success rates are significantly improved compared to manually edited apertures.

Evaluation of respiratory motion-corrected cone-beam CT at end expiration in abdominal radiotherapy sites: a prospective study.

BACKGROUND: Cone beam computed tomography (CBCT) for radiotherapy image guidance suffers from respiratory motion artifacts. This limits soft tissue visualization and localization accuracy, particularly in abdominal sites. We report on a prospective study of respiratory motion-corrected (RMC)-CBCT to evaluate its efficacy in localizing abdominal organs and improving soft tissue visibility at end expiration. MATERIAL AND METHODS: In an IRB approved study, 11 patients with gastroesophageal junction (GEJ) cancer and five with pancreatic cancer underwent a respiration-correlated CT (4DCT), a respiration-gated CBCT (G-CBCT) near end expiration and a one-minute free-breathing CBCT scan on a single treatment day. Respiration was recorded with an external monitor. An RMC-CBCT and an uncorrected CBCT (NC-CBCT) were computed from the free-breathing scan, based on a respiratory model of deformations derived from the 4DCT. Localization discrepancy was computed as the 3D displacement of the GEJ region (GEJ patients), or gross tumor volume (GTV) and kidneys (pancreas patients) in the NC-CBCT and RMC-CBCT relative to their positions in the G-CBCT. Similarity of soft-tissue features was measured using a normalized cross correlation (NCC) function. RESULTS: Localization discrepancy from the end-expiration G-CBCT was reduced for RMC-CBCT compared to NC-CBCT in eight of eleven GEJ cases (mean ± standard deviation, respectively, 0.21 ± 0.11 and 0.43 ± 0.28 cm), in all five pancreatic GTVs (0.26 ± 0.21 and 0.42 ± 0.29 cm) and all ten kidneys (0.19 ± 0.13 and 0.51 ± 0.25 cm). Soft-tissue feature similarity around GEJ was higher with RMC-CBCT in nine of eleven cases (NCC =0.48 ± 0.20 and 0.43 ± 0.21), and eight of ten kidneys (0.44 ± 0.16 and 0.40 ± 0.17). CONCLUSIONS: In a prospective study of motion-corrected CBCT in GEJ and pancreas, RMC-CBCT yielded improved organ visibility and localization accuracy for gated treatment at end expiration in the majority of cases.

AAPM medical physics practice guideline 10.a.: Scope of practice for clinical medical physics.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline (MPPG) represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiation requires specific training, skills, and techniques as described in each document. As the review of the previous version of AAPM Professional Policy (PP)-17 (Scope of Practice) progressed, the writing group focused on one of the main goals: to have this document accepted by regulatory and accrediting bodies. After much discussion, it was decided that this goal would be better served through a MPPG. To further advance this goal, the text was updated to reflect the rationale and processes by which the activities in the scope of practice were identified and categorized. Lastly, the AAPM Professional Council believes that this document has benefitted from public comment which is part of the MPPG process but not the AAPM Professional Policy approval process. The following terms are used in the AAPM's MPPGs: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.

Dose calculation for hypofractionated volumetric-modulated arc therapy: approximating continuous arc delivery and tongue-and-groove modeling.

Hypofractionated treatments generally increase the complexity of a treatment plan due to the more stringent constraints of normal tissues and target coverage. As a result, treatment plans contain more modulated MLC motions that may require extra efforts for accurate dose calculation. This study explores methods to minimize the differences between in-house dose calculation and actual delivery of hypofractionated volumetric-modulated arc therapy (VMAT), by focusing on arc approximation and tongue-and-groove (TG) modeling. For dose calculation, the continuous delivery arc is typically approximated by a series of static beams with an angular spacing of 2°. This causes significant error when there is large MLC movement from one beam to the next. While increasing the number of beams will minimize the dose error, calculation time will increase significantly. We propose a solution by inserting two additional apertures at each of the beam angle for dose calculation. These additional apertures were interpolated at two-thirds' degree before and after each beam. Effectively, there were a total of three MLC apertures at each beam angle, and the weighted average fluence from the three apertures was used for calculation. Because the number of beams was kept the same, calculation time was only increased by about 6%-8%. For a lung plan, areas of high local dose differences (> 4%) between film measurement and calculation with one aperture were significantly reduced in calculation with three apertures. Ion chamber measurement also showed similar results, where improvements were seen with calculations using additional apertures. Dose calculation accuracy was further improved for TG modeling by developing a sampling method for beam fluence matrix. Single element point sampling for fluence transmitted through MLC was used for our fluence matrix with 1 mm resolution. For Varian HDMLC, grid alignment can cause fluence sampling error. To correct this, transmission volume averaging was applied. For three paraspinal HDMLC cases, the average dose difference was greatly reduced in film and calculation comparisons with our new approach. The gamma (3%, 3 mm) pass rates have improved significantly from 74.1%, 90.0%, and 90.4% to 99.2%, 97.9%, and 97.3% for three cases, for calculation without volume averaging and calculation with volume averaging, respectively. Our results indicate that more accurate MLC leaf position and transmission sampling can improve accuracy and agreement between calculation and measurement, and are particularly important for hypofractionated VMAT that consists of large MLC movement.

