In my current role as an assistant professor in the Department of Human Oncology, I strive to apply knowledge and expertise from several interdisciplinary fields to better serve cancer patients. I feel privileged to be part of a large and talented team of physicists and physicians here at UW Madison and to have the opportunity to serve at the main campus and also at UW Cancer Center–Johnson Creek. My goal is to ensure that all patients we treat at this satellite clinic receive the same level of care that they would at the main clinic. As a medical physicist, I apply strict quality assurance procedures to the radiation delivery machines as well as standard of practice procedures in conjunction with guidelines outlined at the main campus. I try to devise models for improving the radiation delivery mechanisms for better tumor control and less dose to normal critical organs.
Residency, University of Wisconsin–Madison, Radiation Oncology Medical Physics (2008)
PhD, University of Wisconsin–Madison, Medical Physics (2010)
MS, University of Wisconsin–Madison, Medical Physics (2009)
Radiological Physics Training, Radiological Physics and Advisory Division, Bhabha Atomic Research Center, Mumbai, India., Radiological Physics (1996)
MS, Delhi University, Physics (1995)
BS, Delhi University, Physics (1993)
Assistant Professor (CHS), Human Oncology (2014)
Associate Researcher, Human Oncology (2010)
Assistant Researcher, Human Oncology (2008)
Research Scholar, Human Oncology (2006)
Consultant Medical Physicist, Indraprastha Apollo Hospital, Delhi, India (2002)
Medical Physicist, Gujarat Cancer and Research Institute, Gujarat, India (1998)
Selected Honors and Awards
Technology Transfer Fellowship, UICC International Union Against Cancer supported by American Society of Clinical Oncology (2005)
Technical expert for giving technical introduction of Radiation Oncology Department of Hemlata Hospitals and Research Center Bhubaneswar Orissa India during inauguration by The President of India, Dr. APJ Abdul Kalam. December 2005 (2005)
Boards, Advisory Committees and Professional Organizations
Life Member Association of Medical Physicists of India (AMPI, India)
Full Member American Association of Physicists in Medicine (AAPM, USA)
Reviewer Medical Physics (AAPM, USA)
Reviewer Journal of Medical Physics (AMPI, India)
Reviewer Physics Medicine in Biology (IOP, UK)
Reviewer Technology in Cancer Research and Treatment (TCRT, USA)
Reviewer Journal of Applied Clinical Medical Physics (JACMP, USA)
Reviewer Journal of Medical Engineering & Physics (IPEM, UK)
Reviewer Radiological Physics and Technology (Springer, Japan)
Member International Advisory Committee for 36th Annual Conference of the Association of Medical Physicists of India (AMPI, India) - AMPICON 2015
Theragnostic Imaging, Informatics and Computer Support, Integrated Database 4DCT and Motion Management Using Nonlinear Dynamical Systems, Image Registration for Treatment Planning & Delivery, Deformable Registration, 4D Treatment Plan Optimization & Treatment Delivery Tools, Pretreatment Image Based Setup Verification, Adaptive Treatment Delivery, Dose Reconstruction, Pretreatment Image Based Setup Verification, Adaptive Treatment Delivery
My research is aimed at providing comprehensive support for theragnostic treatment plan optimization, motion managed treatment delivery and quality assurance for the personalized radiotherapy of cancer patients.
My lifelong goal is to continue the path of elevated level of research in the field of radiation therapy. Millions of patients around the world are diagnosed with cancer every year. More than 50 percent of them require radiation therapy to manage their disease. During the last five decades, radiation therapy has advanced with modern technology to focus radiation to target tumors with more accuracy than ever before. This has enabled us to further increase dose (dose escalation), which can potentially reduce relapse/increase survival for radiosensitive tumors. Relapse is still a widespread problem in treatments due to our inability to precisely delineate the location and extent of tumors. Moreover, it is unknown how quickly and differently tumors respond to radiation insult. Cellular characteristics and dynamic processes for tumor progression and response to fractionated or single fraction is neither well defined nor well studied. I have a unique knowledge base and opportunity to exploit a newly developed MRI-guided conformal radiation technology to a lesser-known area of research: dynamics of tumor response to radiation. My aim is to apply my skills and research tools to exploit, assess and further develop this novel technology to initiate temporal change of the tumor characteristics (cellular), volume,and local environment (vascular).
