Patrick Kupelian

Publications (203) View all

• Article: SU-E-J-58: Patient-Specific Biomechanical Head and Neck Models for Interfraction Dose Accumulation.

  A Santhanam, D Ennis, D Low, P Kupelian
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  ABSTRACT: Purpose: In this abstract, we discuss a biomechanical head and neck model that will be able to represent patient setup variations as well as physiologic changes and subsequently enable dose calculations on the deformed anatomy. Methods: We selected Multi Pose MRI as the imaging modality to aid in model development and validation. The MRI data allowed us to build a biomechanically predictive model that will enable accurate estimation of tumor position when seeded with CT data alone. The soft tissue contrast and lack of ionizing radiation when using MRI enabled us to acquire extensive imaging datasets with a suitable variety of head pose variations. These poses were selected to encompass the clinical positioning variations so that the resulting model will accurately reflect internal organ motion and deformation. All images were acquired using an 8-channel, 1.5T research MRI system in radiology. The imaging volume extended from about T3(upper thoracic vertebrae) to the top of the head, thereby covering the entire head and neck. Model components included: muscles, skeletal bones, lymph nodes, fat tissues, and organs such as salivary glands, tendons, andligaments. At first, one MRI image dataset was selected as the reference image. The biometric properties (length, volume, mass, shape), hinge constraints of the bones, and the biomechanical properties of each of the anatomies were estimated using MRIs acquired at different head and neck poses. Results: The model's ability to represent different head and neck postures can be illustrated by observing the internal tissue deformations andthe model's ability to represent different postures. Conclusions: Results show that the biomechanical model was able to simulate different poses that may be exhibited during interfraction patient setup variations and intrafraction patient motion. Future work would focus on integrating dose calculations on the deforming model and validating the model deformations.
  Medical Physics 06/2012; 39(6):3665. · 2.83 Impact Factor
• Article: SU-E-J-98: 3D Tracking of Interfraction Patient Setup Uncertainties Using Multiple Kinect Sensors.

  A Santhanam, D Low, P Kupelian
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  ABSTRACT: Purpose: On-board optical 3D imaging enables measuring daily setup patient uncertainties without involving any additional imaging-induced radiation dose to critical structures. We hypothesize that the tumor and normal organ deformation caused by routine patient head and neck misalignments can be determined by coupling a quantitative patient-specific biomechanical model with quantitative skin surface 3D imaging. Methods: A set of 3D cameras are used to track the patient anatomy externally. One of the cameras employed a marker less face recognition and tracking for delineating the region of the patient's face. The location of the face was then shared among the camera controllers in real-time and the anatomical contour that closely matches the face region is selected and integrated to form a single 3D anatomical representation. Patient surface aligning was performed between the patient's external surface obtained from a reference 3D anatomy (simulation CT, MRI, patient surface map from previous fraction) and the above-mentioned camera system to quantify the daily patient setup variations. For each of the 3D patient surface, a point feature histogram (PFH) was first generated. Once the PFH descriptors were generated, a non-rigid iterative closest point registration algorithm that minimizes the difference in the PFH descriptor aligns the patient surface to the reference 3D anatomy. Results: The proposed tracking system was able to track both the patient surface setup uncertainty and the internal anatomy when coupledwith a patient specific biomechanical head and neck model. Conclusions: A 3D head and neck tracking system that monitors the interfraction patient setup uncertainties in the head and neck cancer patient is presented. The aligning process was shown to perform for cases with and without the head immobilization system. The external patient surface manifold and the motion vectors will be coupled to align the biomechanical model using model-guided techniques.
  Medical Physics 06/2012; 39(6):3675. · 2.83 Impact Factor
• Article: TU-G-BRA-05: Intra-Fraction Motion Management for Prostate SBRT: Clinical Experience and Imaging Frequency Analysis.

