Prostate brachytherapy involves permanent implantation of radioactive sources into the prostate gland. Since
fluoroscopy and transrectal ultrasound (TRUS) imaging modalities currently complement each other by providing good
visualization of seeds and soft tissue, respectively, the registration of these two imaging modalities could lead to the
intraoperative dosimetry analysis of brachytherapy procedures, thus improving patient outcome and reducing costs.
Although it is desirable to register TRUS and fluoroscopy images by using the implanted seeds as fiducial markers, an
operator, based on our experience, can locate only a small fraction of implanted seeds in axial TRUS images. Therefore,
to perform TRUS-fluoroscopy registration in a clinical setting, there is a need for (1) a new method that can reliably
perform registration at low seed detection rates and (2) a new imaging technique to enhance the seed visibility. We
previously developed iterative optimal assignment (IOA), which can perform registration at seed detection rates below
20%, to address the former. In this paper, we present a new TRUS acquisition method where we acquire images of the
prostate by rotating the longitudinal transducer of a biplanar probe in the clockwise/counter-clockwise direction. We
acquired post-implant fluoroscopy and TRUS images from 35 patients who underwent a seed implant procedure. The
results show that the combined use of IOA and rotational images makes TRUS-fluoroscopy registration possible and
practical, thus our goal of intraoperative dosimetry can be realized.
Proc. SPIE. 5747, Medical Imaging 2005: Image Processing
KEYWORDS: Image segmentation, Prostate, Ultrasonography, Signal to noise ratio, Data modeling, Binary data, Statistical modeling, Image processing, Image processing algorithms and systems, Medical imaging
Prostate segmentation in ultrasound images is a clinically important and technically challenging task. Despite several research attempts, few effective methods are available. One problem is the limited algorithmic robustness to common artifacts in clinical data sets. To improve the robustness, we have developed a hybrid level set method, which incorporates shape constraints into a region-based curve evolution process. The online segmentation method alternates between two steps, namely, shape model estimation (ME) and curve evolution (CE). The prior shape information is encoded in an implicit parametric model derived offline from manually outlined training data. Utilizing this prior shape information, the ME step tries to compute the maximum a posteriori estimate of the model parameters. The estimated shape is then used to guide the CE step, which in turn provides a new model initialization for the ME step. The process stops automatically when the curve locks onto the specific prostate shape. The ME and the CE steps complement each other to capture both global and local shape details. With shape guidance, this algorithm is less sensitive to initial contour placement and more robust even in the presence of large boundary gaps and strong clutter. Promising results are demonstrated on both synthetic and real prostate ultrasound images.
Intraoperative quality assessment during prostate brachytherapy could improve the clinical outcome by ensuring the delivery of a prescribed tumoricidal radiation dose to the entire prostate gland. Accurate prostate boundary segmentation is an essential first step towards this. Classical segmentation techniques fail to generate a reliable edge map in ultrasound images. Modeling the 3D prostate shape in a deformable model framework could lead to more reliable prostate segmentation since missing information in some parts of the images due to the indistinct prostatic margins could be reconstructed using information in adjacent slices, and the resulting boundary elements could be integrated into a coherent mathematical description. We first experimented with deformable superquadrics to generate 3D surfaces that match the manually-outlined prostate contours. The superquadrics were found to capture the global shape, but had limited capability of modeling local shape variations. Then, closed and tubular surfaces were generated using Fourier descriptors to fit the prostate data. The modeling errors were compared with the disagreement between manual outlines by three experts. The preliminary results from 12 patient data sets show that the Fourier descriptors are capable of generating tubular surfaces that closely match the manual outlines. The minimum number of parameters required to reconstruct a tubular prostate surface with a tolerable error margin is 52.