Developing breathing teacher based on nonlinear dynamics theory so lung cancer patients’ breath easily more reproducibly to mitigate tumor motion so we can escalate dose for better tumor control
Developing breathing synchronized delivery techniques for moving tumors to reduce the dose to critical organs and maximizing the dose to tumor
Radiomics based assessment of radiation induced lung injury in lung cancer patients
Radiomics and AI based study to predict recurrence pattern in lung cancer patients
MOSFET dosimeter characterization in MR-guided radiation therapy (MRgRT) Linac Journal of applied clinical medical physics
Yadav P, Hallil A, Tewatia D, Dunkerley AP, Paliwal B
2020 Jan;21(1):127-135. doi: 10.1002/acm2.12799. Epub 2019 Dec 18.
PURPOSE: With the increasing use of MR-guided radiation therapy (MRgRT), it becomes important to understand and explore accuracy of medical dosimeters in the presence of magnetic field. The purpose of this work is to characterize metal-oxide-semiconductor field-effect transistors (MOSFETs) in MRgRT systems at 0.345 T magnetic field strength.
METHODS: A MOSFET dosimetry system, developed by Best Medical Canada for in-vivo patient dosimetry, was used to study various commissioning tests performed on a MRgRT system, MRIdian® Linac. We characterized the MOSFET dosimeter with different cable lengths by determining its calibration factor, monitor unit linearity, angular dependence, field size dependence, percentage depth dose (PDD) variation, output factor change, and intensity modulated radiation therapy quality assurance (IMRT QA) verification for several plans. MOSFET results were analyzed and compared with commissioning data and Monte Carlo calculations.
RESULTS: MOSFET measurements were not found to be affected by the presence of 0.345 T magnetic field. Calibration factors were similar for different cable length dosimeters either placed at the parallel or perpendicular direction to the magnetic field, with variations of less than 2%. The detector showed good linearity (R2 = 0.999) for 100-600 MUs range. Output factor measurements were consistent with ionization chamber data within 2.2%. MOSFET PDD measurements were found to be within 1% for 1-15 cm depth range in comparison to ionization chamber. MOSFET normalized angular response matched thermoluminescent detector (TLD) response within 5.5%. The IMRT QA verification data for the MRgRT linac showed that the percentage difference between ionization chamber and MOSFET was 0.91%, 2.05%, and 2.63%, respectively for liver, spine, and mediastinum.
CONCLUSION: MOSFET dosimeters are not affected by the 0.345 T magnetic field in MRgRT system. They showed physics parameters and performance comparable to TLD and ionization chamber; thus, they constitute an alternative to TLD for real-time in-vivo dosimetry in MRgRT procedures.
PMID:31854078 | PMC:PMC6964768 | DOI:10.1002/acm2.12799
View details for PubMedID 31854078
SU-E-J-146: Time Series Prediction of Lung Cancer Patients' Breathing Pattern Based on Nonlinear Dynamics Medical physics
Tolakanahalli R, Tewatia D, Tome W
2012 Jun;39(6Part8):3686. doi: 10.1118/1.4734982.
PURPOSE: Prediction methods for breathing patterns, which are crucial to deal with system latency in treatments of moving lung tumors using state-space methodologies based on non-linear dynamics are contrasted to linear predictive methods.
METHOD AND MATERIALS: In our previous work we established that breathing patterns can be described as a 5-6 dimensional nonlinear, stationary and deterministic system that exhibits sensitive dependence on initial conditions. In this work, nonlinear prediction methods are used to predict the short-term evolution of the respiratory system for 3 patients. Single step and N-point multi step prediction are performed for sampling rates of 5Hz, 10Hz, and 30Hz. We compare the employed nonlinear prediction methods with respect to prediction accuracy to Infinite Impulse Response (IIR) prediction filters. The simplest form of local prediction is finding similar segments of scalar time series data in a higher dimensional embedding space. Hence, we predict the future value x(t)of N-time steps ahead by simply finding the average of nearest neighbor points to the point x(t) in the past and using them to estimate x(t+N), yielding a local average model (LAM). Local linear models (LLM) which are linear autoregressive models that hold only for a region around the target point formed by the nearest neighbor points is combined with a set of linear regularization techniques to solve ill-posed regression problems are also implemented.
RESULTS: For all sampling frequencies, both single step and N-point multi step prediction results obtained using LAM and LLM with regularization methods are better than IIR prediction filters for the selected sample patients.
CONCLUSIONS: The use of non-linear prediction methods for predicting the breathing pattern of lung cancer patients may lead to improved, robust and accurate long-term prediction to account for system latencies.
