Magnetic resonance imaging (MRI) is one of medical images used by doctors for diagnosing diseases. MRI shows higher quality in displaying soft tissues and tumors. Pharmacokinetic dynamic gadolinium-enhanced MR imaging and functional MR imaging (fMRI) were used in this dissertation. Dynamic MR images are obtained using fast spin-echo sequences at consecutive time after the injection of gadolinium-diethylene-triamine penta-acetic (Gd-DTPA) acid. A pharmacokinetic model analyzes time-signal intensity curves of suspected lesions. Functional MR imaging produces images of activated brain regions by detecting the indirect effects of neuronal activity on local blood volume, flow, and oxygen saturation. Thus it is a promising tool for further understanding the relationships between brain structure, function, and pathology. Because of patients' movement during imaging, serially acquired MR images do not correspond in the same pixel position. Therefore, matching corresponding points from MR images is one of fundamental tasks in this dissertation. Least-squares estimation is a standard method for parameter estimation. However, outliers (due to non-Gaussian noise, lesion evolution, motion-related artifacts, etc.) may exist and thus may cause the motion parameter estimation result to deteriorate. In this dissertation, we describe two robust estimation algorithms for the registration of serially acquired MR images. The first estimation algorithm is based on the Newton method and uses the Tukey's biweight objective function. The second estimation algorithm is based on the Levenberg-Marquardt technique and uses a skipped mean objective function. The robust M-estimators can suppress the effects of the outliers by scaling down their error magnitudes or completely rejecting outliers using a weighting function. Experimental results show the accuracy of the proposed robust estimation algorithms is within subpixel.
MR imaging has been used to evaluate nasal papilloma. However, postoperative MR imaging of nasal papilloma becomes more complicated because repair with granulation and fibrosis occurs after surgery. Therefore, it is possible to misclassify recurrences as postoperative changes or to misclassify postoperative changes as recurrences. Recently, dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis has been successfully used to identify the post-treatment recurrence or postoperative changes in rectal and cervical carcinoma. Nasopharyngeal carcinoma (NPC) comprising malignant tumors is a disease more common in Asia than in other parts of the world. Hence, in this dissertation, we evaluate the feasibility of dynamic gadolinium-enhanced MR imaging with pharmacokinetic analysis in detecting NPC and distinguishing recurrent nasal papilloma from postoperative changes (fibrosis or granulation tissue).
In this dissertation, a new approach to differentiate recurrent nasal papilloma from postoperative changes using pharmacokinetic dynamic gadolinium-enhanced MR imaging and robust motion correction is presented. First, a robust estimation technique is incorporated into nonlinear minimization method to robustly register dynamic gadolinium-enhanced MR images. Next, user roughly selects the region of interest (ROI) and an active contour technique is used to extract a more precise ROI. Then, the relative signal increase (RSI) is calculated. We use a three-parameter mathematical model for pharmacokinetic analysis. The pharmacokinetic parameters A (enhancement amplitude) and Tc (tissue distribution time) are calculated by a nonlinear least-squares fitting technique. The calculated A and Tc are used to characterize tissue. Pharmacokinetic analysis shows that recurrent nasal papilloma has faster tissue distribution time (Tc, 41 versus 88 seconds) and higher enhancement amplitude (A, 2.4 versus 1.2 arbitrary units) than do postoperative changes. A cut-off of 65 seconds for tissue distribution time and 1.6 units for enhancement amplitude yields an accuracy of 100% for differentiating recurrent nasal papilloma from postoperative changes.
Though the above methods obtained good results, finding the region of interest (ROI) was done in a semi-automatic manner. For diagnosing NPC and improve the drawback, a system that automatically detects and labels NPC with dynamic gadolinium-enhanced MR imaging is presented. This system is a multistage process, involving motion correction, gadolinium-enhanced MR data quantitative evaluation, rough segmentation, and rough segmentation refinement. Three approaches, a relative signal increase method, a slope method and a relative signal change method, are proposed for the quantitative evaluation of gadolinium-enhanced MR data. After the quantitative evaluation, a rough NPC outline is determined. Morphological operations are applied to refine the rough segmentation into a final mask. The NPC detection results obtained using the proposed methods had a rating of 85% in match percent compared with these lesions identified by an experienced radiologist. However, the proposed methods can identify the NPC regions quickly and effectively.
In this dissertation, the proposed methods provide significant improvement in correcting the motion-related artifacts and can enhance the detection of real brain activation and provide a fast, valuable diagnostic tool for detecting NPC and differentiating recurrent nasal papilloma from postoperative changes.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0518101-162729 |
| Date | 18 May 2001 |
| Creators | Hsu, Cheng-Chung |
| Contributors | Yung-Nien Sun, Chungnan Lee, Wen-Shyong Hsieh, Chin-Chen Chang, Pau-Choo Chung, Ming-Ting Wu, Ping-Hong Lai, Wen-Chen Huang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0518101-162729 |
| Rights | withheld, Copyright information available at source archive |
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