Anees Dhabaan - Academia.edu (original) (raw)
Papers by Anees Dhabaan
Journal of Applied Clinical Medical Physics, Nov 1, 2012
Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performe... more Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performed with comparable precision in a reasonable time frame. Here, we present a positioning approach for frameless radiosurgery based on in-room volumetric imaging coupled with an advanced six-degrees-of-freedom (6 DOF) image registration technique which avoids use of a bite block. Patient motion is restricted with a custom thermoplastic mask. Accurate positioning is achieved by registering a cone-beam CT to the planning CT scan and applying all translational and rotational shifts using a custom couch mount. System accuracy was initially verified on an anthropomorphic phantom. Isocenters of delineated targets in the phantom were computed and aligned by our system with an average accuracy of 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively. The accuracy in the rotational directions was 0.1°, 0.2°, and 0.1° in the pitch, roll, and yaw, respectively. An additional test was performed using the phantom in which known shifts were introduced. Misalignments up to 10 mm and 3° in all directions/rotations were introduced in our phantom and recovered to an ideal alignment within 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively, and within 0.3° in any rotational axis. These values are less than couch motion precision. Our first 28 patients with 38 targets treated over 63 fractions are analyzed in the patient positioning phase of the study. Mean error in the shifts predicted by the system were less than 0.5 mm in any translational direction and less than 0.3° in any rotation, as assessed by a confirmation CBCT scan. We conclude that accurate and efficient frameless radiosurgery positioning is achievable without the need for a bite block by using our 6 DOF registration method. This system is inexpensive compared to a couch-based 6 DOF system, improves patient comfort compared to systems that utilize a bite block, and is ideal for the treatment of pediatric patients with or without general anesthesia, as well as of patients with dental issues. From this study, it is clear that only adjusting for 4 DOF may, in some cases, lead to significant compromise in PTV coverage. Since performing the additional match with 6 DOF in our registration system only adds a relatively short amount of time to the overall process, we advocate making the precise match in all cases.
SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery
Medical Physics, 2015
Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning... more Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51%more » and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.« less
Journal of Applied Clinical Medical Physics, Jun 21, 2010
The objective was to evaluate the performance of a high-definition multileaf collimator (MLC) of ... more The objective was to evaluate the performance of a high-definition multileaf collimator (MLC) of 2.5 mm leaf width (MLC2.5) and compare to standard 5 mm leaf width MLC (MLC5) for the treatment of intracranial lesions using dynamic conformal arcs (DCA) technique with a dedicated radiosurgery linear accelerator. Simulated cases of spherical targets were created to study solely the effect of target volume size on the performance of the two MLC systems independent of target shape complexity. In addition, 43 patients previously treated for intracranial lesions in our institution were retrospectively planned using DCA technique with MLC2.5 and MLC5 systems. The gross tumor volume ranged from 0.07 to 40.57 cm3 with an average volume of 5.9 cm3. All treatment parameters were kept the same for both MLC-based plans. The plan evaluation was performed using figures of merits (FOM) for a rapid and objective assessment on the quality of the two treatment plans for MLC2.5 and MLC5. The prescriptio...
Medical Imaging 2018: Image Processing, Mar 5, 2018
We propose a denoising method of CT image based on low-rank sparse coding. The proposed method co... more We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.
Medical Imaging 2018: Physics of Medical Imaging, Mar 9, 2018
We propose a CBCT image quality improvement method based on anatomic signature and auto-context a... more We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.
