Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

Objective. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed purposes, but poses a challenge, since unlike computed tomography, they do not occupy all dark regions on the image. The purpose of this study is to develop an automated method to segment the air regions on MRI-guided adaptive radiation therapy (MRgART) of abdominal tumors.Approach. A modified ResUNet3D deep learning (DL)-based auto air delineation model was trained using 102 patients' MR images. The MR images were acquired by a dedicated in-house sequence named 'Air-Scan', which is designed to generate air regions that are especially dark and accentuated. The air volumes generated by the newly developed DL model were compared with the manual air contours using geometric similarity (Dice Similarity Coefficient (DSC)), and dosimetric equivalence using Gamma index and dose-volume parameters.Main results. The average DSC agreement between the DL generated and manual air contours is 99% ± 1%. The gamma index between the dose calculations with overriding the DL versus manual air volumes with density of 0.01 is 97% ± 2% for a local gamma calculation with a tolerance of 2% and 2 mm. The dosimetric parameters from planning target volume-PTV and organs at risk-OARs were all within 1% between when DL versus manual contours were overridden by air density. The model runs in less than five seconds on a PC with 28 Core processor and NVIDIA Quadro®P2000 GPU.Significance: a DL based automated segmentation method was developed to generate air volumes on specialized abdominal MR images and generate results that are practically equivalent to the manual contouring of air volumes.

Development of a multi-layer quality assurance program to evaluate the uncertainty of deformable dose accumulation in adaptive radiotherapy.

PURPOSE: Deformable dose accumulation (DDA) has uncertainties which impede the implementation of DDA-based adaptive radiotherapy (ART) in clinic. The purpose of this study is to develop a multi-layer quality assurance (MLQA) program to evaluate uncertainties in DDA. METHODS: A computer program is developed to generate a pseudo-inverse displacement vector field (DVF) for each deformable image registration (DIR) performed in Accuray's PreciseART. The pseudo-inverse DVF is first used to calculate a pseudo-inverse consistency error (PICE) and then implemented in an energy and mass congruent mapping (EMCM) method to reconstruct a deformed dose. The PICE is taken as a metric to estimate DIR uncertainties. A pseudo-inverse dose agreement rate (PIDAR) is used to evaluate the consequence of the DIR uncertainties in DDA and the principle of energy conservation is used to validate the integrity of dose mappings. The developed MLQA program was tested using the data collected from five representative cancer patients treated with tomotherapy. RESULTS: DIRs were performed in PreciseART to generate primary DVFs for the five patients. The fidelity index and PICE of these DVFs on average are equal to 0.028 mm and 0.169 mm, respectively. With the criteria of 3 mm/3% and 5 mm/5%, the PIDARs of the PreciseART-reconstructed doses are 73.9 ± 4.4% and 87.2 ± 3.3%, respectively. The PreciseART and EMCM-based dose reconstructions have their deposited energy changed by 5.6 ± 3.9% and 2.6 ± 1.5% in five GTVs, and by 9.2 ± 7.8% and 4.7 ± 3.6% in 30 OARs, respectively. CONCLUSIONS: A pseudo-inverse map-based EMCM program has been developed to evaluate DIR and dose mapping uncertainties. This program could also be used as a sanity check tool for DDA-based ART.

A Prior Knowledge-Guided, Deep Learning-Based Semiautomatic Segmentation for Complex Anatomy on Magnetic Resonance Imaging.

PURPOSE: Despite recent substantial improvement in autosegmentation using deep learning (DL) methods, labor-intensive and time-consuming slice-by-slice manual editing is often needed, particularly for complex anatomy (eg, abdominal organs). This work aimed to develop a fast, prior knowledge-guided DL semiautomatic segmentation (DL-SAS) method for complex structures on abdominal magnetic resonance imaging (MRI) scans. METHODS AND MATERIALS: A novel application using contours on an adjacent slice as a prior knowledge informant in a 2-dimensional UNet DL model to guide autosegmentation for a subsequent slice was implemented for DL-SAS. A generalized, instead of organ-specific, DL-SAS model was trained and tested for abdominal organs on T2-weighted MRI scans collected from 75 patients (65 for training and 10 for testing). The DL-SAS model performance was compared with 3 common autocontouring methods (linear interpolation, rigid propagation, and a full 3-dimensional DL autosegmentation model trained with the same training data set) based on various quantitative metrics including the Dice similarity coefficient (DSC) and ratio of acceptable slices (ROA) using paired t tests. RESULTS: For the 10 testing cases, the DL-SAS model performed best with the slice interval (SI) of 1, resulting in an average DSC of 0.93 ± 0.02, 0.92 ± 0.02, 0.91 ± 0.02, 0.88 ± 0.03, and 0.87 ± 0.02 for the large bowel, stomach, small bowel, duodenum, and pancreas, respectively. The performance decreased with increased SIs from the guidance slice. The DL-SAS method performed significantly better (P < .05) than the other 3 methods. The ROA values were in the range of 48% to 66% for all the organs with an SI of 1 for DL-SAS, higher than those for linear interpolation (31%-57% for an SI of 1) and DL auto-segmentation (16%-51%). CONCLUSIONS: The developed DL-SAS model segmented complex abdominal structures on MRI with high accuracy and efficiency and may be implemented as an interactive manual contouring tool or a contour editing tool in conjunction with a full autosegmentation process, facilitating fast and accurate segmentation for MRI-guided online adaptive radiation therapy.