Simultaneous MV-kV imaging for intrafractional motion management during volumetric-modulated arc therapy delivery.

The purpose of this study was to evaluate the accuracy and clinical feasibility of a motion monitoring method employing simultaneously acquired MV and kV images during volumetric-modulated arc therapy (VMAT). Short-arc digital tomosynthesis (SA-DTS) is used to improve the quality of the MV images that are then combined with orthogonally acquired kV images to assess 3D motion. An anthropomorphic phantom with implanted gold seeds was used to assess accuracy of the method under static, typical prostatic, and respiratory motion scenarios. Automatic registra-tion of kV images and single MV frames or MV SA-DTS reconstructed with arc lengths from 2° to 7° with the appropriate reference fiducial template images was performed using special purpose-built software. Clinical feasibility was evaluated by retrospectively analyzing images acquired over four or five sessions for each of three patients undergoing hypofractionated prostate radiotherapy. The standard deviation of the registration error in phantom using MV SA-DTS was similar to single MV images for the static and prostate motion scenarios (σ = 0.25 mm). Under respiratory motion conditions, the standard deviation of the registration error increased to 0.7mm and 1.7 mm for single MV and MV SA-DTS, respectively. Registration failures were observed with the respiratory scenario only and were due to motion-induced fiducial blurring. For the three patients studied, the mean and standard deviation of the difference between automatic registration using 4° MV SA-DTS and manual registration using single MV images results was 0.07±0.52mm. The MV SA-DTS results in patients were, on average, superior to single-frame MV by nearly 1 mm - significantly more than what was observed in phantom. The best MV SA-DTS results were observed with arc lengths of 3° to 4°. Registration failures in patients using MV SA-DTS were primarily due to blockage of the gold seeds by the MLC. The failure rate varied from 2% to 16%. Combined MV SA-DTS and kV imaging is feasible for intratreatment motion monitoring during VMAT of anatomic sites where limited motion is expected, and improves registration accuracy compared to single MV/kV frames. To create a clinically robust technique, further improvements to ensure visualization of fiducials at the desired control points without degradation of the treatment plan are needed.

Incorporation of treatment plan spatial and temporal dose patterns into a prostate intrafractional motion management strategy.

PURPOSE: Periodic MV∕KV radiographs taken during volumetric modulated arc therapy (VMAT) for hypofractionated treatment provide guidance in intrafractional motion management. The choice of imaging frequency and timing are key components in delivering the desired dose while reducing associated overhead such as imaging dose, preparation, and processing time. In this project the authors propose a paradigm with imaging timing and frequency based on the spatial and temporal dose patterns of the treatment plan. METHODS: A number of control points are used in treatment planning to model VMAT delivery. For each control point, the sensitivity of individual target or organ-at-risk dose to motion can be calculated as the summation of dose degradations given the organ displacements along a number of possible motion directions. Instead of acquiring radiographs at uniform time intervals, MV∕KV image pairs are acquired indexed to motion sensitivity. Five prostate patients treated via hypofractionated VMAT are included in this study. Intrafractional prostate motion traces from the database of an electromagnetic tracking system are used to retrospectively simulate the VMAT delivery and motion management. During VMAT delivery simulation patient position is corrected based on the radiographic findings via couch movement if target deviation violates a patient-specific 3D threshold. The violation rate calculated as the percentage of traces failing the clinical dose objectives after motion correction is used to evaluate the efficacy of this approach. RESULTS: Imaging indexed to a 10 s equitime interval and correcting patient position accordingly reduces the violation rate to 19.5% with intervention from 44.5% without intervention. Imaging indexed to the motion sensitivity further reduces the violation rate to 12.1% with the same number of images. To achieve the same 5% violation rate, the imaging incidence can be reduced by 40% by imaging indexed to motion sensitivity instead of time. CONCLUSIONS: The simulation results suggest that image scheduling according to the characteristics of the treatment plan can improve the efficiency of intrafractional motion management. Using such a technique, the accuracy of delivered dose during image-guided hypofractionated VMAT treatment can be improved.

Interactive semiautomatic contour delineation using statistical conditional random fields framework.