International Journal of Radiation Oncology*Biology*Physics
factors for the patients with localized nasal NK/T-cell Lymphoma (ENKL) treated with radiation th... more factors for the patients with localized nasal NK/T-cell Lymphoma (ENKL) treated with radiation therapy with or without chemotherapy in Japan. Materials/Methods: This study was conducted from the multi-institutional retrospective series of the Next-Generation therapy for NK/T-cell lymphoma in East Asia (NKEA) project database with collaboration among Japanese hemato-oncologists and Japanese Radiation Oncology Study Group (JROSG) in Japan. There were retrospective series of patients treated with radiation therapy with or without chemotherapy between 2000 and 2013. The clinical target volume (CTV) of all patients was evaluated by the board certified radiation oncologists belonging to the JROSG lymphoma committee. PTI was evaluated by the two radiation oncologists. The overall survival rate (OS), progression free survival rate (PFS), and loco-regional control rate (LRC) were calculated using Kaplan-Meier method. We also investigated prognostic factors including PTI and sIL-2R using the log-rank test and hazard radio (HR) by Cox regression model. Results: One hundred sixty-four newly diagnosed ENKL patients of stage I or ll disease were selected. The majority of patients (72.6%) were treated with concurrent chemoradiation using dexamethasone, etoposide, ifosfamide, and carboplatin (RT-DeVIC). Median age of the total patients was 58 years (range: 17-88 years). Among the 86 patients (52.4%) who could evaluate the PTI, majority of patients (80.2%) had PTI. Median follow up time was 4.7 years (range: 0.6-13.9 years). The 5-year OS, PFS, LRC for total patients and RT-DeVIC group were 72.1%, 59.1%, 81.8%, and 73.2%, 64.4%, 86.1%, respectively. The univariate analysis revealed that the risk of PTI had significantly worse outcome in PFS in RT-DeVIC group (P Z 0.041). However, PTI is not statistically significant in the multivariate analysis. Multivariate analysis revealed that elevated sIL-2R was an independent prognostic factor for worse OS (P Z 0.003; HR, 2.983; 95% CI, 1.453 to 6.124), PFS (P < 0.001; HR, 2.627; 95% CI, 1.532 to 4.504), and LRC (P Z 0.021; HR, 3.026; 95% CI, 1.181 to 7.751) in total patients. Elevated sIL-2R was also statistically significant in RT-DeVIC group for OS, PFS, and LRC. Other independent prognostic factors in the total patients were smaller CTV for worse LRC and the Age > 60 years for worse OS. Conclusion: Our statistical significance of PTI on localized nasal ENKL was weak, because the majority of Japanese patients showed positive PTI. Elevated sIL-2R may be useful to predict the outcome for localized nasal ENKL patients with positive PTI in Japan.
Pretreatment Quality Assurance for Stereotactic Body Radiation Therapy (SBRT): Feasibility of Weekly “SBRT Rounds” in a High-Volume, Multicenter Academic Practice
International Journal of Radiation Oncology*Biology*Physics
International Journal of Radiation Oncology*Biology*Physics
Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precis... more Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precise image-guided radiation therapy (RT) because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. With more precise treatment monitoring from CBCT, dose delivery errors can be significantly reduced in each fraction or compensated for in subsequent fractions using adaptive RT. However, the current CBCT has severe artifacts mainly due to scatter contamination and its current clinical application is therefore limited to patient setup based only on bony structures. This study's purpose is to develop a learningbased approach to improve CBCT image quality for quantitative analysis during adaptive RT. Materials/Methods: The first step is to build a set of paired training images including planning CT and CBCT. For each pair, the planning CT is used as the regression target of the CBCT. We then remove the uninformative regions, reduce noise, and perform an alignment between CT and CBCT. The proposed correction algorithm consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative CT-CBCT features are identified by feature selection to train random forests. During the correction stage, we extract the selected features from the new (target) CBCT and feed them into the well-trained forests to predict the corrected CBCT. This prediction-based correction algorithm was tested with brain CBCT and CT images of 9 patients. We performed leave-one-out cross-validation method to evaluate the proposed prediction-based correction algorithm. Results: The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the corrected CBCT and planning CT. For 9 patients, the mean MAE, PSNR, and FSIM were 5.87AE4.39, 28.47AE4.71, and 0.90AE0.08, respectively, which demonstrated the corrected CBCT prediction accuracy of the proposed learning-based method. Conclusion: To improve CBCT imaging, we have developed a novel learning-based method in which a random forest regression with a patchbased anatomical signature is used to effectively capture the relationship between the planning CT and CBCT. We have demonstrated that this method could significantly reduce scatter artifacts. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore allowing its quantitative use in adaptive RT.
