Abstract

BackgroundBiologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. A major challenge arises from having only 2D treatment planning data for patients with long-term follow-up. In this study, we evaluate the accuracy of an advanced deformable image registration (DIR) and navigator channels (NC) adaptation technique to reconstruct 3D heart volumes from 2D radiotherapy planning images for Hodgkin's Lymphoma (HL) patients.MethodsPlanning CT images were obtained for 50 HL patients who underwent mediastinal radiotherapy. Twelve image sets (6 male, 6 female) were used to construct a male and a female population heart model, which was registered to 23 HL "Reference" patients' CT images using a DIR algorithm, MORFEUS. This generated a series of population-to-Reference patient specific 3D deformation maps. The technique was independently tested on 15 additional "Test" patients by reconstructing their 3D heart volumes using 2D digitally reconstructed radiographs (DRR). The technique involved: 1) identifying a matching Reference patient for each Test patient using thorax measurements, 2) placement of six NCs on matching Reference and Test patients' DRRs to capture differences in significant heart curvatures, 3) adapting the population-to-Reference patient-specific deformation maps to generate population-to-Test patient-specific deformation maps using linear and bilinear interpolation methods, 4) applying population-to-Test patient specific deformation to the population model to reconstruct Test-patient specific 3D heart models. The percentage volume overlap between the NC-adapted reconstruction and actual Test patient's true heart volume was calculated using the Dice coefficient.ResultsThe average Dice coefficient expressed as a percentage between the NC-adapted and actual Test model was 89.4 ± 2.8%. The modified NC adaptation technique made significant improvements to the population deformation heart models (p = 0.01). As standard evaluation, the residual Dice error after adaptation was comparable to the volumetric differences observed in free-breathing heart volumes (p = 0.62).ConclusionsThe reconstruction technique described generates accurate 3D heart models from limited 2D planning data. This development could potentially be used to retrospectively calculate delivered dose to the heart for historically treated patients and thereby provide a better understanding of late radiation-related cardiac effects.

Highlights

  • Biologically-based models that utilize Three dimensional (3D) radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients

  • Radiation-induced cardiac toxicity is a significant cause of morbidity, following treatment of Hodgkin’s Lymphoma (HL) [1]

  • A refinement of existing radiation therapy (RT)-related 3D doserisk model of cardiac toxicity could potentially allow a better understanding of cardiac radiation tolerances, risk estimation of late cardiac effects, and optimization of RT treatment to minimize cardiac toxicity

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Summary

Introduction

Biologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. Radiation-induced cardiac toxicity is a significant cause of morbidity, following treatment of Hodgkin’s Lymphoma (HL) [1]. A refinement of existing RT-related 3D doserisk model of cardiac toxicity could potentially allow a better understanding of cardiac radiation tolerances, risk estimation of late cardiac effects, and optimization of RT treatment to minimize cardiac toxicity. Due to the delayed onset of cardiac effects, the major barrier to the application of biophysical models remains the absence of 3D dosimetry on historically treated patients, for whom late toxicity outcomes are available, but were treated with a 2D treatment planning system. In order to obtain corresponding dose-volume and reliable late toxicity data, a 3D model reconstruction system is needed to extract 3D volume information from historical 2D planning datasets

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