PURPOSE: Contouring a normal anatomical structure during radiation treatment planning requires significant time and effort. The authors present a fast and accurate semiautomatic contour delineation method to reduce the time and effort required of expert users. METHODS: Following an initial segmentation on one CT slice, the user marks the target organ and nontarget pixels with a few simple brush strokes. The algorithm calculates statistics from this information that, in turn, determines the parameters of an energy function containing both boundary and regional components. The method uses a conditional random field graphical model to define the energy function to be minimized for obtaining an estimated optimal segmentation, and a graph partition algorithm to efficiently solve the energy function minimization. Organ boundary statistics are estimated from the segmentation and propagated to subsequent images; regional statistics are estimated from the simple brush strokes that are either propagated or redrawn as needed on subsequent images. This greatly reduces the user input needed and speeds up segmentations. The proposed method can be further accelerated with graph-based interpolation of alternating slices in place of user-guided segmentation. CT images from phantom and patients were used to evaluate this method. The authors determined the sensitivity and specificity of organ segmentations using physician-drawn contours as ground truth, as well as the predicted-to-ground truth surface distances. Finally, three physicians evaluated the contours for subjective acceptability. Interobserver and intraobserver analysis was also performed and Bland-Altman plots were used to evaluate agreement. RESULTS: Liver and kidney segmentations in patient volumetric CT images show that boundary samples provided on a single CT slice can be reused through the entire 3D stack of images to obtain accurate segmentation. In liver, our method has better sensitivity and specificity (0.925 and 0.995) than region growing (0.897 and 0.995) and level set methods (0.912 and 0.985) as well as shorter mean predicted-to-ground truth distance (2.13 mm) compared to regional growing (4.58 mm) and level set methods (8.55 mm and 4.74 mm). Similar results are observed in kidney segmentation. Physician evaluation of ten liver cases showed that 83% of contours did not need any modification, while 6% of contours needed modifications as assessed by two or more evaluators. In interobserver and intraobserver analysis, Bland-Altman plots showed our method to have better repeatability than the manual method while the delineation time was 15% faster on average. CONCLUSIONS: Our method achieves high accuracy in liver and kidney segmentation and considerably reduces the time and labor required for contour delineation. Since it extracts purely statistical information from the samples interactively specified by expert users, the method avoids heuristic assumptions commonly used by other methods. In addition, the method can be expanded to 3D directly without modification because the underlying graphical framework and graph partition optimization method fit naturally with the image grid structure.

Reduction of irregular breathing artifacts in respiration-correlated CT images using a respiratory motion model.

PURPOSE: Respiration-correlated CT (RCCT) images produced with commonly used phase-based sorting of CT slices often exhibit discontinuity artifacts between CT slices, caused by cycle-to-cycle amplitude variations in respiration. Sorting based on the displacement of the respiratory signal yields slices at more consistent respiratory motion states and hence reduces artifacts, but missing image data (gaps) may occur. The authors report on the application of a respiratory motion model to produce an RCCT image set with reduced artifacts and without missing data. METHODS: Input data consist of CT slices from a cine CT scan acquired while recording respiration by monitoring abdominal displacement. The model-based generation of RCCT images consists of four processing steps: (1) displacement-based sorting of CT slices to form volume images at 10 motion states over the cycle; (2) selection of a reference image without gaps and deformable registration between the reference image and each of the remaining images; (3) generation of the motion model by applying a principal component analysis to establish a relationship between displacement field and respiration signal at each motion state; (4) application of the motion model to deform the reference image into images at the 9 other motion states. Deformable image registration uses a modified fast free-form algorithm that excludes zero-intensity voxels, caused by missing data, from the image similarity term in the minimization function. In each iteration of the minimization, the displacement field in the gap regions is linearly interpolated from nearest neighbor nonzero intensity slices. Evaluation of the model-based RCCT examines three types of image sets: cine scans of a physical phantom programmed to move according to a patient respiratory signal, NURBS-based cardiac torso (NCAT) software phantom, and patient thoracic scans. RESULTS: Comparison in physical motion phantom shows that object distortion caused by variable motion amplitude in phase-based sorting is visibly reduced with model-based RCCT. Comparison of model-based RCCT to original NCAT images as ground truth shows best agreement at motion states whose displacement-sorted images have no missing slices, with mean and maximum discrepancies in lung of 1 and 3 mm, respectively. Larger discrepancies correlate with motion states having a larger number of missing slices in the displacement-sorted images. Artifacts in patient images at different motion states are also reduced. Comparison with displacement-sorted patient images as a ground truth shows that the model-based images closely reproduce the ground truth geometry at different motion states. CONCLUSIONS: Results in phantom and patient images indicate that the proposed method can produce RCCT image sets with reduced artifacts relative to phase-sorted images, without the gaps inherent in displacement-sorted images. The method requires a reference image at one motion state that has no missing data. Highly irregular breathing patterns can affect the method's performance, by introducing artifacts in the reference image (although reduced relative to phase-sorted images), or in decreased accuracy in the image prediction of motion states containing large regions of missing data.

CT and cone-beam CT of ablative radiation therapy for pancreatic cancer with expert organ-at-risk contours.