  N Agazaryan, G Sharma, P Kupelian, M Steinberg, C King
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  ABSTRACT: Purpose: To clinically evaluate the intra-fraction motion management performance of an IGRT protocol established for hypo-fractionated SBRT prospective Phase IIa trial for the treatment of localized prostate cancer. Specifically, to analyze patient data to determine the adequacy of imaging frequency. Methods: Novalis Tx equipped with Exactrac was used for stereoscopic imaging and localization based on three implanted fiducial markers. For intra-fraction motion management, two nearly 360-degree RapidArcs were split to four half-arcs. Following initial Exactrac positioning, CBCT is obtained for volumetric evaluation of bladder and rectal filling and position confirmation. The patient is then stereoscopically imaged prior to the delivery of each half-arc and repositioned when 2 mm tolerance is exceeded. Data from 66 patients with 330 fractions and 2597 image pairs has been analyzed. Results: Following the initial Exactrac and CBCT, mean treatment time from first arc to treatment end was 6.7 mins. ver the course of 66 treatments, patients were repositioned on 257 occasions. On average patients were repositioned 11.9% of the time (SD 10.0%, range 0-40.5%). The mean distance these patients were repositioned was 3.5 mm (SD 1.7 mm, range 2.0-8.5 mm). Of all repositions, 53.5% (SD 29.2%, range 0-100%) occurred before delivery of first arc; in addition, patient repositioning frequency following any half-arc was 9.1% (SD 9.9%, range 0-45%) over the treatment course. Nine patients did not require repositioning throughout the treatment course while nine patients required repositioning more than 25% of the time. Conclusions: Current imaging protocol for intra-fraction motion management fits the clinical workflow. Frequency analysis indicates that the intra-fraction imaging is not excessive. Due to the time spent on performing and analyzing the additional CBCT after initial Exactrac localization, 53.5% of repositions occur preceding first arc. Future analysis will include quantitative dosimetric consequences and tolerances utilized for repositioning patients in this study. Member of Brainlab Academy Speakers Bureau Varian Industry Grant - Rapid Arc: Radisurgical and SBRT Applications, May 2009- May 2011.
  Medical Physics 06/2012; 39(6):3922. · 2.83 Impact Factor
• Article: MO-D-217BCD-01: Personalizing Medicine: Adapting to the Individual.

  K Brock, J Balter, M Joiner, P Kupelian
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  ABSTRACT: Personalizing medicine through patient-specific adaptation is quickly moving from retrospective research to clinical implementation. The commercial availability of clinical tools, including auto-segmentation, deformable registration, and dose accumulation, is enabling these techniques to be utilized more efficiently. Understanding the importance, rationale, and consequences of anatomical and physiological based adaptation is paramount for the safe implementation of these techniques. This includes accounting for radiobiological differences in delivered dose and the impact that this may have on tumor control and normal tissue response. This interactive session will highlight the evidence and rationale for anatomy-based adaptation, including retrospective studies from several anatomical sites indicating the uncertainties between the planned and delivered dose and the benefits achievable through adaptation. Translation of these techniques into the clinic will be discussed. The growing use of functional imaging enables more sophisticated adaptation and personalization of the treatment plan based on an understanding of the individual response of the tumor and normal tissue to radiation. Methods to understand and incorporate this information into the patient treatment plan will be discussed. The radiobiological impact of dose accumulation methods and adaptive strategies is often overlooked. Biological factors and their influence on these adaptive strategies will be addressed. The clinician's perspective will also be highlighted, including the benefits of dose accumulation, personalization, and adaptation for the patient and the impact that this technology may have on clinical trials and outcomes assessment.Learning Objectives:1. Understand the need for anatomy-based adaptation and methods to safely implement this in the clinic2. Recognize the need for physiological-based adaptation and methods to safely implement this into the clinic3. Appreciate the radiobiological limitations and concerns associated with dose summation, and adaptation4. Describe the clinical implications of dose summation and adaptation on individual patient treatments, clinical trials, and outcomes assessment.
  Medical Physics 06/2012; 39(6):3864-3865. · 2.83 Impact Factor
• Article: TH-E-BRCD-04: Impact of Automatic Planning from Clinician's Perspective.

  P Kupelian
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  ABSTRACT: Recent advances in optimization and machine learning methods, it is now conceivable that the design of an individual treatment plan can be made with little, if any, human intervention. Adding autosegmentation processes to automated planning will result in dramatic increase in the efficiency and consistency of individual plans. Once the anatomic information, through imaging, is acquired for planning purposes, the majority of the steps required for the generation of the optimal plan could be automated. Such efforts are already being pursued at many institutions. However, since treatment plan design is one of the most important steps affecting the quality of a delivered treatment, human intervention, or at least supervision, will be crucial for the gradual development of confidence in these automated processes. In this talk, I will provide my insights on the aspects of automated treatment planning that would be addressed for this practice to become an integral part of the future practice of radiation therapy.Learning Objectives:1. Understand the concerns related to the implementation and practice of automated treatment planning from a clinician's perspective.2. Understand the impact of automated treatment planning on improving quality and consistence of radiation therapy from a clinician's perspective.
  Medical Physics 06/2012; 39(6):4008. · 2.83 Impact Factor