International Journal of Radiation Oncology*Biology*Physics, 2016
Materials/Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analys... more Materials/Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analysis, Pareto chart, and control chart) were utilized to determine the steps that need focus for improving the patient-specific QA process. The patient-specific range QA plans were selected according to 7 treatment site groups, a total of 1437 cases. The process capability index, C pm was used to guide the tolerance design of patient site-specific range. We also analyzed the financial impact of this project. The cost was estimate using the Activity-Based Costing model. The cost in treatmentrelated activities is divided into four categories: simulation, planning (including the QA and evaluation processes), manufacturing (including the field accessories such as compensator, block), and treatment delivery. Results: Our results suggested that the patient range measurements were non-capable at the current tolerance level of AE1 mm in clinical proton plans and we concluded that we should customize the range tolerance for each treatment sites. The optimized tolerances were AE1.5 mm for head & neck, AE2.6 mm for spine and breast, AE2.5 mm for lung, AE2.7 mm for liver, AE3.0 mm for pancreas, AE1.0 mm for prostate treatments. Control charts for the patient QA time were constructed to compare QA time before and after the new tolerances were implemented. For 10 random liver and pancreas cases the average QA time improved from 115 min to less than 87 min for all steps-planning and converting process, depth-dose measurement, and evaluation. It is found that overall processing time was decreased by 24.3% after establishing new site-specific range tolerances. The QA failure for whole process in proton therapy would lead up to a 46% increase in total cost. This result can also predict how costs are affected by changes in adopting the tolerance design. We often believe that the quality and performance of proton therapy can easily be improved by merely tightening some or all of its tolerance requirements. This can become costly, however, and it is not necessarily a guarantee of better performance. The tolerance design is not a task to be undertaken without careful thought. In fact, it is recommended that only after using statistical tools for extensive QA data analysis should tolerance design be carried out as a last resort to improve the QA process. Conclusion: The Six Sigma DMAIC can be used to improve the QA process by setting optimized tolerances. When tolerance design is optimized, the quality is reasonably balanced with time and cost demands.
Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy
Physics in Medicine & Biology
A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems
Medical Physics
Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy
Medical Dosimetry
Prospective International Pilot Study Evaluating the Efficacy of a Self-Guided Contouring Teaching Module With Integrated Feedback for Transitioning From 2D to 3D Treatment Planning
Journal of Global Oncology
PURPOSE Transitioning from two-dimensional to three-dimensional treatment planning requires devel... more PURPOSE Transitioning from two-dimensional to three-dimensional treatment planning requires developing contouring skills. Contouring atlases are excellent resources, but they do not provide users active feedback. Developing countries may not have many radiation oncologists experienced in three-dimensional planning to provide training. We sought to develop a standardized self-guided educational module with integrated feedback to teach contouring skills. METHODS AND MATERIALS All 18 oncology residents at Black Lion Hospital/Addis Ababa University in Ethiopia were trained to contour the level II lymph node station. Residents took a baseline pretest quiz, survey, and contouring evaluation. Residents then watched an instructional contouring lecture and performed three additional cases with integrated feedback by comparing their contours to gold-standard contours. Residents then took a post-training quiz, survey, and contouring evaluation. Paired t tests and analysis of variance were used...
Medical physics, Jan 24, 2018
Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radioth... more Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation and patient dose calculation. However, CBCT is currently limited to patient setup only in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. An auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alterna...
Journal of Neurosurgery
OBJECTIVEThe optimal margin size in postoperative stereotactic radiosurgery (SRS) for brain metas... more OBJECTIVEThe optimal margin size in postoperative stereotactic radiosurgery (SRS) for brain metastases is unknown. Herein, the authors investigated the effect of SRS planning target volume (PTV) margin on local recurrence and symptomatic radiation necrosis postoperatively.METHODSRecords of patients who received postoperative LINAC-based SRS for brain metastases between 2006 and 2016 were reviewed and stratified based on PTV margin size (1.0 or > 1.0 mm). Patients were treated using frameless and framed SRS techniques, and both single-fraction and hypofractionated dosing were used based on lesion size. Kaplan-Meier and cumulative incidence models were used to estimate survival and intracranial outcomes, respectively. Multivariate analyses were also performed.RESULTSA total of 133 patients with 139 cavities were identified; 36 patients (27.1%) and 35 lesions (25.2%) were in the 1.0-mm group, and 97 patients (72.9%) and 104 lesions (74.8%) were in the > 1.0–mm group. Patient char...
MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method
Medical dosimetry : official journal of the American Association of Medical Dosimetrists, Jan 14, 2018
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI pro... more Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation i...