We describe a dataset from patients who received ablative radiation therapy for locally advanced pancreatic cancer (LAPC), consisting of computed tomography (CT) and cone-beam CT (CBCT) images with physician-drawn organ-at-risk (OAR) contours. The image datasets (one CT for treatment planning and two CBCT scans at the time of treatment per patient) were collected from 40 patients. All scans were acquired with the patient in the treatment position and in a deep inspiration breath-hold state. Six radiation oncologists delineated the gastrointestinal OARs consisting of small bowel, stomach and duodenum, such that the same physician delineated all image sets belonging to the same patient. Two trained medical physicists further edited the contours to ensure adherence to delineation guidelines. The image and contour files are available in DICOM format and are publicly available from The Cancer Imaging Archive ( https://doi.org/10.7937/TCIA.ESHQ-4D90 , Version 2). The dataset can serve as a criterion standard for evaluating the accuracy and reliability of deformable image registration and auto-segmentation algorithms, as well as a training set for deep-learning-based methods.

Automated and Clinically Optimal Treatment Planning for Cancer Radiotherapy.

Each year, approximately 18 million new cancer cases are diagnosed worldwide, and about half must be treated with radiotherapy. A successful treatment requires treatment planning with the customization of penetrating radiation beams to sterilize cancerous cells without harming nearby normal organs and tissues. This process currently involves extensive manual tuning of parameters by an expert planner, making it a time-consuming and labor-intensive process, with quality and immediacy of critical care dependent on the planner's expertise. To improve the speed, quality, and availability of this highly specialized care, Memorial Sloan Kettering Cancer Center developed and applied advanced optimization tools to this problem (e.g., using hierarchical constrained optimization, convex approximations, and Lagrangian methods). This resulted in both a greatly improved radiotherapy treatment planning process and the generation of reliable and consistent high-quality plans that reflect clinical priorities. These improved techniques have been the foundation of high-quality treatments and have positively impacted over 4,000 patients to date, including numerous patients in severe pain and in urgent need of treatment who might have otherwise required longer hospital stays or undergone unnecessary surgery to control the progression of their disease. We expect that the wide distribution of the system we developed will ultimately impact patient care more broadly, including in resource-constrained countries.

Determination of action thresholds for electromagnetic tracking system-guided hypofractionated prostate radiotherapy using volumetric modulated arc therapy.

PURPOSE: Hypofractionated prostate radiotherapy may benefit from both volumetric modulated are therapy (VMAT) due to shortened treatment time and intrafraction real-time monitoring provided by implanted radiofrequency(RF) transponders. The authors investigate dosimetrically driven action thresholds (whether treatment needs to be interrupted and patient repositioned) in VMAT treatment with electromagnetic (EM) tracking. METHODS: VMAT plans for five patients are generated for prescription doses of 32.5 and 42.5 Gy in five fractions. Planning target volume (PTV) encloses the clinical target volume (CTV) with a 3 mm margin at the prostate-rectal interface and 5 mm elsewhere. The VMAT delivery is modeled using 180 equi-spaced static beams. Intrafraction prostate motion is simulated in the plan by displacing the beam isocenter at each beam assuming rigid organ motion according to a previously recorded trajectory of the transponder centroid. The cumulative dose delivered in each fraction is summed over all beams. Two sets of 57 prostate motion trajectories were randomly selected to form a learning and a testing dataset. Dosimetric end points including CTV D95%, rectum wall D1cc, bladder wall D1cc, and urethra Dmax, are analyzed against motion characteristics including the maximum amplitude of the anterior-posterior (AP), superior-inferior (SI), and left-right components. Action thresholds are triggered when intrafraction motion causes any violations of dose constraints to target and organs at risk (OAR), so that treatment is interrupted and patient is repositioned. RESULTS: Intrafraction motion has a little effect on CTV D95%, indicating PTV margins are adequate. Tight posterior and inferior action thresholds around 1 mm need to be set in a patient specific manner to spare organs at risk, especially when the prescription dose is 42.5 Gy. Advantages of setting patient specific action thresholds are to reduce false positive alarms by 25% when prescription dose is low, and increase the sensitivity of detecting dose limits violations by 30% when prescription dose is high, compared to a generic 2 mm action box. The sensitivity and specificity calculated from the testing dataset are consistent to the learning set, which indicates that the patient specific approach is reliable and reproducible within the scope of the prostate database. CONCLUSIONS: This work introduces a formalism for ensuring a VMAT delivery meets the most clinically important dose requirements by using patient specific and dosimetric-driven action thresholds to hold the beam and reposition the patient when necessary. Such methods can provide improved sensitivity and specificity compared to conventional methods, which assume directionally symmetric action thresholds.

Deep-learning-based image registration and automatic segmentation of organs-at-risk in cone-beam CT scans from high-dose radiation treatment of pancreatic cancer.

