SBIR-STTR Award

During radiation therapy for prostate cancer it is common practice to localize the prostate on many, ideally all, days of treatment to achieve the goal of delivering a high dose to the prostate while greatly sparing nearby radiosensitive normal tissues. The prostate can be localized in CT images acquired immediately prior to treatment or by tracking markers, implanted in the prostate, during treatment delivery. The advantage of CT imaging is that the image data provides the basis for computing radiation doses actually delivered to the prostate and surrounding tissues as needed for Adaptive Radiation Therapy (ART), a procedure for periodically adjusting the treatment plan in order to deliver a final dose distribution as originally planned. Disadvantages of CT imaging include monetary, space and time expenses. Major advantages of marker tracking include ease of use and frequent sampling during each dose fraction, e.g., 10 Hz, potentially allowing dynamic adjustment of treatment parameters. Unfortunately the absence of image data from tracking systems prevents the practice of ART, and for some patients may preclude the use of smaller margins around the prostate that would allow better sparing of nearby normal tissues. The hypothesis of the proposed research is that the prostate markers localized during treatment delivery can be used as the basis for mapping reference CT image data into the treatment space to estimate pre-treatment CT image data acceptable for calculating delivered dose. In particular the markers will be used for computing the image-match term in a Bayesian-like framework to optimize the non-rigid registration of a statistically trainable deformable shape model of a prostate, including immediately surrounding tissues, with marker positions in the treatment space. A patient-specific model, called an m-rep, created from the reference planning image for the patient being treated will embed the underlying image data, including prostate-related marker coordinates, in the model-related coordinate system unique to m- reps. The deformed m-rep created by the registration process to match the markers located during treatment implies a transformation that maps the entire reference image data to the treatment space to estimate the pre-treatment CT image. The tissue region in and around the prostate is mapped diffeomorphically. The overall aim is to establish proof of concept for estimating pre-treatment CT images acceptable for calculating delivered dose as described above

Public Health Relevance:
The hypothesis of the proposed research is that prostate markers localized during radiation therapy treatment delivery can be used as the basis for mapping reference CT image data into the treatment space to estimate pre-treatment CT image data acceptable for calculating delivered dose. In particular the markers will be used for computing the image-match term in a Bayesian-like framework to optimize the non-rigid registration of a statistically trainable deformable shape model of a prostate, including immediately surrounding tissues, with marker positions in the treatment space. A patient- specific model, called an m-rep, created from the reference planning image for the patient being treated will embed the underlying image data, including prostate-related marker coordinates, in the model-related coordinate system unique to m-reps. The deformed m-rep created by the registration process to match the markers located during treatment implies a transformation that maps the entire reference image data to the treatment space to estimate the pre-treatment CT image. The tissue region in and around the prostate is mapped diffeomorphically. The overall aim is to establish proof of concept for estimating pre-treatment CT images acceptable for calculating delivered dose as described above.

Public Health Relevance Statement:
Narrative The hypothesis of the proposed research is that prostate markers localized during radiation therapy treatment delivery can be used as the basis for mapping reference CT image data into the treatment space to estimate pre-treatment CT image data acceptable for calculating delivered dose. In particular the markers will be used for computing the image-match term in a Bayesian-like framework to optimize the non-rigid registration of a statistically trainable deformable shape model of a prostate, including immediately surrounding tissues, with marker positions in the treatment space. A patient- specific model, called an m-rep, created from the reference planning image for the patient being treated will embed the underlying image data, including prostate-related marker coordinates, in the model-related coordinate system unique to m-reps. The deformed m-rep created by the registration process to match the markers located during treatment implies a transformation that maps the entire reference image data to the treatment space to estimate the pre-treatment CT image. The tissue region in and around the prostate is mapped diffeomorphically. The overall aim is to establish proof of concept for estimating pre-treatment CT images acceptable for calculating delivered dose as described above.

Project Terms:
Agreement; Anterior; Bladder; Body Tissues; Cancer Radiotherapy; Cancer of Prostate; Computer Programs; Computer software; Data; Disadvantaged; Dose; Genital System, Male, Prostate; Goals; Human Prostate; Human Prostate Gland; Image; Implant; Location; Malignant Tumor of the Prostate; Malignant neoplasm of prostate; Malignant prostatic tumor; Maps; Methods; Modeling; Normal Tissue; Normal tissue morphology; Patients; Phase; Position; Positioning Attribute; Procedures; Process; Prostate; Prostate CA; Prostate Cancer; Prostate Gland; Prostatic Cancer; Prostatic Gland; Radiation; Radiation therapy; Radiotherapeutics; Radiotherapy; Research; Sampling; Shapes; Software; Spottings; Surface; System; System, LOINC Axis 4; Time; Tissues; Treatment Period; Urinary System, Bladder; base; computer program/software; develop software; developing computer software; imaging; irradiation; prevent; preventing; prototype; public health relevance; ray (radiation); rectal; software development; software systems; treatment days; treatment duration; treatment planning; urinary bladder