Image-based metal artifact reduction in x-ray computed tomography utilizing local anatomical similarity
Medical Imaging 2017: Physics of Medical Imaging
A patch-based CBCT scatter artifact correction using prior CT
Medical Imaging 2017: Physics of Medical Imaging
Patient-specific quality assurance method for VMAT treatment delivery
Medical Physics, Oct 1, 2009
Volumetric modulated arc therapy (VMAT) is a system for intensity-modulated radiotherapy treatmen... more Volumetric modulated arc therapy (VMAT) is a system for intensity-modulated radiotherapy treatment delivery that achieves high dose conformality by optimizing the dose rate, gantry speed, and the leaf positions of the dynamic multileaf collimator (DMLC). The aim of this work is to present a practical approach for patient-specific volumetric reconstruction of the dose delivered of a VMAT treatment using the DMLC and treatment controller log (Dynalog) files. The accuracy of VMAT delivery was analyzed for five prostate patients. For each patient, a clinical treatment was delivered and values recorded in the log files for the gantry angle, dose rate, and leaf positions were converted to a new DICOM-compliant plan using a custom-developed software system. The plan was imported in a treatment planning system and the dose distribution was recreated on the original CT by simply recomputing the dose. Using the standard evaluation tools, it is straightforward to assess if reconstructed dose meets clinical endpoints, as well as to compare side-by-side reconstructed and original plans. The study showed that log files can be directly used for dose reconstruction without resorting to phantom measurements or setups. In all cases, analysis of the leaf positions showed a maximum error of -0.26 mm (mean of 0.15 mm). Gantry angle deviation was less than 1degree and the total MU was within 0.5 from the planned value. Differences between the reconstructed and the intended dose matrices were less than 1.46% for all cases. Measurements using the MATRIXX system in a phantom were used to validate the dosimetric accuracy of the proposed method, with an agreement of at least 96% in all pixels as measured using the gamma index. The methodology provides a volumetric evaluation of the dose reconstructed by VMAT plans which is easily achieved by automated analysis of Dynalog files without additional measurements or phantom setups. This process provides a valuable platform for adaptive therapy in the future.
TH-C-BRD-06: A Novel MRI Based CT Artifact Correction Method for Improving Proton Range Calculation in the Presence of Severe CT Artifacts
Medical Physics, 2014
ABSTRACT Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range... more ABSTRACT Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range thereby resulting in a clinically unacceptable treatment plan. In this work, we investigated a novel CT artifact correction method based on a coregistered MRI and investigated its ability to estimate CT HU and proton range in the presence of severe CT artifacts.
Journal of Applied Clinical Medical Physics, Nov 1, 2012
Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performe... more Frameless radiosurgery is an attractive alternative to the framed procedure if it can be performed with comparable precision in a reasonable time frame. Here, we present a positioning approach for frameless radiosurgery based on in-room volumetric imaging coupled with an advanced six-degrees-of-freedom (6 DOF) image registration technique which avoids use of a bite block. Patient motion is restricted with a custom thermoplastic mask. Accurate positioning is achieved by registering a cone-beam CT to the planning CT scan and applying all translational and rotational shifts using a custom couch mount. System accuracy was initially verified on an anthropomorphic phantom. Isocenters of delineated targets in the phantom were computed and aligned by our system with an average accuracy of 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively. The accuracy in the rotational directions was 0.1°, 0.2°, and 0.1° in the pitch, roll, and yaw, respectively. An additional test was performed using the phantom in which known shifts were introduced. Misalignments up to 10 mm and 3° in all directions/rotations were introduced in our phantom and recovered to an ideal alignment within 0.2 mm, 0.3 mm, and 0.4 mm in the lateral, vertical, and longitudinal directions, respectively, and within 0.3° in any rotational axis. These values are less than couch motion precision. Our first 28 patients with 38 targets treated over 63 fractions are analyzed in the patient positioning phase of the study. Mean error in the shifts predicted by the system were less than 0.5 mm in any translational direction and less than 0.3° in any rotation, as assessed by a confirmation CBCT scan. We conclude that accurate and efficient frameless radiosurgery positioning is achievable without the need for a bite block by using our 6 DOF registration method. This system is inexpensive compared to a couch-based 6 DOF system, improves patient comfort compared to systems that utilize a bite block, and is ideal for the treatment of pediatric patients with or without general anesthesia, as well as of patients with dental issues. From this study, it is clear that only adjusting for 4 DOF may, in some cases, lead to significant compromise in PTV coverage. Since performing the additional match with 6 DOF in our registration system only adds a relatively short amount of time to the overall process, we advocate making the precise match in all cases.