PURPOSE: Accurate deformable registration between computed tomography (CT) and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high biologically effective radiation doses is essential to assess changes in organ-at-risk (OAR) locations and shapes and to compute delivered dose. This study describes the development and evaluation of a deep-learning (DL) registration model to predict OAR segmentations on the CBCT derived from segmentations on the planning CT. METHODS: The DL model is trained with CT-CBCT image pairs of the same patient, on which OAR segmentations of the small bowel, stomach, and duodenum have been manually drawn. A transformation map is obtained, which serves to warp the CT image and segmentations. In addition to a regularity loss and an image similarity loss, an OAR segmentation similarity loss is also used during training, which penalizes the mismatch between warped CT segmentations and manually drawn CBCT segmentations. At test time, CBCT segmentations are not required as they are instead obtained from the warped CT segmentations. In an IRB-approved retrospective study, a dataset consisting of 40 patients, each with one planning CT and two CBCT scans, was used in a fivefold cross-validation to train and evaluate the model, using physician-drawn segmentations as reference. Images were preprocessed to remove gas pockets. Network performance was compared to two intensity-based deformable registration algorithms (large deformation diffeomorphic metric mapping [LDDMM] and multimodality free-form [MMFF]) as baseline. Evaluated metrics were Dice similarity coefficient (DSC), change in OAR volume within a volume of interest (enclosing the low-dose PTV plus 1 cm margin) from planning CT to CBCT, and maximum dose to 5 cm3 of the OAR [D(5cc)]. RESULTS: Processing time for one CT-CBCT registration with the DL model at test time was less than 5 seconds on a GPU-based system, compared to an average of 30 minutes for LDDMM optimization. For both small bowel and stomach/duodenum, the DL model yielded larger median DSC and smaller interquartile variation than either MMFF (paired t-test P < 10-4 for both type of OARs) or LDDMM (P < 10-3 and P = 0.03 respectively). Root-mean-square deviation (RMSD) of DL-predicted change in small bowel volume relative to reference was 22% less than for MMFF (P = 0.007). RMSD of DL-predicted stomach/duodenum volume change was 28% less than for LDDMM (P = 0.0001). RMSD of DL-predicted D(5cc) in small bowel was 39% less than for MMFF (P = 0.001); in stomach/duodenum, RMSD of DL-predicted D(5cc) was 18% less than for LDDMM (P < 10-3 ). CONCLUSIONS: The proposed deep network CT-to-CBCT deformable registration model shows improved segmentation accuracy compared to intensity-based algorithms and achieves an order-of-magnitude reduction in processing time.

Dose correction strategy for the optimization of volumetric modulated arc therapy.

PURPOSE: Dose calculation during optimization of volumetric modulated arc therapy (VMAT) is necessarily simplified to keep computation time manageably low; however the approximations used in the scatter dose calculation lead to discrepancy with more accurate dose calculation following optimization. The purpose of this study is to develop a dose correction strategy in optimization that can minimize the disagreement. METHODS: VMAT delivery is modeled using a number of static equispaced beams. Dose correction factors (C(ij)) are associated with each beam i and point j inside the region of interest. C(ij) is calculated as the ratio of dose obtained from the full scatter dose calculation over that from the partial scatter dose calculation in optimization. VMAT optimization algorithm is a multiple resolution approach. The dose correction factors are calculated at the beginning of each resolution and applied as multiplicative corrections to the partial scatter dose during optimization. Clinical cases for brain, prostate, paraspinal, and esophagus are utilized to evaluate the method. Treatment plans created with and without the correction scheme are normalized such that the complication rates of organs at risk (OARs) are comparable. The resulting planning target volume (PTV) mean doses are used to compare plan quality. RESULTS: The difference between the dose calculated at the end of optimization and at the end of the final forward dose calculation is reduced from 7% and 5% for the PTV and OAR mean doses without correction to approximately 1% with correction. Applying dose correction during optimization saves planners 2-4 h in average in treatment planning, and has a positive impact on plan quality, evidenced by a noticeably higher PTV mean dose: 2.1%, 2.4%, 0.5%, and 9.3% of the corresponding prescription dose in the brain, esophagus, prostate, and paraspinal cases, respectively. CONCLUSIONS: When dose correction is applied during optimization, dose discrepancies between optimization and full dose calculation are reduced. Integrating dose correction in VMAT optimization allows planners to adjust the optimization constraints more easily and confidently during optimization and has the potential to improve plan quality.

Evaluation of respiration-correlated digital tomosynthesis in lung.

