Anniversary Paper: A sampling of novel technologies and the role of medical physicists in radiation oncology

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135037/1/mp1006.pd

DYNAMIC MEASUREMENT OF THREE-DIMENSIONAL MOTION FROM SINGLE-PERSPECTIVE TWO-DIMENSIONAL RADIOGRAPHIC PROJECTIONS

The digital evolution of the x-ray imaging modality has spurred the development of numerous clinical and research tools. This work focuses on the design, development, and validation of dynamic radiographic imaging and registration techniques to address two distinct medical applications: tracking during image-guided interventions, and the measurement of musculoskeletal joint kinematics. Fluoroscopy is widely employed to provide intra-procedural image-guidance. However, its planar images provide limited information about the location of surgical tools and targets in three-dimensional space. To address this limitation, registration techniques, which extract three-dimensional tracking and image-guidance information from planar images, were developed and validated in vitro. The ability to accurately measure joint kinematics in vivo is an important tool in studying both normal joint function and pathologies associated with injury and disease, however it still remains a clinical challenge. A technique to measure joint kinematics from single-perspective x-ray projections was developed and validated in vitro, using clinically available radiography equipmen

DEVELOPMENT OF A PATIENT SPECIFIC IMAGE PLANNING SYSTEM FOR RADIATION THERAPY

A patient specific image planning system (IPS) was developed that can be used to assist in kV imaging technique selection during localization for radiotherapy. The IPS algorithm performs a divergent ray-trace through a three dimensional computed tomography (CT) data set. Energy-specific attenuation through each voxel of the CT data set is calculated and imaging detector response is integrated into the algorithm to determine the absolute values of pixel intensity and image contrast. Phantom testing demonstrated that image contrast resulting from under exposure, over exposure as well as a contrast plateau can be predicted by use of a prospective image planning algorithm. Phantom data suggest the potential for reducing imaging dose by selecting a high kVp without loss of image contrast. In the clinic, image acquisition parameters can be predicted using the IPS that reduce patient dose without loss of useful image contrast

Appropriate margin for planning target volume for breast radiotherapy during deep inspiration breath-hold by variance component analysis

BACKGROUND: This study aimed to quantify errors by using a cine electronic portal imaging device (cine EPID) during deep inspiration breath-hold (DIBH) for left-sided breast cancer and to estimate the planning target volume (PTV) by variance component analysis. METHODS: This study included 25 consecutive left-sided breast cancer patients treated with whole-breast irradiation (WBI) using DIBH. Breath-holding was performed while monitoring abdominal anterior-posterior (AP) motion using the Real-time Position Management (RPM) system. Cine EPID was used to evaluate the chest wall displacements in patients. Cine EPID images of the patients (309, 609 frames) were analyzed to detect the edges of the chest wall using a Canny filter. The errors that occurred during DIBH included differences between the chest wall position detected by digitally reconstructed radiographs and that of all cine EPID images. The inter-patient, inter-fraction, and intra-fractional standard deviations (SDs) in the DIBH were calculated, and the PTV margin was estimated by variance component analysis. RESULTS: The median patient age was 55 (35-79) years, and the mean irradiation time was 20.4 ± 1.7 s. The abdominal AP motion was 1.36 ± 0.94 (0.14-5.28) mm. The overall mean of the errors was 0.30 mm (95% confidence interval: - 0.05-0.65). The inter-patient, inter-fraction, and intra-fractional SDs in the DIBH were 0.82 mm, 1.19 mm, and 1.63 mm, respectively, and the PTV margin was calculated as 3.59 mm. CONCLUSIONS: Errors during DIBH for breast radiotherapy were monitored using EPID images and appropriate PTV margins were estimated by variance component analysis

Regression learning for 2D/3D image registration

Image registration is a common technique in medical image analysis. The goal of image registration is to discover the underlying geometric transformation of target objects or regions appearing in two images. This dissertation investigates image registration methods for lung Image-Guided Radiation Therapy (IGRT). The goal of lung IGRT is to lay the radiation beam on the ever-changing tumor centroid but avoid organs at risk under the patient's continuous respiratory motion during the therapeutic procedure. To achieve this goal, I developed regression learning methods that compute the patient's 3D deformation between a treatment-time acquired x-ray image and a treatment-planning CT image (2D/3D image registration) in real-time. The real-time computation involves learning x-ray to 3D deformation regressions from a simulated patient-specific training set that captures credible deformation variations obtained from the patient's Respiratory-Correlated CT (RCCT) images. At treatment time, the learned regressions can be applied efficiently to the acquired x-ray image to yield an estimation of the patient's 3D deformation. In this dissertation, three regression learning methods - linear, non-linear, and locally-linear regression learning methods are presented to approach this 2D/3D image registration problem.Doctor of Philosoph