Development and implementation of an automatic air delineation technique for MRI-guided adaptive radiation therapy.

Objective.Auto-delineation of air regions on daily MRI for MR-guided online adaptive radiotherapy (MRgOART) of abdominal tumors is challenging since the air packets occur randomly and their MR intensities can be similar to some other tissue types. This work reports a new method to auto-delineate air regions on MRI.Approach.The proposed method (named DIFF method) consists of (1) generating a combined volumeVcomb, which is a union of the air-containing organs on a reference MR image offline, (2) transferringVcombfrom the reference MR to a daily MR via DIR, (3) combining the transferredVcombwith a region of high DIR inaccuracy, and (4) applying a threshold to the obtained final combined volume to generate the air volumes. The high DIR inaccuracy region was calculated from the absolute difference between the deformed daily and the reference images. This method was tested on 36 abdominal daily MRI sets acquired from 7 patients on a 1.5 T MR-Linac. The performance of DIFF was compared with alternative auto-air generation methods that (1) does not account for DIR inaccuracies, and (2) uses rigid registration instead of DIR.Main results.The results show that the proposed DIFF method can be fully automated and can be executed within 25 s. The Dice similarity coefficient of manual and DIFF auto-generated air contours was >92% for all cases, while it was 90% for the alternative auto-delineation methods. Dosimetrically, the auto-generated air regions using DIFF resulted in practically identical DVHs as those generated by using manual air contours.Significance.The DIFF method is robust and accurate and can be implemented to automatically consider the inter- and intra- fractional air volume variations during MRgOART for abdominal tumors. The use of DIFF method improves dosimetric accuracy as compared to other methods, especially beneficial for the patients with large daily abdominal air volume variations.

Multi-parametric magnetic resonance imaging for radiation treatment planning.

In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities, and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing, and quality assurance strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI-guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed.

Use of a DVH overlay technique for quality assurance of deformable image registration-based dose accumulation.

PURPOSE: We present a DVH overlay technique as a quality assurance (QA) metric for deformable image registration-based dose accumulation (DIR-DA). We use the technique to estimate the uncertainty in a DIR-DA for a revised treatment plan, and to compare two different DIR algorithms. MATERIALS AND METHODS: The required inputs to the DVH overlay workflow are deformably registered primary and secondary images, primary regions-of-interest (ROIs), and secondary dose distribution. The primary ROIs were forward warped to the secondary image, the secondary dose was inversely warped to the primary image, and the DVHs for each image were compiled. Congruent DVHs imply minimal inverse consistency error (ICE) within an ROI. For a pancreas case re-planned after 21 fractions of a 29-fraction course, the workflow was used to quantify dose accumulation error attributable to ICE, based on a hybrid contour-and-intensity-based DIR. The usefulness of the workflow was further demonstrated by assessing the performance of two DIR algorithms (one free-form intensity-based, FFIB, the other using normalized correlation coefficients, NCC, over small neighborhood patches) as applied toward kilovoltage computed tomography (kVCT)-to-megavoltage computed tomography (MVCT) registration and five-fraction dose accumulation of ten male pelvis cases. RESULTS: For the re-planned pancreas case, when applying the DVH-overlay-based uncertainties the resulting accumulated dose remained compliant with all but two of the original plan objectives. Among the male pelvis cases, FFIB and NCC DIR showed good invertibility within the planning target volume (PTV), according to the DVH overlay QA results. NCC DIR exhibited better invertibility for the bladder and rectum compared with FFIB. However, compared with FFIB, NCC DIR exhibited less regional deformation for the bladder and a tendency for increased local contraction of the rectum ROI. For the five-fraction summations, ICE for the PTV V100%Rx is comparable for both algorithms (FFIB 0.8 ± 0.7%, NCC 0.7 ± 0.3%). For the bladder and rectum V70%Rx , ICE is greater for FFIB (1.8 ± 0.7% for bladder, 1.7 ± 0.6% for rectum) than for NCC (1.0 ± 0.3% for bladder, 1.0 ± 0.4% for rectum). CONCLUSIONS: The DVH overlay technique identified instances in which a DIR exhibits favorable invertibility, implying low ICE in a DIR-based dose accumulation. Differences in the overlaid DVHs can also estimate dose accumulation errors attributable to ICE for given ROIs.