Digital tomosynthesis (DTS) with a linear accelerator-mounted imaging system provides a means of reconstructing tomographic images from radiographic projections over a limited gantry arc, thus requiring only a few seconds to acquire. Its application in the thorax, however, often results in blurred images from respiration-induced motion. This work evaluates the feasibility of respiration-correlated (RC) DTS for soft-tissue visualization and patient positioning. Image data acquired with a gantry-mounted kilovoltage imaging system while recording respiration were retrospectively analyzed from patients receiving radiotherapy for non-small-cell lung carcinoma. Projection images spanning an approximately 30 degrees gantry arc were sorted into four respiration phase bins prior to DTS reconstruction, which uses a backprojection, followed by a procedure to suppress structures above and below the reconstruction plane of interest. The DTS images were reconstructed in planes at different depths through the patient and normal to a user-selected angle close to the center of the arc. The localization accuracy of RC-DTS was assessed via a comparison with CBCT. Evaluation of RC-DTS in eight tumors shows visible reduction in image blur caused by the respiratory motion. It also allows the visualization of tumor motion extent. The best image quality is achieved at the end-exhalation phase of the respiratory motion. Comparison of RC-DTS with respiration-correlated cone-beam CT in determining tumor position, motion extent and displacement between treatment sessions shows agreement in most cases within 2-3 mm, comparable in magnitude to the intraobserver repeatability of the measurement. These results suggest the method's applicability for soft-tissue image guidance in lung, but must be confirmed with further studies in larger numbers of patients.

Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model.

PURPOSE: Respiratory motion adversely affects CBCT image quality and limits its localization accuracy for image-guided radiation treatment. Motion correction methods in CBCT have focused on the thorax because of its higher soft tissue contrast, whereas low-contrast tissue in abdomen remains a challenge. The authors report on a method to correct respiration-induced motion artifacts in 1 min CBCT scans that is applicable in both thorax and abdomen, using a motion model adapted to the patient from a respiration-correlated image set. METHODS: Model adaptation consists of nonrigid image registration that maps each image to a reference image in the respiration-correlated set, followed by a principal component analysis to reduce errors in the nonrigid registration. The model parametrizes the deformation field in terms of observed surrogate (diaphragm or implanted marker) position and motion (inhalation or exhalation) between the images. In the thorax, the model is obtained from the same CBCT images that are to be motion-corrected, whereas in the abdomen, the model uses respiration-correlated CT (RCCT) images acquired prior to the treatment session. The CBCT acquisition is a single 360 degrees rotation lasting 1 min, while simultaneously recording patient breathing. The approximately 600 projection images are sorted into six (in thorax) or ten (in abdomen) subsets and reconstructed to obtain a set of low-quality respiration-correlated RC-CBCT images. Application of the motion model deforms each of the RC-CBCT images to a chosen reference image in the set; combining all images yields a single high-quality CBCT image with reduced blurring and motion artifacts. Repeated application of the model with different reference images produces a series of motion-corrected CBCT images over the respiration cycle, for determining the motion extent of the tumor and nearby organs at risk. The authors also investigate a simpler correction method, which does not use PCA and correlates motion state with respiration phase, thus assuming repeatable breathing patterns. Comparison of contrast-to-noise ratios of pixel intensities within anatomical structures relative to surrounding background tissue provides a quantitative assessment of relative organ visibility. RESULTS: Evaluation in lung phantom, two patient cases in thorax and two in upper abdomen, shows that blurring and streaking artifacts are visibly reduced with motion correction. The boundaries of tumors in the thorax, liver, and kidneys are sharper and more discernible. Repeat application of the method in one thorax case, with reference images chosen at end expiration and end inspiration, indicates its feasibility for observing tumor motion extent. Phase-based motion correction without PCA reduces blurring less effectively; in addition, implanted markers appear broken up, indicating inconsistencies in the phase-based correction. In structures showing 1 cm or more motion excursion, PCA-based motion correction shows the highest contrast-to-noise ratios in the cases examined. CONCLUSIONS: Motion correction of CBCT is feasible and yields observable improvement in the thorax and abdomen. The PCA-based model is an important component: First, by reducing deformation errors caused by the nonrigid registration and second, by relating deformation to surrogate position rather than phase, thus accommodating breathing pattern changes between imaging sessions. The accuracy of the method requires confirmation in further patient studies.

Couch and multileaf collimator tracking: A clinical feasibility study for pancreas and liver treatment.

