MATERIALS AND METHODS: The HRCT and MR imaging in 46 cochlear implant patients in our department were reviewed.
RESULTS: Majority of our patients [34 patients (73.9%)] showed normal HRCT of the temporal bone; 5 (10.9%) patients had labyrinthitis ossificans, 2 (4.3%) had Mondini's abnormality and 2 (4.3%) had middle ear effusion. One patient each had high jugular bulb, hypoplasia of the internal auditory canal and single cochlear cavity, respectively.
CONCLUSION: The above findings contribute significantly to our surgical decisions regarding candidacy for surgery, side selection and surgical technique in cochlear implantation.
PATIENTS AND METHODS: The institutional review board approved this prospective study. The brain MRI protocol, including sagittal T1-weighted, axial T2-weighted, coronal fluid-attenuated inversion recovery, and axial T1-weighted with contrast enhancement (T1WCE) sequences, was assessed in 26 patients divided into two groups: Medulloblastoma (n=22) and ependymoma (n=4). The quantified region of interest (ROI) values of tumors and their ratios to parenchyma were compared between the two groups. Multivariate logistic regression analysis was utilized to find significant factors influencing the differential diagnosis between the two groups. A generalized estimating equation (GEE) was used to create the predictive model for the discrimination of medulloblastoma from ependymoma.
RESULTS: Multivariate logistic regression analysis showed that the T2- and T1WCE-ROI values of tumors and the ratios of T1WCE-ROI values to parenchyma were the most significant factors influencing the diagnosis between these two groups. GEE produced the model: y=exn/(1+exn) with predictor xn=-8.773+0.012x1 - 0.032x2 - 13.228x3, where x1 was the T2-weighted signal intensity (SI) of tumor, x2 the T1WCE SI of tumor, and x3 the T1WCE SI ratio of tumor to parenchyma. The sensitivity, specificity, and area under the curve of the GEE model were 77.3%, 100%, and 92%, respectively.
CONCLUSION: The GEE predictive model can discriminate between medulloblastoma and ependymoma clinically. Further research should be performed to validate these findings.
OBJECTIVES: The aims of this study is to investigate the capability of random walks as knee cartilage segmentation method.
METHODS: Experts would scribble on knee cartilage image to initialize random walks segmentation. Then, reproducibility of the method is assessed against manual segmentation by using Dice Similarity Index. The evaluation consists of normal cartilage and diseased cartilage sections which is divided into whole and single cartilage categories.
RESULTS: A total of 15 normal images and 10 osteoarthritic images were included. The results showed that random walks method has demonstrated high reproducibility in both normal cartilage (observer 1: 0.83±0.028 and observer 2: 0.82±0.026) and osteoarthritic cartilage (observer 1: 0.80±0.069 and observer 2: 0.83±0.029). Besides, results from both experts were found to be consistent with each other, suggesting the inter-observer variation is insignificant (Normal: P=0.21; Diseased: P=0.15).
CONCLUSION: The proposed segmentation model has overcame technical problems reported by existing semi-automated techniques and demonstrated highly reproducible and consistent results against manual segmentation method.
Material and Methods: In this study, we have introduced a new technique to reduce the motion artifacts, based on data binning and low rank plus sparse (L+S) reconstruction method for DCE MRI. For Data binning, radial k-space data is acquired continuously using the golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing- (CS-) based L+S matrix decomposition model is then used to reconstruct motion sorted DCE MR images. Undersampled free breathing 3D liver and abdominal DCE MR data sets are used to validate the proposed technique.
Results: The performance of the technique is compared with conventional L+S decomposition qualitatively along with the image sharpness and structural similarity index. Recovered images are visually sharper and have better similarity with reference images.
Conclusion: L+S decomposition provides improved MR images with data binning as preprocessing step in free breathing scenario. Data binning resolves the respiratory motion by dividing different respiratory positions in multiple bins. It also differentiates the respiratory motion and contrast agent (CA) variations. MR images recovered for each bin are better as compared to the method without data binning.