Towards automated planning for unsealed source therapy

Eduard Schreibmann,Tim Fox

doi:10.1120/jacmp.v13i4.3789

Abstract

The purpose of this study was to develop and validate a technique for unsealed source radiotherapy planning that combines the segmentation and registration tasks of single‐photon emission tomography (SPECT) and computed tomography (CT) datasets. The segmentation task is automated by an atlas registration approach that takes advantage of a hybrid scheme using a diffeomorphic demons algorithm to warp a standard template to the patient's CT. To overcome the lack of common anatomical features between the CT and SPECT datasets, registration is achieved through a narrow band approach that matches liver contours in the CT with the gradients of the SPECT dataset. Deposited dose is then computed from the SPECT dataset using a convolution operation with tracer‐specific deposition kernels. Automatic segmentation showed good agreement with manual contouring, measured using the dice similarity coefficient and ranging from 0.72 to 0.87 for the liver, 0.47 to 0.93 for the kidneys, and 0.74 to 0.83 for the spinal cord. The narrow band registration achieved variations of less 0.5 mm translation and 1° rotation, as measured with convergence analysis. With the proposed combined segmentation–registration technique, the uncertainty of soft‐tissue target localization is greatly reduced, ensuring accurate therapy planning.PACS number: 87.55.de, 87.55.kd

Highlights

The trend of more personalized therapy is driving clinical use of molecular imaging for assessment of therapeutic targets and identification of resistance factors to match therapy to tumor biology
Integration of functional single-photon emission computed tomography (SPECT) imaging data into radiation dose calculations has drawn the interest of many researchers because of its compelling advantages in quantifying absorbed dose delivered to tumors and normal tissue for targeted radiotherapy.[1,2,3,4] One of the most common applications is to treat hepatic malignancies by radioembolization with microspheres containing yttrium-90 (90Y) delivered via the hepatic artery.[5,6,7,8] In this approach, patient-specific data from computed tomography (CT) or magnetic resonance imaging (MRI) provides an anatomical model with resolutions on the order of 1 mm
To automate the registration process based on specifics of the CT and SPECT datasets, we propose a registration method based on the narrow band approach.[31,32] The key piece is usage of a narrow band to connect a liver segmentation in the CT dataset to the corresponding gradients of voxel activity in the SPECT images

Summary

Introduction

The trend of more personalized therapy is driving clinical use of molecular imaging for assessment of therapeutic targets and identification of resistance factors to match therapy to tumor biology. Integration of functional single-photon emission computed tomography (SPECT) imaging data into radiation dose calculations has drawn the interest of many researchers because of its compelling advantages in quantifying absorbed dose delivered to tumors and normal tissue for targeted radiotherapy.[1,2,3,4] One of the most common applications is to treat hepatic malignancies by radioembolization with microspheres containing yttrium-90 (90Y) delivered via the hepatic artery.[5,6,7,8] In this approach, patient-specific data from computed tomography (CT) or magnetic resonance imaging (MRI) provides an anatomical model with resolutions on the order of 1 mm. Monte Carlo and dose convolution methods for calculating the absorbed dose have improved;(15) image registration and anatomical segmentation methods require significant user interaction,(2,3) which limit the clinical usefulness of the system

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