PURPOSE: Real-time tumor tracking through active correction by the multileaf collimator or treatment couch offers a promising strategy to mitigate delivery uncertainty due to intrafractional tumor motion. This study evaluated the performance of MLC and couch tracking using the prototype iTools Tracking system in TrueBeam Developer Mode and the application for abdominal cancer treatments. METHODS: Experiments were carried out using a phantom with embedded Calypso transponders and a motion simulation platform. Geometric evaluations were performed using a circular conformal field with sinusoidal traces and pancreatic tumor motion traces. Geometric tracking accuracy was retrospectively calculated by comparing the compensational MLC or couch motion extracted from machine log files to the target motion reconstructed from real-time MV and kV images. Dosimetric tracking accuracy was measured with radiochromic films using clinical abdominal VMAT plans and pancreatic tumor traces. RESULTS: Geometrically, the root-mean-square errors for MLC tracking were 0.5 and 1.8 mm parallel and perpendicular to leaf travel direction, respectively. Couch tracking, in contrast, showed an average of 0.8 mm or less geometric error in all directions. Dosimetrically, both MLC and couch tracking reduced motion-induced local dose errors compared to no tracking. Evaluated with five pancreatic tumor motion traces, the average 2%/2 mm global gamma pass rate of eight clinical abdominal VMAT plans was 67.4% (range: 26.4%-92.7%) without tracking, which was improved to 86.0% (range: 67.9%-95.6%) with MLC tracking, and 98.1% (range: 94.9%-100.0%) with couch tracking. In 16 out of 40 deliveries with different plans and motion traces, MLC tracking did not achieve clinically acceptable dosimetric accuracy with 3%/3mm gamma pass rate below 95%. CONCLUSIONS: This study demonstrated the capability of MLC and couch tracking to reduce motion-induced dose errors in abdominal cases using a prototype tracking system. Clinically significant dose errors were observed with MLC tracking for certain plans which could be attributed to the inferior MLC tracking accuracy in the direction perpendicular to leaf travel, as well as the interplay between motion tracking and plan delivery for highly modulated plans. Couch tracking outperformed MLC tracking with consistently high dosimetric accuracy in all plans evaluated, indicating its clinical potential in the treatment of abdominal cancers.

Technical Note: 3D localization of lung tumors on cone beam CT projections via a convolutional recurrent neural network.

PURPOSE: To design a convolutional recurrent neural network (CRNN) that calculates three-dimensional (3D) positions of lung tumors from continuously acquired cone beam computed tomography (CBCT) projections, and facilitates the sorting and reconstruction of 4D-CBCT images. METHOD: Under an IRB-approved clinical lung protocol, kilovoltage (kV) projections of the setup CBCT were collected in free-breathing. Concurrently, an electromagnetic signal-guided system recorded motion traces of three transponders implanted in or near the tumor. Convolutional recurrent neural network was designed to utilize a convolutional neural network (CNN) for extracting relevant features of the kV projections around the tumor, followed by a recurrent neural network for analyzing the temporal patterns of the moving features. Convolutional recurrent neural network was trained on the simultaneously collected kV projections and motion traces, subsequently utilized to calculate motion traces solely based on the continuous feed of kV projections. To enhance performance, CRNN was also facilitated by frequent calibrations (e.g., at 10° gantry rotation intervals) derived from cross-correlation-based registrations between kV projections and templates created from the planning 4DCT. Convolutional recurrent neural network was validated on a leave-one-out strategy using data from 11 lung patients, including 5500 kV images. The root-mean-square error between the CRNN and motion traces was calculated to evaluate the localization accuracy. RESULT: Three-dimensional displacement around the simulation position shown in the Calypso traces was 3.4 ± 1.7 mm. Using motion traces as ground truth, the 3D localization error of CRNN with calibrations was 1.3 ± 1.4 mm. CRNN had a success rate of 86 ± 8% in determining whether the motion was within a 3D displacement window of 2 mm. The latency was 20 ms when CRNN ran on a high-performance computer cluster. CONCLUSIONS: CRNN is able to provide accurate localization of lung tumors with aid from frequent recalibrations using the conventional cross-correlation-based registration approach, and has the potential to remove reliance on the implanted fiducials.

Clinical Experience of Automated SBRT Paraspinal and Other Metastatic Tumor Planning With Constrained Hierarchical Optimization.

PURPOSE: We report on the clinical performance of a fully automated approach to treatment planning based on a Pareto optimal, constrained hierarchical optimization algorithm, named Expedited Constrained Hierarchical Optimization (ECHO). METHODS AND MATERIALS: From April 2017 to October 2018, ECHO produced 640 treated plans for 523 patients who underwent stereotactic body radiation therapy (RT) for paraspinal and other metastatic tumors. A total of 182 plans were for 24 Gy in a single fraction, 387 plans were for 27 Gy in 3 fractions, and the remainder were for other prescriptions or fractionations. Of the plans, 84.5% were for paraspinal tumors, with 69, 302, and 170 in the cervical, thoracic, and lumbosacral spine, respectively. For each case, after contouring, a template plan using 9 intensity modulated RT fields based on disease site and tumor location was sent to ECHO through an application program interface plug-in from the treatment planning system. ECHO returned a plan that satisfied all critical structure hard constraints with optimal target volume coverage and the lowest achievable normal tissue doses. Upon ECHO completion, the planner received an e-mail indicating the plan was ready for review. The plan was accepted if all clinical criteria were met. Otherwise, a limited number of parameters could be adjusted for another ECHO run. RESULTS: The median planning target volume size was 84.3 cm3 (range, 6.9-633.2). The median time to produce 1 ECHO plan was 63.5 minutes (range, 11-340 minutes) and was largely dependent on the field sizes. Of the cases, 79.7% required 1 run to produce a clinically accepted plan, 13.3% required 1 additional run with minimal parameter adjustments, and 7.0% required ≥2 additional runs with significant parameter modifications. All plans met or bettered the institutional clinical criteria. CONCLUSIONS: We successfully implemented automated stereotactic body RT paraspinal and other metastatic tumors planning. ECHO produced high-quality plans, improved planning efficiency and robustness, and enabled expedited treatment planning at our clinic.