PMID:28518937 | DOI:10.1118/1.4734982
View details for PubMedID 28518937
SU-E-J-150: To Design a Methodology Based on Numerical Phantom for Reconstruction of Dose Delivered to Moving Lung Tumors Medical physics
Tewatia D, Tolakanahalli R
2012 Jun;39(6Part8):3687. doi: 10.1118/1.4734987.
PURPOSE: To design a methodology based on numerical phantom for reconstruction of dose delivered to moving lung tumors.
METHODS: MatlabTM 7.6 was used to generate a 4D numerical lung phantom (NLP). Customer parameter files were used as input to this NLP, which consists of multiple ellipsoids representing body, lung, cord and tumor. In this study, we studied the impact of varying breathing pattern on a left lower lobe tumor, where the tumor motion was simulated on the daily breathing pattern of the patient acquired using real time positioning management (RPMTM) system from Varian Medical Systems. Based on the daily breathing pattern, the original RPM signal and the original tumor trajectory, 5 sets of motion trajectories were simulated. This was then used to build 10 different phases of the numerical phantom. Average Intensity Projection (AIP) was then generated from the different phases. The actual delivered dose on the 5 AIP sets were compared to the intended dose on the original planning AIP image set.
RESULTS: The mean target coverage (TC) recomputed on the 5 AIP sets was approximately 18% lower than the TC for the planning AIP image set. The mean homogeneity index (HI) recomputed on the 5 sets, was approximately 5 times higher than HI for the planning AIP image set. The lung NTDmean dose was approximately 9.5 Gy3 and did not differ much.
CONCLUSIONS: The presented numerical simulation framework may assist in monitoring the changes in dose accumulation due to changes in the patient's breathing on a daily basis. This can also be used for validation of new motion tracking algorithms and its impact of dose coverage.
PMID:28518928 | DOI:10.1118/1.4734987
View details for PubMedID 28518928
SU-E-J-144: Recurrence Quantification Analysis of Lung Cancer Patients' Breathing Pattern Medical physics
Tolakanahalli R, Tewatia D, Tome W
2012 Jun;39(6Part8):3685-3686. doi: 10.1118/1.4734980.
PURPOSE: To demonstrate that Recurrence quantification analysis (RQA) can be used as a quantitative decision making tool to classify patients breathing pattern and select treatment strategy for maneuvering the tumor motion : (a) MIP based treatment (b) 4D treatment using non-linear prediction only (c) 4D treatment non-linear control prediction and breathing control.
METHOD AND MATERIALS: In our previous work we established that breathing patterns can be described as a 5-6 dimensional nonlinear, stationary and deterministic system that exhibits sensitive dependence on initial conditions. Recurrence plots enable one to investigate an m-dimensional state space trajectory through a two-dimensional representation of its recurrences where the value of a specific pixel is 1 if the distance between the two corresponding trajectory points is less than a threshold value ε. Important measures calculated are: Recurrence Rate (RR), %Determinism, Divergence, Shannon Entropy, LMean, and Renyi entropy (K2). Time Resolved RQA: By implementing a sliding window design, each of the above calculated parameters is computed multiple times. Alignment of those parameters with the time series reveals details not obvious in the 1 -dimensional time series data. The breathing pattern for seven randomly chosen volunteers were recorded using the RPM system for 15 minutes. Non-linear prediction was performed and the normalized root mean square error (NRMSE) was calculated for each of the volunteer data.
RESULTS: The threshold value ε was chosen such that the Recurrence Rate was equal to 1%. There is a strong correlation of NRMSE with Entropy, Determinism and LMean. Time resolved RR locates strong Unstable Periodic Orbits(UPOs), i.e. patterns of uninterrupted equally spaced diagonal lines.
CONCLUSIONS: RQAs could prove to be a very powerful tool for design of personalized treatment regimen. Entropy, Determinism in conjunction with strong UPOs can be used to determine if patients are suitable candidates for prediction and chaos control.
