| Summary: | 碩士 === 國立臺灣大學 === 醫學工程學研究所 === 105 === Malignant neoplasms have been the top leading cause of death in Taiwan for decades. Lung cancer, regardless of gender, ranks the first leading cause of cancer deaths, amounting to 28.3% of all cancer deaths. Surgery is the best and effective method in the early stage of the lung cancer. However, only 15% of the diagnosed early-stage patients are suitable for surgery. Most lung cancers, when found, have been in the intermediate to terminal stages, for which radiation therapy usually serves as one of the main therapeutic approaches. During radiation therapy, high intensity radiation not only kills tumor cells but simultaneously causes damage to normal lung tissues resulting in radiation induced lung disease (RILD). RILD is a severe complication of radiotherapy in lung cancer patients, which poses potential threat to life and deteriorates patients’ quality of life. If the correlation between the radiation dose distribution and RILD is known and a prediction model of RILD can be built before radiotherapy, it would be of great help for clinicians to make the therapeutic plan and prevent severe complications.
Due to the variation of pulmonary radiosensitivity among different patients, the tumors and lung tissues of different patients may respond to the radiation therapy differently. The ultimate goal of this study was to assist the medical doctors in understanding the correlation between the RILD changes and dose distribution over time such that the individual difference could be taken into account in the effort of minimizing the tissue damages of RILD during radiation therapy. To achieve this goal, a new longitudinal registration algorithm was proposed in this thesis, which allowed a medical doctor to investigate the longitudinal RILD changes of the same regions before and after radiation therapy.
To establish the corresponding pixels of the pulmonary CT images before and after radiation therapy, our laboratory had developed a non-rigid longitudinal registration algorithm based on the Coherence Point Drift (CPD) algorithm previously using the structures surrounding the lung as a whole to describe the deformation resulted from breathing. While this algorithm was able to alignment two lung CT images to a reasonable extent, it suffered the problem that the lung deformation does not satisfy the basic assumption of the CPD method, i.e., all points moving coherently as a group. In reality, different portions of the lung structures, including lung walls and parenchyma, have different deformation models, i.e., deforming incoherently. As a result, larger misalignment errors were observed for the portions of the lung wall around the heart, which suggested that it is insufficient to model lung deformation using the CPD method.
To improve the registration accuracy of lung boundaries and account for the incoherent motion nature of different portions of the lung structures, a Component-Structure CPD (CSCPD) registration algorithm was proposed in this thesis. The basic idea of the CSCPD algorithm was that the lung was decomposed into several component structures, each of which was then registered by the CPD method. Compared with the CPD algorithm modeling the whole deformation as a group, the CSCPD algorithm could better describe the individualized coherent deformation model of each component structure.
To present the ideas and the performances of the CSCPD algorithm, two versions of the CSCPD algorithms were described in this thesis according to the anatomical compositions. The first one, denoted as CSCPD_W (CSCPD_LungWall), focused on the registration of the lung wall. The CSCPD_W algorithm first performed a rigid registration using the spine as the reference structure. Following that, multiple non-rigid registrations were carried out for such structures as ribs, spine, airway, innerr-lung wall, lower-lung wall, and sternum, each of which served as a component structure registered by a CPD method. The second one, denoted as CSCPD_WV(CSCPD_LungWall and Vessel), was an augmented version of the CSCPD_W by integrating a new idea, namely, regional vascular point matching, to account for the spatially-variant deformation within the parenchyma. The uniqueness of the regional vascular point matching method lied in the capability of generating a great number of corresponding point pairs with only tens of pre-selected corresponding point pairs. Finally, the thin-plate spline (TPS) method was used to construct the non-rigid registration model for the whole lung.
Independent two-sample t test was used to compare the mean registration errors of lung boundaries achieved by the CPD and CSCPD_W non-rigid registration algorithms for 18 patients with mild-to-severe RILD. The results showed that the CSCPD_W algorithm was significantly better than the CPD method (p<0.05), which suggested that the proposed CSCPD algorithm was effective in resolving the incoherent motion problem encountered by the CPD method.
To assess the registration performances of the CPD, CSCPD_W and CSCPD_WV algorithms on the parenchyma, One-way ANOVA with repeated measures was employed to compare the target registration error (TRE) of 20 manually selected blood vessels landmarks on the pre- and post-treatment CT images of 6 patients. While the null hypothesis of the ANOVA was rejected, the pair-wise post-hoc comparison tests showed that the mean TRE of CSCPD_WV was significantly smaller than those of CSCPD_W and CPD, respectively (p<0.01). It implied that the idea of regional vascular point matching can improve the registration accuracy within the parenchyma.
In summary, the thesis proposed a new non-rigid registration algorithm, called CSCPD, for the CT images of the same patient before and after radiation therapy. The key idea of the CSCPD algorithm was to decompose the lung, including the lung wall and parenchyma, and the peripheral structures into component structures and register each of them by the CPD method. The corresponding point pairs established by all component structures were then used to construct the non-rigid registration model using TPS. The analysis results confirmed that the CSCPD algorithm significantly enhance the registration accuracy for both of the lung wall and parenchyma.
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