A Patient-Specific Autosegmentation Strategy Using Multi-Input Deformable Image Registration for Magnetic Resonance Imaging-Guided Online Adaptive Radiation Therapy: A Feasibility Study.

PURPOSE: Magnetic resonance-guided online adaptive radiation therapy (MRgOART) requires accurate and efficient segmentation. However, the performance of current autosegmentation tools is generally poor for magnetic resonance imaging (MRI) owing to day-to-day variations in image intensity and patient anatomy. In this study, we propose a patient-specific autosegmentation strategy using multiple-input deformable image registration (DIR; PASSMID) to improve segmentation accuracy and efficiency for MRgOART. METHODS AND MATERIALS: Longitudinal MRI scans acquired on a 1.5T MRI-Linac for 10 patients with abdominal cancer were used. The proposed PASSMID includes 2 steps: applying a patient-specific image processing pipeline to longitudinal MRI scans, and populating all contours from previous sessions/fractions to a new fractional MRI using multiple DIRs and combining the resulted contours using simultaneous truth and performance level estimation (STAPLE) to obtain the final consensus segmentation. Five contour propagation strategies were compared: planning computed tomography to fractional MRI scans through rigid body registration (RDR), pretreatment MRI to fractional MRI scans through RDR and DIR, and the proposed multi-input DIR/STAPLE without preprocessing, and the PASSMID. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) with ground truth contours were calculated slice by slice to quantify the contour accuracy. A quantitative index, defined as the ratio of acceptable slices, was introduced using a criterion of DSC > 0.8 and MDA < 2 mm. RESULTS: The proposed PASSMID performed well with an average 2-dimensional DSC/MDA of 0.94/1.78 mm, 0.93/1.04 mm, 0.93/1.06 mm, 0.93/1.14 mm, 0.92/0.83 mm, 0.84/1.53 mm, 0.86/2.39 mm, 0.81/2.49 mm, 0.72/5.48 mm, and 0.70/5.03 mm for the liver, left kidney, right kidney, spleen, aorta, pancreas, stomach, duodenum, small bowel, and colon, respectively. Starting from the third fractions, the contour accuracy was significantly improved with PASSMID compared with the single-DIR strategy (P < .05). The mean ratio of acceptable slices were 13.9%, 17.5%, 60.8%, 70.6%, and 71.8% for the 5 strategies, respectively. CONCLUSIONS: The proposed PASSMID solution, by combining image processing, multi-input DIRs, and STAPLE, can significantly improve the accuracy of autosegmentation for intrapatient MRI scans, reducing the time required for further contour editing, thereby facilitating the routine practice of MRgOART.

Initial clinical experience of Stereotactic Body Radiation Therapy (SBRT) for liver metastases, primary liver malignancy, and pancreatic cancer with 4D-MRI based online adaptation and real-time MRI monitoring using a 1.5 Tesla MR-Linac.

PURPOSE/OBJECTIVES: Recently a 1.5 Tesla MR Linac has been FDA approved and is commercially available. Clinical series describing treatment methods and outcomes for upper abdominal tumors using a 1.5 Tesla MR Linac are lacking. We present the first clinical series of upper abdominal tumors treated using a 1.5 Tesla MR Linac along with the acquisition of intra-treatment quantitative imaging. MATERIALS/METHODS: 10 patients with abdominal tumors were treated at our institution. Each patient enrolled in an IRB approved advanced imaging protocol. Both daily real-time adaptive and non-adaptive methods were used, and selection criteria are described. Adaptive plans were based on pre-beam motion-averaged or mid-position images derived from respiratory-correlated 4D-MRI. Quantitative intravoxel incoherent motion diffusion-weighted imaging and T2 mapping were acquired during plan adaptation. Real-time motion monitoring using cine MRI was performed during beam-on. RESULTS: Median patient age was 68.2, five patients were female. Tumor types included liver metastatic lesions from melanoma and sarcoma, primary liver hepatocellular carcinoma (HCC), and regional abdominal tumors included pancreatic metastatic lesions from renal cell carcinoma (RCC) along with two cases of recurrent pancreatic cancer. Doses included 30 Gy in 6 fractions, 33 Gy in 5 fractions, 50 Gy in 5 fractions, 45 Gy in 3 fractions, and 60 Gy in 3 fractions, depending on the location and clinical circumstances. Treatments were feasible and were successfully completed in all patients without significant acute toxicity, technical complications, or need for back up CT based treatment plans. CONCLUSIONS: We present a first clinical series of patients treated for pancreatic tumors, primary liver tumors, and secondary liver tumors with a 1.5 Tesla MR Linear accelerator using adapt-to-position and adapt-to-shape strategies. Treatments were well tolerated by all patients. Acquisition of fully quantitative MR imaging was feasible during the course of the treatment delivery workflow without extending overall treatment times.