PMID:28518905 | DOI:10.1118/1.4734980
View details for PubMedID 28518905
Time series prediction of lung cancer patients' breathing pattern based on nonlinear dynamics Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Tolakanahalli RP, Tewatia DK, Tomé WA
2015 May;31(3):257-65. doi: 10.1016/j.ejmp.2015.01.018. Epub 2015 Feb 26.
This study focuses on predicting breathing pattern, which is crucial to deal with system latency in the treatments of moving lung tumors. Predicting respiratory motion in real-time is challenging, due to the inherent chaotic nature of breathing patterns, i.e. sensitive dependence on initial conditions. In this work, nonlinear prediction methods are used to predict the short-term evolution of the respiratory system for 62 patients, whose breathing time series was acquired using respiratory position management (RPM) system. Single step and N-point multi step prediction are performed for sampling rates of 5 Hz and 10 Hz. We compare the employed non-linear prediction methods with respect to prediction accuracy to Adaptive Infinite Impulse Response (IIR) prediction filters. A Local Average Model (LAM) and local linear models (LLMs) combined with a set of linear regularization techniques to solve ill-posed regression problems are implemented. For all sampling frequencies both single step and N-point multi step prediction results obtained using LAM and LLM with regularization methods perform better than IIR prediction filters for the selected sample patients. Moreover, since the simple LAM model performs as well as the more complicated LLM models in our patient sample, its use for non-linear prediction is recommended.
PMID:25726478 | DOI:10.1016/j.ejmp.2015.01.018
View details for PubMedID 25726478
Impact of energy variation on Cone Ratio, PDD10, TMR20/10 and IMRT doses for flattening filter free (FFF) beam of TomoTherapy Hi-Art(TM)machines Journal of B.U.ON. : official journal of the Balkan Union of Oncology
Tolakanahalli RP, Tewatia DK
PURPOSE: The beam energy (PDD10: Percent depth dose) of a Tomotherapy Hi-ArtTM machine was varied in a controlled experiment from -1.64 to +1.66%, while keeping the output at 100% and the effect of this on IMRT output, MU chamber ratio (MUR), cone ratio (CR) and Tissue Maximum Ratio (TMR20/10) was studied
METHODS: In this study, Injector Current Voltage (VIC) and Pulse Forming Network Voltage (VPFN) were changed in steps such that the PDD10 was varied from golden beam value incrementally between -1.64 to +1.66%. The effect of this on other energy indicators was studied to verify the sensitivity of TMR20/10, MUR, and detector data-based-CR. To quantify the effect of energy variation on Intensity Modulated Radiation Therapy (IMRT) dose, multiple ion-chamber based dose measurements were recorded by irradiating a cylindrical phantom with standard IMRT plans. Dose variation across each commissioned Field width (FW) was tabulated against energy variation.
RESULTS: Good agreement between PDD10 and TMR20/10, MUR, CR was observed. CR was more sensitive to energy change than PDD10. More variation was observed across standard IMRT plan with increasing energy.
CONCLUSION: CR is more sensitive to energy changes compared to PDD10, and CR with MUR can definitely be used as surrogates for checks on a daily/weekly basis. Variation in output across the 6 standard IMRT plans can vary up to 2.8% for a 1.6% increase in energy. Hence, it is of utmost importance to manage the PDD10 tightly around +0.5% in order to regulate standard IMRT QA agreement to within 1% and patient IMRT QA within ±3%.
View details for PubMedID 25536623
Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours Radiology research and practice
Chebrolu VV, Saenz D, Tewatia D, Sethares WA, Cannon G, Paliwal BR
2014;2014:547075. doi: 10.1155/2014/547075. Epub 2014 Aug 3.
Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase (512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037. In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.
PMID:25165581 | PMC:PMC4137600 | DOI:10.1155/2014/547075
View details for PubMedID 25165581
Time series analyses of breathing patterns of lung cancer patients using nonlinear dynamical system theory Physics in medicine and biology
