Volumetric visualization of anatomy for treatment planning

Delineation of volumes of interest for three-dimensional (3D) treatment planning is usually performed by contouring on two-dimensional sections. We explore the usage of segmentation-free volumetric rendering of the three-dimensional image data set for tumor and normal tissue visualization.

Standard treatment planning computed tomography (CT) studies, with typically 5 to 10 mm slice thickness, and spiral CT studies with 3 mm slice thickness were used. The data were visualized using locally developed volume-rendering software. Similar to the method of Drebin et al., CT voxels are automatically assigned an opacity and other visual properties (e.g., color) based on a probabilistic classification into tissue types. Using volumetric compositing, a projection into the opacity-weighted volume is produced. Depth cueing, perspective, and gradient-based shading are incorporated to achieve realistic images. Unlike surface-rendered displays, no hand segmentation is required to produce detailed renditions of skin, muscle, or bony anatomy. By suitable manipulation of the opacity map, tissue classes can be made transparent, revealing muscle, vessels, or bone, for example. Manually supervised tissue masking allows irrelevant tissues overlying tumors or other structures of interest to be removed.

Very high-quality renditions are produced in from 5 s to 1 min on midrange computer workstations. In the pelvis, an anteroposterior (AP) volume rendered view from a typical planning CT scan clearly shows the skin and bony anatomy. A muscle opacity map permits clear visualization of the superficial thigh muscles, femoral veins, and arteries. Lymph nodes are seen in the femoral triangle. When overlying muscle and bone are cut away, the prostate, seminal vessels, bladder, and rectum are seen in 3D perspective. Similar results are obtained for thorax and for head and neck scans.

Volumetric visualization of anatomy is useful in treatment planning, because 3D views can be generated without the need for segmentation. When relationships among anatomical structures, rather than geometric models of them, are important, volume rendering presents advantages. The presented algorithm is readily adaptable to distributed parallel implementation on a network of heterogeneous workstations.