4D-MRI driven MR-guided online adaptive radiotherapy for abdominal stereotactic body radiation therapy on a high field MR-Linac: Implementation and initial clinical experience.

BACKGROUND AND PURPOSE: In this report, we describe our implementation and initial clinical experience using 4D-MRI driven MR-guided online adaptive radiotherapy (MRgOART) for abdominal stereotactic body radiotherapy (SBRT) on the Elekta Unity MR-Linac. MATERIALS AND METHODS: Eleven patients with abdominal malignancies were treated with free-breathing SBRT in three to five fractions on a 1.5 T MR-Linac. Online adaptive plans were generated using Adapt-To-Position (ATP) or Adapt-To-Shape (ATS) workflows based on motion averaged or mid-position images derived from a pre-beam 4D-MRI. A high performance server positioned on the local MR-Linac machine network was utilized for 4D-MR image reconstruction. A parallel contour editing approach was employed in the ATS workflow. Intravoxel incoherent motion (IVIM) and T2 mapping sequences were acquired during adaptive planning in both ATP and ATS workflows for treatment response monitoring. Adaptive plans were delivered under real-time cine image motion monitoring. RESULTS: The shortest 4D-MRI time-to-image was the motion averaged image, followed by mid position and respiratory binned images. In this cohert of patients, 50% of treatments utilized the ATS workflow; the remaining treatments utilized the ATP workflow. Mid-position images were utilized as daily planning images for two of the eleven patients. The mean daily adaptive plan secondary dose calculation and ArcCheck 3D Gamma passing rates were 97.5% (92.1-100.0%) and 99.3% (96.2-100.0%), respectively. The median overall treatment times for abdominal SBRT was 46 and 62 min for ATP and ATS workflows, respectively. CONCLUSION: We have successfully implemented and utilized a 4D-MRI driven MRgOART process with ATP and ATS workflows for free-breathing abdominal SBRT on a 1.5 T Elekta Unity MR-Linac.

Indications of Online Adaptive Replanning Based On Organ Deformation.

PURPOSE: Although vital to account for interfractional variations during radiation therapy, online adaptive replanning (OLAR) is time-consuming and labor-intensive compared with the repositioning method. Repositioning is enough for minimal interfractional deformations. Therefore, determining indications for OLAR is desirable. We introduce a method to rapidly determine the need for OLAR by analyzing the Jacobian determinant histogram (JDH) obtained from deformable image registration between reference (planning) and daily images. METHODS AND MATERIALS: The proposed method was developed and tested based on daily computed tomography (CT) scans acquired during image guided radiation therapy for prostate cancer using an in-room CT scanner. Deformable image registration between daily and reference CT scans was performed. JDHs were extracted from the prostate and a uniform surrounding 10-mm expansion. A classification tree was trained to determine JDH metrics to predict the need for OLAR for a daily CT set. Sixty daily CT scans from 12 randomly selected prostate cases were used as the training data set, with dosimetric plans for both OLAR and repositioning used to determine their class. The resulting classification tree was tested using an independent data set of 45 daily CT scans from 9 other patients with 5 CT scans each. RESULTS: Of a total of 27 JDH metrics tested, 5 were identified predicted whether OLAR was substantially superior to repositioning for a given fraction. A decision tree was constructed using the obtained metrics from the training set. This tree correctly identified all cases in the test set where benefits of OLAR were obvious. CONCLUSIONS: A decision tree based on JDH metrics to quickly determine the necessity of online replanning based on the image of the day without segmentation was determined using a machine learning process. The process can be automated and completed within a minute, allowing users to quickly decide which fractions require OLAR.