SU-D-BRB-01: A Predictive Planning Tool for Stereotactic Radiosurgery
Medical Physics, 2015
Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning... more Purpose: To demonstrate the feasibility of a predictive planning tool which provides SRS planning guidance based on simple patient anatomical properties: PTV size, PTV shape and distance from critical structures. Methods: Ten framed SRS cases treated at Winship Cancer Institute of Emory University were analyzed to extract data on PTV size, sphericity (shape), and distance from critical structures such as the brainstem and optic chiasm. The cases consisted of five pairs. Each pair consisted of two cases with a similar diagnosis (such as pituitary adenoma or arteriovenous malformation) that were treated with different techniques: DCA, or IMRS. A Naive Bayes Classifier was trained on this data to establish the conditions under which each treatment modality was used. This model was validated by classifying ten other randomly-selected cases into DCA or IMRS classes, calculating the probability of each technique, and comparing results to the treated technique. Results: Of the ten cases used to validate the model, nine had their technique predicted correctly. The three cases treated with IMRS were all identified as such. Their probabilities of being treated with IMRS ranged between 59% and 100%. Six of the seven cases treated with DCA were correctly classified. These probabilities ranged between 51%more » and 95%. One case treated with DCA was incorrectly predicted to be an IMRS plan. The model’s confidence in this case was 91%. Conclusion: These findings indicate that a predictive planning tool based on simple patient anatomical properties can predict the SRS technique used for treatment. The algorithm operated with 90% accuracy. With further validation on larger patient populations, this tool may be used clinically to guide planners in choosing an appropriate treatment technique. The prediction algorithm could also be adapted to guide selection of treatment parameters such as treatment modality and number of fields for radiotherapy across anatomical sites.« less
Journal of Applied Clinical Medical Physics, Jun 21, 2010
The objective was to evaluate the performance of a high-definition multileaf collimator (MLC) of ... more The objective was to evaluate the performance of a high-definition multileaf collimator (MLC) of 2.5 mm leaf width (MLC2.5) and compare to standard 5 mm leaf width MLC (MLC5) for the treatment of intracranial lesions using dynamic conformal arcs (DCA) technique with a dedicated radiosurgery linear accelerator. Simulated cases of spherical targets were created to study solely the effect of target volume size on the performance of the two MLC systems independent of target shape complexity. In addition, 43 patients previously treated for intracranial lesions in our institution were retrospectively planned using DCA technique with MLC2.5 and MLC5 systems. The gross tumor volume ranged from 0.07 to 40.57 cm3 with an average volume of 5.9 cm3. All treatment parameters were kept the same for both MLC-based plans. The plan evaluation was performed using figures of merits (FOM) for a rapid and objective assessment on the quality of the two treatment plans for MLC2.5 and MLC5. The prescriptio...
Medical Imaging 2018: Image Processing, Mar 5, 2018
We propose a denoising method of CT image based on low-rank sparse coding. The proposed method co... more We propose a denoising method of CT image based on low-rank sparse coding. The proposed method constructs an adaptive dictionary of image patches and estimates the sparse coding regularization parameters using the Bayesian interpretation. A low-rank approximation approach is used to simultaneously construct the dictionary and achieve sparse representation through clustering similar image patches. A variable-splitting scheme and a quadratic optimization are used to reconstruct CT image based on achieved sparse coefficients. We tested this denoising technology using phantom, brain and abdominal CT images. The experimental results showed that the proposed method delivers state-of-art denoising performance, both in terms of objective criteria and visual quality.
Medical Imaging 2018: Physics of Medical Imaging, Mar 9, 2018
We propose a CBCT image quality improvement method based on anatomic signature and auto-context a... more We propose a CBCT image quality improvement method based on anatomic signature and auto-context alternating regression forest. Patient-specific anatomical features are extracted from the aligned training images and served as signatures for each voxel. The most relevant and informative features are identified to train regression forest. The well-trained regression forest is used to correct the CBCT of a new patient. This proposed algorithm was evaluated using 10 patients’ data with CBCT and CT images. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross correlation (NCC) indexes were used to quantify the correction accuracy of the proposed algorithm. The mean MAE, PSNR and NCC between corrected CBCT and ground truth CT were 16.66HU, 37.28dB and 0.98, which demonstrated the CBCT correction accuracy of the proposed learning-based method. We have developed a learning-based method and demonstrated that this method could significantly improve CBCT image quality. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore, allowing its quantitative use in CBCT-guided adaptive radiotherapy.