The prostate is a mobile organ that demonstrates momentary and daily changes in position and shape that critically affect accurate delivery of radiation therapy. For this reason, in modern technically demanding radiation therapy for prostate cancer, it is standard practice to localize the prostate on most days treatment as an aid to achieve the goal of delivering a high dose to the prostate while protecting nearby radiosensitive normal tissues. The Phase I study established the feasibility of a novel approach that uses unique mathematical properties of a class of statistically trainable deformable shape models to estimate treatment image data by mapping reference CT image data into the treatment space based on measured positions of markers implanted in the prostate. The estimated images capture the pose, position and shape of the prostate during treatment and are suitable for accurate calculation of dose delivered to the prostate and immediately surrounding tissues. The first two years of this Phase II study will focus on design and development of a clinical prototype system based on this creative technology. The prototype will be evaluated in the clinical setting during the third year. The envisioned commercial form will accept input data from multiple devices including the treatment planning system, treatment machine, and marker tracking system, and will communicate output data to the treatment planning system. Within a few seconds the proposed prototype will use input data to automatically compute the estimated treatment image and calculate delivered dose. The proposed system will leverage work accomplished under a currently active Phase II project (R44 CA119571) that will allow the prototype to be used with intra-treatment imaging devices as well as marker-trackers. Moreover the proposed approach may enable a form of dose computation, called synchronized dynamic dose reconstruction, needed for the most accurate form of ART.

Public Health Relevance:
The prostate is a mobile organ that demonstrates momentary and daily changes in position and shape that critically affect accurate delivery of radiation therapy. For this reason, in modern technically demanding radiation therapy for prostate cancer, it is standard practice to localize the prostate on most days treatment as an aid to achieve the goal of delivering a high dose to the prostate while protecting nearby radiosensitive normal tissues. The Phase I study established the feasibility of a novel approach that uses unique mathematical properties of a class of statistically trainable deformable shape models to estimate treatment image data by mapping reference CT image data into the treatment space based on measured positions of markers implanted in the prostate. The estimated images capture the pose, position and shape of the prostate during treatment and are suitable for accurate calculation of dose delivered to the prostate and immediately surrounding tissues. The first two years of this Phase II study will focus on design and development of a clinical prototype system based on this creative technology. The prototype will be evaluated in the clinical setting during the third year. The envisioned commercial form will accept input data from multiple devices including the treatment planning system, treatment machine, and marker tracking system, and will communicate output data to the treatment planning system. Within a few seconds the proposed prototype will use input data to automatically compute the estimated treatment image and calculate delivered dose. The proposed system will leverage work accomplished under a currently active Phase II project (R44 CA119571) that will allow the prototype to be used with intra-treatment imaging devices as well as marker-trackers. Moreover the proposed approach may enable a form of dose computation, called synchronized dynamic dose reconstruction, needed for the most accurate form of ART. This project will contribute to public health in two ways: 1) It will allow an intra-treatment-image-guided form of treatment planning called Adaptive Radiation Planning that assures a close match between the planned and actual delivered doses;and 2) It will provide a needed but currently missing quality assurance step to confirm the safety of the delivered dose for each treatment session. This contribution is especially significant in light of a series of recent mistreatments that have led many to believe that new planning and delivery technologies have outpaced the development of clinically practical methods to independently validate safe planning and delivery.

Thesaurus Terms:
Affect;Body Tissues;Clinical;Code;Coding System;Communication;Data;Development;Devices;Dose;Electronics;Elements;Goals;Image;Imaging Device;Imaging Tool;Implant;Loinc Axis 2 Property;Loinc Axis 4 System;Light;Malignant Tumor Of The Prostate;Malignant Neoplasm Of Prostate;Malignant Prostatic Tumor;Maps;Measures;Methods;Modeling;Normal Tissue;Normal Tissue Morphology;Organ;Output;Patients;Phase;Phase I Study;Photoradiation;Position;Positioning Attribute;Property;Prostate;Prostate Ca;Prostate Cancer;Prostate Gland;Prostatic Cancer;Prostatic Gland;Public Health;Radiation;Radiation Therapy;Radiotherapeutics;Radiotherapy;Records;Safety;Sampling;Series;Shapes;Speed;Speed (Motion);System;Technology;Tissues;Treatment Period;Work;Base;Design;Designing;Develop Software;Developing Computer Software;Developmental;Imaging;Maltreatment;Mistreatment;New Approaches;Novel Approaches;Novel Strategies;Novel Strategy;Phase 1 Study;Phase 2 Study;Phase Ii Study;Prevent;Preventing;Prototype;Public Health Medicine (Field);Quality Assurance;Ray (Radiation);Reconstruction;Software Development;Treatment Days;Treatment Duration;Treatment Planning