Automated air region delineation on MRI for synthetic CT creation.

Automatically and accurately separating air from other low signal regions (especially bone, liver, etc) in an MRI is difficult because these tissues produce similar MR intensities, resulting in errors in synthetic CT generation for MRI-based radiation therapy planning. This work aims to develop a technique to accurately and automatically determine air-regions for MR-guided adaptive radiation therapy. CT and MRI scans (T2-weighted) of phantoms with fabricated air-cavities and abdominal cancer patients were used to establish an MR intensity threshold for air delineation. From the phantom data, air/tissue boundaries in MRI were identified by CT-MRI registration. A formula relating the MRI intensities of air and surrounding materials was established to auto-threshold air-regions. The air-regions were further refined by using quantitative image texture features. A naive Bayesian classifier was trained using the extracted features with a leave-one-out cross validation technique to differentiate air from non-air voxels. The multi-step air auto-segmentation method was tested against the manually segmented air-regions. The dosimetry impacts of the air-segmentation methods were studied. Air-regions in the abdomen can be segmented on MRI within 1 mm accuracy using a multi-step auto-segmentation method as compared to manually delineated contours. The air delineation based on the MR threshold formula was improved using the MRI texture differences between air and tissues, as judged by the area under the receiver operating characteristic curve of 81% when two texture features (autocorrelation and contrast) were used. The performance increased to 82% with using three features (autocorrelation, sum-variance, and contrast). Dosimetric analysis showed no significant difference between the auto-segmentation and manual MR air delineation on commonly used dose volume parameters. The proposed techniques consisting of intensity-based auto-thresholding and image texture-based voxel classification can automatically and accurately segment air-regions on MRI, allowing synthetic CT to be generated quickly and precisely for MR-guided adaptive radiation therapy.

A daily end-to-end quality assurance workflow for MR-guided online adaptive radiation therapy on MR-Linac.

PURPOSE: Magnetic Resonance (MR)-guided online adaptive radiation therapy (MRgOART), enabled with MR-Linac, has potential to revolutionize radiation therapy. MRgOART is a complex process. This work is to introduce a comprehensive end-to-end quality assurance (QA) workflow in routine clinic for MRgOART with a high-magnetic-field MR-Linac. MATERIALS AND METHOD: The major components in MRgOART with a high-magnetic field MR-Linac (Unity, Elekta) include: (1) a patient record and verification (R&V) system (e.g., Mosaiq, Elekta), (2) a treatment session manager, (3) an offline treatment planning system (TPS), (4) an online adaptive TPS, (5) a 1.5T MRI scanner, (6) an 7MV Linac, (7) an MV imaging controller (MVIC), and (8) ArtQA: software for plan data consistency checking and secondary dose calculation. Our end-to-end QA workflow was designed to test the performance and connectivity of all these components by transferring, adapting and delivering a specifically designed five-beam plan on a phantom. Beams 1-4 were designed to check Multi-Leaves Collimator (MLC) position shift based on rigid image registration in TPS, while beam 5 was used to check daily radiation output based on image pixel factor of MV image of the field. The workflow is initiated in the R&V system and followed by acquiring and registering daily MRI of the phantom, checking isocenter shift, performing online adaptive replanning, checking plan integrity and secondary 3D dose calculation, delivering the plan while acquiring MV imaging using MVIC, acquiring real-time images of the phantom, and checking the delivering parameters with ArtQA. RESULTS: It takes 10 min to finish the entire end-to-end QA workflow. The workflow has detected communication problems, permitted resolution prior to setting up patients for MRgOART. Up to 0.9 mm discrepancies in isocenter shift based on the image registration were detected. ArtQA performed the secondary 3D dose calculation, verified the plan integrity as well as the MR-MV isocenter alignment values in TPS. The MLC shapes of beam 1-4 in all adaptive plans were conformal to the target and agreed with MV images. The variation of daily output was within ±2.0%. CONCLUSIONS: The comprehensive end-to-end QA workflow can efficiently check the performance and communication between different components in MRgOART and has been successfully implemented for daily clinical practice.

Measurement validation of treatment planning for a MR-Linac.