International Journal of Radiation Oncology*Biology*Physics
factors for the patients with localized nasal NK/T-cell Lymphoma (ENKL) treated with radiation th... more factors for the patients with localized nasal NK/T-cell Lymphoma (ENKL) treated with radiation therapy with or without chemotherapy in Japan. Materials/Methods: This study was conducted from the multi-institutional retrospective series of the Next-Generation therapy for NK/T-cell lymphoma in East Asia (NKEA) project database with collaboration among Japanese hemato-oncologists and Japanese Radiation Oncology Study Group (JROSG) in Japan. There were retrospective series of patients treated with radiation therapy with or without chemotherapy between 2000 and 2013. The clinical target volume (CTV) of all patients was evaluated by the board certified radiation oncologists belonging to the JROSG lymphoma committee. PTI was evaluated by the two radiation oncologists. The overall survival rate (OS), progression free survival rate (PFS), and loco-regional control rate (LRC) were calculated using Kaplan-Meier method. We also investigated prognostic factors including PTI and sIL-2R using the log-rank test and hazard radio (HR) by Cox regression model. Results: One hundred sixty-four newly diagnosed ENKL patients of stage I or ll disease were selected. The majority of patients (72.6%) were treated with concurrent chemoradiation using dexamethasone, etoposide, ifosfamide, and carboplatin (RT-DeVIC). Median age of the total patients was 58 years (range: 17-88 years). Among the 86 patients (52.4%) who could evaluate the PTI, majority of patients (80.2%) had PTI. Median follow up time was 4.7 years (range: 0.6-13.9 years). The 5-year OS, PFS, LRC for total patients and RT-DeVIC group were 72.1%, 59.1%, 81.8%, and 73.2%, 64.4%, 86.1%, respectively. The univariate analysis revealed that the risk of PTI had significantly worse outcome in PFS in RT-DeVIC group (P Z 0.041). However, PTI is not statistically significant in the multivariate analysis. Multivariate analysis revealed that elevated sIL-2R was an independent prognostic factor for worse OS (P Z 0.003; HR, 2.983; 95% CI, 1.453 to 6.124), PFS (P < 0.001; HR, 2.627; 95% CI, 1.532 to 4.504), and LRC (P Z 0.021; HR, 3.026; 95% CI, 1.181 to 7.751) in total patients. Elevated sIL-2R was also statistically significant in RT-DeVIC group for OS, PFS, and LRC. Other independent prognostic factors in the total patients were smaller CTV for worse LRC and the Age > 60 years for worse OS. Conclusion: Our statistical significance of PTI on localized nasal ENKL was weak, because the majority of Japanese patients showed positive PTI. Elevated sIL-2R may be useful to predict the outcome for localized nasal ENKL patients with positive PTI in Japan.
Pretreatment Quality Assurance for Stereotactic Body Radiation Therapy (SBRT): Feasibility of Weekly “SBRT Rounds” in a High-Volume, Multicenter Academic Practice
International Journal of Radiation Oncology*Biology*Physics
International Journal of Radiation Oncology*Biology*Physics
Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precis... more Purpose/Objective(s): Quantitative Cone Bean CT (CBCT) imaging is on increasing demand for precise image-guided radiation therapy (RT) because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. With more precise treatment monitoring from CBCT, dose delivery errors can be significantly reduced in each fraction or compensated for in subsequent fractions using adaptive RT. However, the current CBCT has severe artifacts mainly due to scatter contamination and its current clinical application is therefore limited to patient setup based only on bony structures. This study's purpose is to develop a learningbased approach to improve CBCT image quality for quantitative analysis during adaptive RT. Materials/Methods: The first step is to build a set of paired training images including planning CT and CBCT. For each pair, the planning CT is used as the regression target of the CBCT. We then remove the uninformative regions, reduce noise, and perform an alignment between CT and CBCT. The proposed correction algorithm consists of two major stages: the training stage and the prediction stage. During the training stage, patch-based anatomical features are extracted from the registered training images with patient-specific information, and the most robust and informative CT-CBCT features are identified by feature selection to train random forests. During the correction stage, we extract the selected features from the new (target) CBCT and feed them into the well-trained forests to predict the corrected CBCT. This prediction-based correction algorithm was tested with brain CBCT and CT images of 9 patients. We performed leave-one-out cross-validation method to evaluate the proposed prediction-based correction algorithm. Results: The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and feature similarity (FSIM) indexes were used to quantify the differences between the corrected CBCT and planning CT. For 9 patients, the mean MAE, PSNR, and FSIM were 5.87AE4.39, 28.47AE4.71, and 0.90AE0.08, respectively, which demonstrated the corrected CBCT prediction accuracy of the proposed learning-based method. Conclusion: To improve CBCT imaging, we have developed a novel learning-based method in which a random forest regression with a patchbased anatomical signature is used to effectively capture the relationship between the planning CT and CBCT. We have demonstrated that this method could significantly reduce scatter artifacts. The proposed method has great potential in improving CBCT image quality to a level close to planning CT, therefore allowing its quantitative use in adaptive RT.
