|dc.description.abstract||Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) of the breast is a medical imaging tool used to detect and diagnose breast disease. A DCE-MR image is a series of three-dimensional (3D) breast MRI scans. It is acquired to form a 4D image (3D spatial + time), before and after the injection of paramagnetic contrast agents. DCE-MRI allows the analysis of the intensity variation of magnetic resonance (MR) signals, before and after the injection of contrast agents over time. The interpretation of 4D DCE-MRI images can be time consuming due to the amount of information involved. Motion artifacts in between the image scans further complicate the diagnosis.
A DCE-MR image includes a large amount of data and it is challenging to interpret even for an experienced radiologist. Therefore, a computer-aided diagnosis (CAD) system is desirable in assisting the diagnosis of abnormal findings in the DCE-MR image. We propose a fully automated CAD system that is comprised of five novel components: a new image registration method to recover motions in between MR image acquisitions, a novel
lesion detection method to identify all suspicious regions, a new lesion segmentation method to draw lesion contours and a novel lesion feature characterization method. We then classify the automatically detected lesions using our proposed features. The following lists the challenges found in most CAD systems and the contributions in our CAD system of breast DCE-MRI.
1. Image registration. One challenge in the interpretation of DCEMRI is motion artifacts which cause the pattern of tissue enhancement to be unreliable. Image registration is used to recover rigid and nonrigid motions between the 3D image sequences in a 4D breast DCE-MRI. Most existing b-spline based registration methods require lesion segmentation in breast DCE-MRI to preserve the lesion volume before performing the registration. An automatic regularization coefficients generation method is proposed in the b-spline based registration of the breast DCE-MRI, where the tumor regions are transformed in a rigid fashion. Our method does not perform lesion segmentation
but computes a map to reflect the tissue rigidity. In the evaluation of our proposed coefficients, the registration methods using our coefficients for rigidity terms are compared against manually assigned coefficients of the rigidity terms and smoothness terms. The evaluation is performed on 30 synthetic and 40 clinical pairs of pre- and
post-contrast MRI scans. The results show that the tumor volumes can be well-preserved by using a rigidity term (2:25 +- 4:48% of volume changes) compared to a smoothness term (22:47% +- 20:1%). In our dataset, the volume preservation performance by using our automatically generated coefficients is comparable to the manually assigned rigidity coefficients (2:29% 13:25%), and show no significant difference in volume changes (p > 0:05).
2. Lesion detection. After the motions have been corrected by our registration method, we locate the region of interest (ROI) using our lesion detection method. The aim is to highlight the suspicious ROIs to reduce the ROI searching time and the possibility of overlooking small regions by radiologists. A low signal-to-noise ratio is a general
challenge in lesion detection of MRI. In addition, the value ranges of a feature of normal tissue in a patient can overlap with that of malignant tissue in another patient, e.g. tissue intensity values, enhancement et al.. Most existing lesion detection methods face the
problem of high false positive rate due to blood vessels or motion artifacts. In our method, we locate suspicious lesions by applying a threshold on essential features. The features are normalized to reduce the variation between patients. We then exclude blood vessel
or motion artifacts from the initial results by applying filters that can differentiate them from other tissues. In the evaluation of the system on 21 patients with 50 lesions, all were successfully detected with 5.04 false positive regions per breast.
3. Lesion segmentation. One of the main challenges of existing lesion segmentation methods in breast DCE-MRI is that they require the size of the ROI that encloses a lesion to be small in order to successfully segment the lesion. We propose a lesion segmentation method based on naive Bayes and Markov random field. Our method also
requires a ROI generated by a user, but the method is not sensitive to the size of the ROI. In our method, the ROI selected in a DCE-MR image is modeled as a connected graph with local Markov properties where each voxel of the image is regarded as a node. Three
edge potentials of the graph are proposed to encourage the smoothness of the segmented regions. In the validation on 72 lesions, our method performs better than a baseline fuzzy-c-means method and another closely related method in segmenting lesions in breast MRI by showing a higher overlap with the ground truth.
4. Feature analysis and lesion classification. The challenge of feature analysis in breast DCE-MRI is that different types of lesions can share similar features. In our study, we extract various morphological, textural and kinetic features of the lesions and apply three
classifiers to label them. In the morphological feature analysis, we propose minimum volume enclosing ellipsoid (MVEE) based features to measure the similarity of between a lesion and its MVEE. In statistical testing on 72 lesion, the MVEE-based features are significant in differentiating malignant from benign lesions.
5. CAD applications. The proposed CAD system is versatile. We show two scenarios in which a radiologist makes use of the system. In the first scenario, a user selects a rectangular region of interest (ROI) as input and the CAD automatically localizes and classifies the lesion in the ROI as benign or malignant. In another scenario, the CAD system acts as a “second reader” which fully and automatically identifies all malignant regions. At the time of writing, this is the first automated CAD system that is capable carrying out all these processes without any human interaction.
In this thesis, we evaluated the proposed image registration, lesion detection, lesion segmentation, feature extraction and lesion classification using a relatively small database which makes conclusions on generalizability difficult. In the future work, the system requires clinical testing on a large dataset in order to advance this breast MRI CAD to reduce the image interpretation time, eliminate unnecessary biopsy and improve the cancer identification sensitivity for radiologists.||en_US