PURPOSE: The magnetic field can cause a nonnegligible dosimetric effect in an MR-Linac system. This effect should be accurately accounted for by the beam models in treatment planning systems (TPS). The purpose of the study was to verify the beam model and the entire treatment planning and delivery process for a 1.5 T MR-Linac based on comprehensive dosimetric measurements and end-to-end tests. MATERIAL AND METHODS: Dosimetry measurements and end-to-end tests were performed on a preclinical MR-Linac (Elekta AB) using a multitude of detectors and were compared to the corresponding beam model calculations from the TPS for the MR-Linac. Measurement devices included ion chambers (IC), diamond detector, radiochromic film, and MR-compatible ion chamber array and diode array. The dose in inhomogeneous phantom was also verified. The end-to-end tests include the generation, delivery, and comparison of 3D and IMRT plan with measurement. RESULTS: For the depth dose measurements with Farmer IC, micro IC and diamond detector, the absolute difference between most measurement points and beam model calculation beyond the buildup region were <1%, at most 2% for a few measurement points. For the beam profile measurements, the absolute differences were no more than 1% outside the penumbra region and no more than 2.5% inside the penumbra region. Results of end-to-end tests demonstrated that three 3D static plans with single 5 × 10 cm2 fields (at gantry angle 0°, 90° and 270°) and two IMRT plans successfully passed gamma analysis with clinical criteria. The dose difference in the inhomogeneous phantom between the calculation and measurement was within 1.0%. CONCLUSIONS: Both relative and absolute dosimetry measurements agreed well with the TPS calculation, indicating that the beam model for MR-Linac properly accounts for the magnetic field effect. The end-to-end tests verified the entire treatment planning process.

A Technique to Rapidly Generate Synthetic Computed Tomography for Magnetic Resonance Imaging-Guided Online Adaptive Replanning: An Exploratory Study.

PURPOSE: To develop an automatic, accurate, atlas-based technique for synthetic computed tomography (sCT) generation to be used for online adaptive replanning during magnetic resonance imaging (MRI)-guided radiation therapy (RT). METHODS AND MATERIALS: The proposed method uses deformable image registration (DIR) of daily MRI and reference computed tomography (CT) with additional corrections to maintain bone rigidity and to transfer random air regions by thresholding. The DIR is performed with constraints on the bony structures using a special algorithm of ADMIRE (Elekta). The air regions are delineated from low-signal regions on the daily MRI and forced to air density. The bone regions in the MRI (already determined from the CT) are separated from the air regions because both bone and air have low signal density in MRI. All these steps are automated. The generated sCT is compared with reference CT and the alternative voxel-based CT (bCT) for 4 extracranial sites (head and neck, thorax, abdomen, pelvis) in terms of mean absolute error (MAE), gamma analysis of 3-dimensional doses, and dose volume histogram parameters. RESULTS: Both MAE and dosimetric analysis results were favorable for the proposed sCT generation method. The average MAE for the sCT/bCT were 25.5/66.7, 25.9/65.3, 24.8/44.2 and 16.6/47.7 for head and neck, thorax, abdomen, and pelvis, respectively, and the gamma analysis (1.5%, 2 mm) yielded 98.7/97.1, 99.1/93.9, 99.5/99.4, 99.7/99.4, respectively, for those sites. CONCLUSIONS: The proposed method generates equal or more accurate sCT than those from the bulk density assignment, without the need for multiple MRI sequences. This method can be fully automated and applicable for online adaptive replanning.

Technical Note: Acceleration of online adaptive replanning with automation and parallel operations.

PURPOSE: Limited by various human interventions and manual operations, the routine practice of online adaptive replanning (OLAR) is time consuming and impractical with the current planning technology. To accelerate OLAR and to minimize human efforts, the methods and software tools are developed to automate OLAR workflow and to use parallel processing during OLAR. METHODS: Speedy online adaptive replanning SOLAR, a software tool, was developed to automate the OLAR workflow and to implement parallel operation in plan generation and contour editing. The SOLAR tool is designed to automate and monitor the operation of OLAR on multiple client workstations and to allow parallel manual contour editing and plan generation with multiple workstations. The performance of the SOLAR tool was tested on selected prostate and pancreatic cancer cases. RESULTS: The SOLAR system has been tested in the clinical environment. With the automation and parallel operations, the operation time for OLAR can be reduced by 70%, allowing OLAR to be completed within 10 min for the tested prostate cancer cases and within 15 min for the pancreatic cancer cases. The SOLAR system generated superior plans compared to the standard repositioning method. CONCLUSION: SOLAR software was developed to accelerate online adaptive replanning workflow with automation and parallel operations. By reducing the time and human intervention, thus, reducing potential human error, the SOLAR solution would improve the practicality of OLAR.

A fast 4D IMRT/VMAT planning method based on segment aperture morphing.

