J H Killoran1, E H Baldini, C J Beard, L Chin. 1. Longwood Radiation Oncology Center, Department of Radiation Oncology, Harvard Medical School, Boston, MA, USA. jkilloran@lroc.harvard.edu
Abstract
INTRODUCTION: Virtual simulation (VS) of radiotherapy uses CT data. Digitally reconstructed radiographs (DRRs) are a critical element of this process, and the quality of these images is frequently suboptimal. We present techniques to improve DRR quality for clinical purposes. The results of two approaches to DRR optimization are presented. METHODS AND MATERIALS: One approach to DRR optimization is to use traditional radiographs as a guide and to adjust the algorithm parameters based on image and objective contrast to produce images that more closely resemble traditional radiographs (Method 1). Another approach is to focus on the visibility of specific anatomic structures. Using this method, two DRR images are optimized manually by interactively adjusting reconstruction parameters, then they are combined into a single composite image (Method 2). DRRs for the chest region, generated using both methods, were evaluated by clinical staff based on usability for treatment verification and field definition. RESULTS: Using Method 1, the resulting DRRs more closely resembled traditional radiographs. This technique allows DRR quality to be improved with little user interaction. These DRRs are generally adequate for clinical use, but not optimal for sites such as the chest. Images generated using Method 2 were considered clinically superior in terms of visibility of specific anatomic structures. These images also compare well with traditional radiographs, although they show an increased contrast level between bone and lower density structures. CONCLUSION: Both Methods 1 and 2 can be used to improve DRR quality for clinical purposes. For the chest region, the additional effort required by Method 2 to achieve a more detailed image appears justified.
INTRODUCTION: Virtual simulation (VS) of radiotherapy uses CT data. Digitally reconstructed radiographs (DRRs) are a critical element of this process, and the quality of these images is frequently suboptimal. We present techniques to improve DRR quality for clinical purposes. The results of two approaches to DRR optimization are presented. METHODS AND MATERIALS: One approach to DRR optimization is to use traditional radiographs as a guide and to adjust the algorithm parameters based on image and objective contrast to produce images that more closely resemble traditional radiographs (Method 1). Another approach is to focus on the visibility of specific anatomic structures. Using this method, two DRR images are optimized manually by interactively adjusting reconstruction parameters, then they are combined into a single composite image (Method 2). DRRs for the chest region, generated using both methods, were evaluated by clinical staff based on usability for treatment verification and field definition. RESULTS: Using Method 1, the resulting DRRs more closely resembled traditional radiographs. This technique allows DRR quality to be improved with little user interaction. These DRRs are generally adequate for clinical use, but not optimal for sites such as the chest. Images generated using Method 2 were considered clinically superior in terms of visibility of specific anatomic structures. These images also compare well with traditional radiographs, although they show an increased contrast level between bone and lower density structures. CONCLUSION: Both Methods 1 and 2 can be used to improve DRR quality for clinical purposes. For the chest region, the additional effort required by Method 2 to achieve a more detailed image appears justified.