International Journal of Radiation Oncology*Biology*Physics, 2016
Materials/Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analys... more Materials/Methods: The Six Sigma tools (process flow diagram, cause and effect, capability analysis, Pareto chart, and control chart) were utilized to determine the steps that need focus for improving the patient-specific QA process. The patient-specific range QA plans were selected according to 7 treatment site groups, a total of 1437 cases. The process capability index, C pm was used to guide the tolerance design of patient site-specific range. We also analyzed the financial impact of this project. The cost was estimate using the Activity-Based Costing model. The cost in treatmentrelated activities is divided into four categories: simulation, planning (including the QA and evaluation processes), manufacturing (including the field accessories such as compensator, block), and treatment delivery. Results: Our results suggested that the patient range measurements were non-capable at the current tolerance level of AE1 mm in clinical proton plans and we concluded that we should customize the range tolerance for each treatment sites. The optimized tolerances were AE1.5 mm for head & neck, AE2.6 mm for spine and breast, AE2.5 mm for lung, AE2.7 mm for liver, AE3.0 mm for pancreas, AE1.0 mm for prostate treatments. Control charts for the patient QA time were constructed to compare QA time before and after the new tolerances were implemented. For 10 random liver and pancreas cases the average QA time improved from 115 min to less than 87 min for all steps-planning and converting process, depth-dose measurement, and evaluation. It is found that overall processing time was decreased by 24.3% after establishing new site-specific range tolerances. The QA failure for whole process in proton therapy would lead up to a 46% increase in total cost. This result can also predict how costs are affected by changes in adopting the tolerance design. We often believe that the quality and performance of proton therapy can easily be improved by merely tightening some or all of its tolerance requirements. This can become costly, however, and it is not necessarily a guarantee of better performance. The tolerance design is not a task to be undertaken without careful thought. In fact, it is recommended that only after using statistical tools for extensive QA data analysis should tolerance design be carried out as a last resort to improve the QA process. Conclusion: The Six Sigma DMAIC can be used to improve the QA process by setting optimized tolerances. When tolerance design is optimized, the quality is reasonably balanced with time and cost demands.
Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy
Physics in Medicine & Biology
A standardized commissioning framework of Monte Carlo dose calculation algorithms for proton pencil beam scanning treatment planning systems
Medical Physics
Dosimetric study on learning-based cone-beam CT correction in adaptive radiation therapy
Medical Dosimetry
Prospective International Pilot Study Evaluating the Efficacy of a Self-Guided Contouring Teaching Module With Integrated Feedback for Transitioning From 2D to 3D Treatment Planning
Journal of Global Oncology
PURPOSE Transitioning from two-dimensional to three-dimensional treatment planning requires devel... more PURPOSE Transitioning from two-dimensional to three-dimensional treatment planning requires developing contouring skills. Contouring atlases are excellent resources, but they do not provide users active feedback. Developing countries may not have many radiation oncologists experienced in three-dimensional planning to provide training. We sought to develop a standardized self-guided educational module with integrated feedback to teach contouring skills. METHODS AND MATERIALS All 18 oncology residents at Black Lion Hospital/Addis Ababa University in Ethiopia were trained to contour the level II lymph node station. Residents took a baseline pretest quiz, survey, and contouring evaluation. Residents then watched an instructional contouring lecture and performed three additional cases with integrated feedback by comparing their contours to gold-standard contours. Residents then took a post-training quiz, survey, and contouring evaluation. Paired t tests and analysis of variance were used...
Medical physics, Jan 24, 2018
Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radioth... more Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image-guided radiotherapy because it provides a foundation for advanced image-guided techniques, including accurate treatment setup, online tumor delineation and patient dose calculation. However, CBCT is currently limited to patient setup only in the clinic because of the severe issues in its image quality. In this study, we develop a learning-based approach to improve CBCT's image quality for extended clinical applications. An auto-context model is integrated into a machine learning framework to iteratively generate corrected CBCT (CCBCT) with high image quality. The first step is data preprocessing for the built training dataset, in which uninformative image regions are removed, noise is reduced, and CT and CBCT images are aligned. After a CBCT image is divided into a set of patches, the most informative and salient anatomical features are extracted to train random forests. Within each patch, alterna...