PURPOSE: Four-dimensional volumetric modulated arc therapy (4D VMAT) and four-dimensional intensity-modulated radiotherapy (4D IMRT) are developing radiation therapy treatment strategies designed to maximize dose conformality, minimize normal tissue dose, and deliver the treatment as efficiently as possible. The patient's entire breathing cycle is captured through 4D imaging modalities and then separated into individual breathing phases for planning purposes. Optimizing multiphase VMAT and IMRT plans is computationally demanding and currently impractical for clinical application. The purpose of this study is to assess a new planning process decreasing the upfront computational time required to optimize multiphased treatment plans while maintaining good plan quality. METHODS: Optimized VMAT and IMRT plans were created on the end-of-exhale (EOE) breathing phase of 10-phase 4D CT scans with planning tumor volume (PTV)-based targets. These single-phase optimized plans are analogous to single-phase gated treatment plans. The simulated tracked plans were created by deformably registering EOE contours to the remaining breathing phases, recalculating the optimized EOE plan onto the other individual phases and realigning the MLC's relative positions to the PTV border in each of the individual breathing phases using a segment aperture morphing (SAM) algorithm. Doses for each of the 10 phases were calculated with the treatment planning system and deformably transferred back onto the EOE phase and averaged with equal weighting simulating the actual delivered dose a patient would potentially receive in a tracked treatment plan. RESULTS: Plan DVH quality for the 10-phase 4D SAM plans were comparable with the individual EOE optimized treatment plans for the PTV structures as well as the organ at risk structures. SAM-based algorithms out performed simpler isocenter-shifted only approaches. SAM-based 4D planning greatly reduced plan computation time vs individually optimizing all 10 phases. In addition, since this technique allows irradiation during all 10 breathing phases it will also decrease the treatment times required to treat each fraction in comparison to the gated treatment planning approach. CONCLUSIONS: Segment aperture morphing (SAM) can successfully be used to transfer radiation therapy plans originally planned on a single breathing phase image set to other patient breathing phase image sets. SAM is a useful tool for the fast creation of 4D, multibreathing phase radiation therapy treatment plans.

Management of independent motion between multiple targets in lung cancer radiation therapy.

PURPOSE: To quantify interfractional independent motions between multiple primary targets in radiation therapy (RT) of lung cancer and to study the dosimetric benefits of an online adaptive replanning method to account for these variations. METHODS AND MATERIALS: Ninety-five on-treatment diagnostic-quality computed tomography (CT) scans acquired for 9 lung cancer patients treated with image-guided RT (IGRT) using a CT-on-rails (CTVision, Siemens) were analyzed. On each on-treatment CT set, contours of the targets (gross tumor volume, clinical target volume, or involved nodes), and organs at risk were generated by populating the planning contours using an autosegmentation tool (ABAS, Elekta) with manual editing. For each patient, an intensity modulated RT plan was generated based on the planning CT with a prescription dose of 60 Gy in 2 Gy per fraction. Three plans were generated and compared for each on-treatment CT set: an IGRT (repositioning) plan by copying the original plan with the required shifts, an online adaptive plan by rapidly modifying the aperture shapes, and segment weights of the original plan to conform to the on-treatment anatomy and a new fully reoptimized plan based on the on-treatment CT. RESULTS: The interfractional deviations of the distance between centers of masses of the targets from the planning CTs varied from -1.0 to 0.8 cm with an average -0.09 ± 0.41 cm (1 standard deviation). The average combined CTV receiving at least 100% of the prescribed dose (V100) were 99.0 ± 0.7%, 97.8 ± 2.8%, 99.0 ± 0.6%, and 99.1 ± 0.6%, and the lung V20Gy 928 ± 332 cm3, 944 ± 315 cm3, 917 ± 300 cm3, and 891 ± 295 cm3 for the original, repositioning, adaptive, and reoptimized plans, respectively. Wilcoxon signed-rank tests showed that the adaptive plans were statistically significantly better than the repositioning plans and comparable with the reoptimized plans. CONCLUSION: Interfractional, relative volume changes and independent motions between multiple primary targets during lung cancer RT, which cannot be accounted for by the current IGRT repositioning exist, but can be corrected by the online adaptive replanning method.

Clinical experience with planning, quality assurance, and delivery of burst-mode modulated arc therapy.

