Recent development in optical 3D surface imaging technologies provide better ways to digitalize the 3D surface and its motion in real-time. The non-invasive 3D surface imaging approach has great potential for many medical imaging applications, such as motion monitoring of radiotherapy, pre/post evaluation of plastic surgery and dermatology, to name a few. Various commercial 3D surface imaging systems have appeared on the market with different dimension, speed and accuracy. For clinical applications, the accuracy, reproducibility and robustness across the widely heterogeneous skin color, tone, texture, shape properties, and ambient lighting is very crucial. Till now, a systematic approach for evaluating the performance of different 3D surface imaging systems still yet exist. In this paper, we present a systematic performance assessment approach to 3D surface imaging system assessment for medical applications. We use this assessment approach to exam a new real-time surface imaging system we developed, dubbed <strong>"Neo3D Camera"</strong>, for image-guided radiotherapy (IGRT). The assessments include accuracy, field of view, coverage, repeatability, speed and sensitivity to environment, texture and color.
Dynamic volumetric medical imaging (4DMI) has reduced motion artifacts, increased early diagnosis of small mobile tumors, and improved target definition for treatment planning. High speed cameras for video, X-ray, or other forms of sequential imaging allow a live tracking of external or internal movement useful for real-time image-guided radiation therapy (IGRT). However, none of 4DMI can track real-time organ motion and no camera has correlated with 4DMI to show volumetric changes. With a brief review of various IGRT techniques, we propose a fast 3D camera for live-video stereovision, an automatic surface-motion identifier to classify body or respiratory motion, a mechanical model for synchronizing the external surface movement with the internal target displacement by combination use of the real-time stereovision and pre-treatment 4DMI, and dynamic multi-leaf collimation for adaptive aiming the moving target. Our preliminary results demonstrate that the technique is feasible and efficient in IGRT of mobile targets. A clinical trial has been initiated for validation of its spatial and temporal accuracies and dosimetric impact for intensity-modulated RT (IMRT), volumetric-modulated arc therapy (VMAT), and stereotactic body radiotherapy (SBRT) of any mobile tumors. The technique can be extended for surface-guided stereotactic needle insertion in biopsy of small lung nodules.
Rapid optical three-dimensional (O3D) imaging systems provide accurate digitized 3D surface data in real-time, with no patient contact nor radiation. The accurate 3D surface images offer crucial information in image-guided radiation therapy (IGRT) treatments for accurate patient repositioning and respiration management. However, applications of O3D imaging techniques to image-guided radiotherapy have been clinically challenged by body deformation, pathological and anatomical variations among individual patients, extremely high dimensionality of the 3D surface data, and irregular respiration motion. In existing clinical radiation therapy (RT) procedures target displacements are caused by (1) inter-fractional anatomy changes due to weight, swell, food/water intake; (2) intra-fractional variations from anatomy changes within any treatment session due to voluntary/involuntary physiologic processes (e.g. respiration, muscle relaxation); (3) patient setup misalignment in daily reposition due to user errors; and (4) changes of marker or positioning device, etc. Presently, viable solution is lacking for in-vivo tracking of target motion and anatomy changes during the beam-on time without exposing patient with additional ionized radiation or high magnet field. Current O3D-guided radiotherapy systems relay on selected points or areas in the 3D surface to track surface motion. The configuration of the marks or areas may change with time that makes it inconsistent in quantifying and interpreting the respiration patterns. To meet the challenge of performing real-time respiration tracking using O3D imaging technology in IGRT, we propose a new approach to automatic respiration motion analysis based on linear dimensionality reduction technique based on PCA (principle component analysis). Optical 3D image sequence is decomposed with principle component analysis into a limited number of independent (orthogonal) motion patterns (a low dimension eigen-space span by eigen-vectors). New images can be accurately represented as weighted summation of those eigen-vectors, which can be easily discriminated with a trained classifier. We developed algorithms, software and integrated with an O3D imaging system to perform the respiration tracking automatically. The resulting respiration tracking system requires no human intervene during it tracking operation. Experimental results show that our approach to respiration tracking is more accurate and robust than the methods using manual selected markers, even in the presence of incomplete imaging data.