Journal of Neurosurgery
OBJECTIVEThe optimal margin size in postoperative stereotactic radiosurgery (SRS) for brain metas... more OBJECTIVEThe optimal margin size in postoperative stereotactic radiosurgery (SRS) for brain metastases is unknown. Herein, the authors investigated the effect of SRS planning target volume (PTV) margin on local recurrence and symptomatic radiation necrosis postoperatively.METHODSRecords of patients who received postoperative LINAC-based SRS for brain metastases between 2006 and 2016 were reviewed and stratified based on PTV margin size (1.0 or > 1.0 mm). Patients were treated using frameless and framed SRS techniques, and both single-fraction and hypofractionated dosing were used based on lesion size. Kaplan-Meier and cumulative incidence models were used to estimate survival and intracranial outcomes, respectively. Multivariate analyses were also performed.RESULTSA total of 133 patients with 139 cavities were identified; 36 patients (27.1%) and 35 lesions (25.2%) were in the 1.0-mm group, and 97 patients (72.9%) and 104 lesions (74.8%) were in the > 1.0–mm group. Patient char...
MRI-based treatment planning for brain stereotactic radiosurgery: Dosimetric validation of a learning-based pseudo-CT generation method
Medical dosimetry : official journal of the American Association of Medical Dosimetrists, Jan 14, 2018
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI pro... more Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast without ionizing radiation compared with computed tomography (CT). However, it requires the generation of pseudo CT from MRI images for patient setup and dose calculation. Our machine-learning-based method to generate pseudo CT images has been shown to provide pseudo CT images with excellent image quality, while its dose calculation accuracy remains an open question. In this study, we aim to investigate the accuracy of dose calculation in brain frameless stereotactic radiosurgery (SRS) using pseudo CT images which are generated from MRI images using the machine learning-based method developed by our group. We retrospectively investigated a total of 19 treatment plans from 14 patients, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation i...
Image-based metal artifact reduction in x-ray computed tomography utilizing local anatomical similarity
Medical Imaging 2017: Physics of Medical Imaging
A patch-based CBCT scatter artifact correction using prior CT
Medical Imaging 2017: Physics of Medical Imaging
Patient-specific quality assurance method for VMAT treatment delivery
Medical Physics, Oct 1, 2009
Volumetric modulated arc therapy (VMAT) is a system for intensity-modulated radiotherapy treatmen... more Volumetric modulated arc therapy (VMAT) is a system for intensity-modulated radiotherapy treatment delivery that achieves high dose conformality by optimizing the dose rate, gantry speed, and the leaf positions of the dynamic multileaf collimator (DMLC). The aim of this work is to present a practical approach for patient-specific volumetric reconstruction of the dose delivered of a VMAT treatment using the DMLC and treatment controller log (Dynalog) files. The accuracy of VMAT delivery was analyzed for five prostate patients. For each patient, a clinical treatment was delivered and values recorded in the log files for the gantry angle, dose rate, and leaf positions were converted to a new DICOM-compliant plan using a custom-developed software system. The plan was imported in a treatment planning system and the dose distribution was recreated on the original CT by simply recomputing the dose. Using the standard evaluation tools, it is straightforward to assess if reconstructed dose meets clinical endpoints, as well as to compare side-by-side reconstructed and original plans. The study showed that log files can be directly used for dose reconstruction without resorting to phantom measurements or setups. In all cases, analysis of the leaf positions showed a maximum error of -0.26 mm (mean of 0.15 mm). Gantry angle deviation was less than 1degree and the total MU was within 0.5 from the planned value. Differences between the reconstructed and the intended dose matrices were less than 1.46% for all cases. Measurements using the MATRIXX system in a phantom were used to validate the dosimetric accuracy of the proposed method, with an agreement of at least 96% in all pixels as measured using the gamma index. The methodology provides a volumetric evaluation of the dose reconstructed by VMAT plans which is easily achieved by automated analysis of Dynalog files without additional measurements or phantom setups. This process provides a valuable platform for adaptive therapy in the future.
TH-C-BRD-06: A Novel MRI Based CT Artifact Correction Method for Improving Proton Range Calculation in the Presence of Severe CT Artifacts
Medical Physics, 2014
ABSTRACT Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range... more ABSTRACT Purpose: Severe CT artifacts can impair our ability to accurately calculate proton range thereby resulting in a clinically unacceptable treatment plan. In this work, we investigated a novel CT artifact correction method based on a coregistered MRI and investigated its ability to estimate CT HU and proton range in the presence of severe CT artifacts.