"Burst-mode" modulated arc therapy (hereafter referred to as "mARC") is a form of volumetric-modulated arc therapy characterized by variable gantry rotation speed, static MLCs while the radiation beam is on, and MLC repositioning while the beam is off. We present our clinical experience with the planning techniques and plan quality assurance measurements of mARC delivery. Clinical mARC plans for five representative cases (prostate, low-dose-rate brain, brain with partial-arc vertex fields, pancreas, and liver SBRT) were generated using a Monte Carlo-based treatment planning system. A conventional-dose-rate flat 6 MV and a high-dose-rate non-flat 7 MV beam are available for planning and delivery. mARC plans for intact-prostate cases can typically be created using one 360° arc, and treatment times per fraction seldom exceed 6 min using the flat beam; using the nonflat beam results in slightly higher MU per fraction, but also in delivery times less than 4 min and with reduced mean dose to distal organs at risk. mARC also has utility in low-dose-rate brain irradiation; mARC fields can be designed which deliver a uniform 20 cGy dose to the PTV in approximately 3-minute intervals, making it a viable alternative to conventional 3D CRT. For brain cases using noncoplanar arcs, delivery time is approximately six min using the nonflat beam. For pancreas cases using the nonflat beam, two overlapping 360° arcs are required, and delivery times are approximately 10 min. For liver SBRT, the time to deliver 800 cGy per frac-tion is at least 12 min. Plan QA measurements indicate that the mARC delivery is consistent with the plan calculation for all cases. mARC has been incorporated into routine practice within our clinic; currently, on average approximately 15 patients per day are treated using mARC; and with the exception of LDR brain cases, all are treated using the nonflat beam.

An online replanning method using warm start optimization and aperture morphing for flattening-filter-free beams.

PURPOSE: In a situation where a couch shift for patient positioning is not preferred or prohibited (e.g., MR-linac), segment aperture morphing (SAM) can address target dislocation and deformation. For IMRT/VMAT with flattening-filter-free (FFF) beams, however, SAM method would lead to an adverse translational dose effect due to the beam unflattening. Here the authors propose a new two-step process to address both the translational effect of FFF beams and the target deformation. METHODS: The replanning method consists of an offline and an online step. The offline step is to create a series of preshifted-plans (PSPs) obtained by a so-called "warm start" optimization (starting optimization from the original plan, rather than from scratch) at a series of isocenter shifts. The PSPs all have the same number of segments with very similar shapes, since the warm start optimization only adjusts the MLC positions instead of regenerating them. In the online step, a new plan is obtained by picking the closest PSP or linearly interpolating the MLC positions and the monitor units of the closest PSPs for the shift determined from the image of the day. This two-step process is completely automated and almost instantaneous (no optimization or dose calculation needed). The previously developed SAM algorithm is then applied for daily deformation. The authors tested the method on sample prostate and pancreas cases. RESULTS: The two-step interpolation method can account for the adverse dose effects from FFF beams, while SAM corrects for the target deformation. Plan interpolation method is effective in diminishing the unflat beam effect and may allow reducing the required number of PSPs. The whole process takes the same time as the previously reported SAM process (5-10 min). CONCLUSIONS: The new two-step method plus SAM can address both the translation effects of FFF beams and target deformation, and can be executed in full automation except the delineation of target contour required by the SAM process.

Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams.

PURPOSE: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filter free (FFF) beams. METHODS: A software tool was developed to interface with a VMAT research planning system, which enables the input and output of beam and machine parameters of VMAT plans. The SAM algorithm was used to modify multileaf collimator positions for each segment aperture based on the changes of the target from the planning (CT/MR) to daily image [CT/CBCT/magnetic resonance imaging (MRI)]. The leaf travel distance was controlled for large shifts to prevent the increase of VMAT delivery time. The SAM algorithm was tested for 11 patient cases including prostate, pancreatic, and lung cancers. For each daily image set, three types of VMAT plans, image-guided radiation therapy (IGRT) repositioning, SAM adaptive, and full-scope reoptimization plans, were generated and compared. RESULTS: The SAM adaptive plans were found to have improved the plan quality in target and/or critical organs when compared to the IGRT repositioning plans and were comparable to the reoptimization plans based on the data of planning target volume (PTV)-V100 (volume covered by 100% of prescription dose). For the cases studied, the average PTV-V100 was 98.85% ± 1.13%, 97.61% ± 1.45%, and 92.84% ± 1.61% with FFF beams for the reoptimization, SAM adaptive, and repositioning plans, respectively. The execution of the SAM algorithm takes less than 10 s using 16-CPU (2.6 GHz dual core) hardware. CONCLUSIONS: The SAM algorithm can generate adaptive VMAT plans using FFF beams with comparable plan qualities as those from the full-scope reoptimization plans based on daily CT/CBCT/MRI and can be used for online replanning to address interfractional variations.