Tewatia DK, Tolakanahalli RP, Paliwal BR, Tomé WA
2011 Apr 7;56(7):2161-81. doi: 10.1088/0031-9155/56/7/017. Epub 2011 Mar 9.
The underlying requirements for successful implementation of any efficient tumour motion management strategy are regularity and reproducibility of a patient's breathing pattern. The physiological act of breathing is controlled by multiple nonlinear feedback and feed-forward couplings. It would therefore be appropriate to analyse the breathing pattern of lung cancer patients in the light of nonlinear dynamical system theory. The purpose of this paper is to analyse the one-dimensional respiratory time series of lung cancer patients based on nonlinear dynamics and delay coordinate state space embedding. It is very important to select a suitable pair of embedding dimension 'm' and time delay 'τ' when performing a state space reconstruction. Appropriate time delay and embedding dimension were obtained using well-established methods, namely mutual information and the false nearest neighbour method, respectively. Establishing stationarity and determinism in a given scalar time series is a prerequisite to demonstrating that the nonlinear dynamical system that gave rise to the scalar time series exhibits a sensitive dependence on initial conditions, i.e. is chaotic. Hence, once an appropriate state space embedding of the dynamical system has been reconstructed, we show that the time series of the nonlinear dynamical systems under study are both stationary and deterministic in nature. Once both criteria are established, we proceed to calculate the largest Lyapunov exponent (LLE), which is an invariant quantity under time delay embedding. The LLE for all 16 patients is positive, which along with stationarity and determinism establishes the fact that the time series of a lung cancer patient's breathing pattern is not random or irregular, but rather it is deterministic in nature albeit chaotic. These results indicate that chaotic characteristics exist in the respiratory waveform and techniques based on state space dynamics should be employed for tumour motion management.
PMID:21389355 | DOI:10.1088/0031-9155/56/7/017
View details for PubMedID 21389355
Hippocampal-sparing whole-brain radiotherapy: a "how-to" technique using helical tomotherapy and linear accelerator-based intensity-modulated radiotherapy International journal of radiation oncology, biology, physics
Gondi V, Tolakanahalli R, Mehta MP, Tewatia D, Rowley H, Kuo JS, Khuntia D, Tomé WA
2010 Nov 15;78(4):1244-52. doi: 10.1016/j.ijrobp.2010.01.039.
PURPOSE: Sparing the hippocampus during cranial irradiation poses important technical challenges with respect to contouring and treatment planning. Herein we report our preliminary experience with whole-brain radiotherapy using hippocampal sparing for patients with brain metastases.
METHODS AND MATERIALS: Five anonymous patients previously treated with whole-brain radiotherapy with hippocampal sparing were reviewed. The hippocampus was contoured, and hippocampal avoidance regions were created using a 5-mm volumetric expansion around the hippocampus. Helical tomotherapy and linear accelerator (LINAC)-based intensity-modulated radiotherapy (IMRT) treatment plans were generated for a prescription dose of 30 Gy in 10 fractions.
RESULTS: On average, the hippocampal avoidance volume was 3.3 cm(3), occupying 2.1% of the whole-brain planned target volume. Helical tomotherapy spared the hippocampus, with a median dose of 5.5 Gy and maximum dose of 12.8 Gy. LINAC-based IMRT spared the hippocampus, with a median dose of 7.8 Gy and maximum dose of 15.3 Gy. On a per-fraction basis, mean dose to the hippocampus (normalized to 2-Gy fractions) was reduced by 87% to 0.49 Gy(2) using helical tomotherapy and by 81% to 0.73 Gy(2) using LINAC-based IMRT. Target coverage and homogeneity was acceptable with both IMRT modalities, with differences largely attributed to more rapid dose fall-off with helical tomotherapy.
CONCLUSION: Modern IMRT techniques allow for sparing of the hippocampus with acceptable target coverage and homogeneity. Based on compelling preclinical evidence, a Phase II cooperative group trial has been developed to test the postulated neurocognitive benefit.
PMID:20598457 | PMC:PMC2963699 | DOI:10.1016/j.ijrobp.2010.01.039
View details for PubMedID 20598457
Advances in radiation therapy dosimetry Journal of medical physics
Paliwal B, Tewatia D
2009 Jul;34(3):108-16. doi: 10.4103/0971-6203.54842.
During the last decade, there has been an explosion of new radiation therapy planning and delivery tools. We went through a rapid transition from conventional three-dimensional (3D) conformal radiation therapy to intensity-modulated radiation therapy (IMRT) treatments, and additional new techniques for motion-adaptive radiation therapy are being introduced. These advances push the frontiers in our effort to provide better patient care; and with the addition of IMRT, temporal dimensions are major challenges for the radiotherapy patient dosimetry and delivery verification. Advanced techniques are less tolerant to poor implementation than are standard techniques. Mis-administrations are more difficult to detect and can possibly lead to poor outcomes for some patients. Instead of presenting a manual on quality assurance for radiation therapy, this manuscript provides an overview of dosimetry verification tools and a focused discussion on breath holding, respiratory gating and the applications of four-dimensional computed tomography in motion management. Some of the major challenges in the above areas are discussed.