From phase-based to displacement-based gating: a software tool to facilitate respiration-gated radiation treatment.

The Varian Real-time Position Management (RPM) system allows respiratory gating based on either the phase or displacement (amplitude) of the breathing waveform. A problem in clinical application is that phase-based gating, required for respiration-correlated (4D-CT) simulation, is not robust to irregular breathing patterns during treatment, and a widely used system version (1.6) does not provide an easy means to change from a phase-based gate into an equivalent displacement-based one. We report on the development and evaluation of a robust method to convert phase-gate thresholds, set by the physician, into equivalent displacement-gate thresholds to facilitate its clinical application to treatment. The software tool analyzes the respiration trace recorded during the 4D-CT simulation, and determines a relationship between displacement and phase through a functional fit. The displacement gate thresholds are determined from an average of two values of this function, corresponding to the start and end thresholds of the original phase gate. The software tool was evaluated in two ways: first, whether in-gate residual target motion and predicted treatment beam duty cycle are equivalent between displacement gating and phase gating during 4D-CT simulation (using retrospective phase recalculation); second, whether residual motion is improved with displacement gating during treatment relative to phase gating (using real-time phase calculation). Residual target motion was inferred from the respiration traces and quantified in terms of mean and standard deviation in-gate displacement measured relative to the value at the start of the recorded trace. For retrospectively-calculated breathing traces compared with real-time calculated breathing traces, we evaluate the inaccuracies of real-time phase calculation by measuring the phase gate position in each trace as well as the mean in-gate displacement and standard deviation of the displacement. Retrospectively-calculated data from ten patients were analyzed. The patient averaged in-gate mean +/- standard deviation displacement (representing residual motion) was reduced from 0.16 +/- 0.14 cm for phase gating under simulation conditions to 0.12 +/- 0.08 cm for displacement gating. Evaluation of respiration traces under treatment conditions (real-time phase calculation) showed that the average displacement gate threshold results in a lower in-gate mean and residual motion (variance) for all patients studied. The patient-averaged in-gate mean +/- standard deviation displacement was reduced from 0.26 +/- 0.18 cm for phase gating (under treatment conditions) to 0.15 +/- 0.09 cm for displacement gating. Real-time phase gating sometimes leads to gating on incorrect portions of the breathing cycle when the breathing trace is irregular. Displacement gating is less prone to such errors, as evidenced by the lower in-gate residual motion in a large majority of cases.

Segmenting lung tumors on longitudinal imaging studies via a patient-specific adaptive convolutional neural network.

PURPOSE: To design a deep learning algorithm that automatically delineates lung tumors seen on weekly magnetic resonance imaging (MRI) scans acquired during radiotherapy and facilitates the analysis of geometric tumor changes. METHODS: This longitudinal imaging study comprised 9 lung cancer patients who had 6-7 weekly T2-weighted MRI scans during radiotherapy. Tumors on all scans were manually contoured as the ground truth. Meanwhile, a patient-specific adaptive convolutional neural network (A-net) was developed to simulate the workflow of adaptive radiotherapy and to utilize past weekly MRI and tumor contours to segment tumors on the current weekly MRI. To augment the training data, each voxel inside the volume of interest was expanded to a 3 × 3 cm patch as the input, whereas the classification of the corresponding patch, background or tumor, was the output. Training was updated weekly to incorporate the latest MRI scan. For comparison, a population-based neural network was implemented, trained, and validated on the leave-one-out scheme. Both algorithms were evaluated by their precision, DICE coefficient, and root mean square surface distance between the manual and computerized segmentations. RESULTS: Training of A-net converged well within 2 h of computations on a computer cluster. A-net segmented the weekly MR with a precision, DICE, and root mean square surface distance of 0.81 ±â€¯0.10, 0.82 ±â€¯0.10, and 2.4 ±â€¯1.4 mm, and outperformed the population-based algorithm with 0.63 ±â€¯0.21, 0.64 ±â€¯0.19, and 4.1 ±â€¯3.0 mm, respectively. CONCLUSION: A-net can be feasibly integrated into the clinical workflow of a longitudinal imaging study and become a valuable tool to facilitate decision- making in adaptive radiotherapy.