<p>One of the challenges facing clinical practice today is intra-operative margin detection in breast conserving surgeries (BCS) or lumpectomy procedures. When a surgeon removes a breast tumor from a patient during a BCS procedure, the surgically excised tissue specimen is examined to see whether it contains a margin of healthy tissue around the tumor. A healthy margin of tissue around the tumor would indicate that the tumor in its entirety has been removed. On the other hand, if cancerous tissue is at the surface of the specimen, that would indicate that the tumor may have been transected during the procedure, leaving some residual cancerous tissue inside the patient. The most effective intra-operative real-time margin detection techniques currently used in clinical practice are frozen section analysis (FSA) and touch-prep cytology. These methods have been shown to possess inconsistent accuracy, which result in 20% to 30% of BCS patients being called back for a repeat BCS procedure to remove the residual tumor tissue. In addition these techniques have been shown to be time-consuming--requiring the operating room team to have to wait at least 20 minutes for the results. Therefore, there is a need for accurate and faster technology for intra-operative margin detection. </p><p>In this dissertation, we describe an x-ray coherent scatter imaging technique for intra-operative margin detection with greater accuracy and speed than currently available techniques. The method is based on cross-sectional imaging of the differential coherent scatter cross section in the sample. We first develop and validate a Monte Carlo simulation of coherent scattering. Then we use that simulation to design and test coherent scatter computed tomography (CSCT) and coded aperture coherent scatter spectral imaging (CACSSI) for cancerous voxel detection and for intra-operative margin detection using (virtual) clinical trials. Finally, we experimentally implement a CACSSI system and determine its accuracy in cancer detection using tissue histology. </p><p>We find that CSCT and CACSSI are able to accurately detect cancerous voxels inside of breast tissue specimens and accurately perform intra-operative margin detection. Specifically, for the task of individual cancerous voxel detection, we show that CSCT and CACSSI have AUC values of 0.97 and 0.94, respectively. Whereas for the task of intra-operative margin detection, the results of our virtual clinical trials show that CSCT and CACSSI have AUC values of 0.975 and 0.741, respectively. The gap in spatial resolution between CSCT and CACSSI affects the results of intra-operative margin detection much more than it does the task of individual cancerous voxel detection. Finally, we also show that CSCT would require on the order of 30 minutes to create a 3D image of a breast cancer specimen, whereas CACSSI would require on the order of 3 minutes. </p><p>These results of this work show that coherent scatter imaging has the potential to provide more accurate intra-operative margin detection than currently used clinical techniques. In addition, the speed (and therefore low scan duration: 3 min) of CACSSI, along with its ability to automatically classify cancerous tissue for margin detection means that coherent scatter imaging would be much more cost-effective than the clinical techniques that require up to 20 minutes and a trained pathologist. With the cancerous voxel detection accuracy of a 0.94 AUC and scan time of on the order of 3 minutes demonstrated for coherent scatter imaging in this work, coherent scatter imaging has the potential to reduce healthcare costs for BCS procedures and rates of repeat BCS surgeries. The accuracy for CACSSI can be considerably improved to match CSCT accuracy by improving its spatial resolution through a number of techniques: incorporating into the CACSSI reconstruction algorithm the ability to differentiate noise from high frequency signal so that we can image with higher frequency coded aperture masks; implementing a 2D coded aperture mask with a 2D detector; or acquiring additional angles of projection data.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/11393 |
| Date | January 2015 |
| Creators | Lakshmanan, Manu Nachiappan |
| Contributors | Kapadia, Anuj J, Farsiu, Sina |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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