PMID:20098555 | PMC:PMC2807673 | DOI:10.4103/0971-6203.54842
View details for PubMedID 20098555
Dose calculation on kV cone beam CT images: an investigation of the Hu-density conversion stability and dose accuracy using the site-specific calibration Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Rong Y, Smilowitz J, Tewatia D, Tomé WA, Paliwal B
2010 Autumn;35(3):195-207. doi: 10.1016/j.meddos.2009.06.001. Epub 2009 Jul 15.
Precise calibration of Hounsfield units (HU) to electron density (HU-density) is essential to dose calculation. On-board kV cone beam computed tomography (CBCT) imaging is used predominantly for patients' positioning, but will potentially be used for dose calculation. The impacts of varying 3 imaging parameters (mAs, source-imager distance [SID], and cone angle) and phantom size on the HU number accuracy and HU-density calibrations for CBCT imaging were studied. We proposed a site-specific calibration method to achieve higher accuracy in CBCT image-based dose calculation. Three configurations of the Computerized Imaging Reference Systems (CIRS) water equivalent electron density phantom were used to simulate sites including head, lungs, and lower body (abdomen/pelvis). The planning computed tomography (CT) scan was used as the baseline for comparisons. CBCT scans of these phantom configurations were performed using Varian Trilogy system in a precalibrated mode with fixed tube voltage (125 kVp), but varied mAs, SID, and cone angle. An HU-density curve was generated and evaluated for each set of scan parameters. Three HU-density tables generated using different phantom configurations with the same imaging parameter settings were selected for dose calculation on CBCT images for an accuracy comparison. Changing mAs or SID had small impact on HU numbers. For adipose tissue, the HU discrepancy from the baseline was 20 HU in a small phantom, but 5 times lager in a large phantom. Yet, reducing the cone angle significantly decreases the HU discrepancy. The HU-density table was also affected accordingly. By performing dose comparison between CT and CBCT image-based plans, results showed that using the site-specific HU-density tables to calibrate CBCT images of different sites improves the dose accuracy to approximately 2%. Our phantom study showed that CBCT imaging can be a feasible option for dose computation in adaptive radiotherapy approach if the site-specific calibration is applied.
PMID:19931031 | DOI:10.1016/j.meddos.2009.06.001
View details for PubMedID 19931031
Clinical implementation of target tracking by breathing synchronized delivery Medical physics
Tewatia D, Zhang T, Tome W, Paliwal B, Metha M
2006 Nov;33(11):4330-6. doi: 10.1118/1.2359228.
Target-tracking techniques can be categorized based on the mechanism of the feedback loop. In real time tracking, breathing-delivery phase correlation is provided to the treatment delivery hardware. Clinical implementation of target tracking in real time requires major hardware modifications. In breathing synchronized delivery (BSD), the patient is guided to breathe in accordance with target motion derived from four-dimensional computed tomography (4D-CT). Violations of mechanical limitations of hardware are to be avoided at the treatment planning stage. Hardware modifications are not required. In this article, using sliding window IMRT delivery as an example, we have described step-by-step the implementation of target tracking by the BSD technique: (1) A breathing guide is developed from patient's normal breathing pattern. The patient tries to reproduce this guiding cycle by following the display in the goggles; (2) 4D-CT scans are acquired at all the phases of the breathing cycle; (3) The average tumor trajectory is obtained by deformable image registration of 4D-CT datasets and is smoothed by Fourier filtering; (4) Conventional IMRT planning is performed using the images at reference phase (full exhalation phase) and a leaf sequence based on optimized fluence map is generated; (5) Assuming the patient breathes with a reproducible breathing pattern and the machine maintains a constant dose rate, the treatment process is correlated with the breathing phase; (6) The instantaneous average tumor displacement is overlaid on the dMLC position at corresponding phase; and (7) DMLC leaf speed and acceleration are evaluated to ensure treatment delivery. A custom-built mobile phantom driven by a computer-controlled stepper motor was used in the dosimetry verification. A stepper motor was programmed such that the phantom moved according to the linear component of tumor motion used in BSD treatment planning. A conventional plan was delivered on the phantom with and without motion. The BSD plan was also delivered on the phantom that moved with the prescheduled pattern and synchronized with the delivery of each beam. Film dosimetry showed underdose and overdose in the superior and inferior regions of the target, respectively, if the tumor motion is not compensated during the delivery. BSD delivery resulted in a dose distribution very similar to the planned treatments.
PMID:17153412 | DOI:10.1118/1.2359228
View details for PubMedID 17153412
Dinesh Tewatia, PhD600 Highland Avenue, K4/b57,
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