Restoration of intensity uniformity of bi-contrast MRI data with bayesian co-occurrence coring

The reconstruction in MRI assumes a uniform radiofrequency field. However, this is violated, which leads to anatomically inconsequential intensity non-uniformities. An anatomic region can be imaged with multiple contrasts that result in different non-uniformities. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled. Their non-stationary deconvolution gives Bayesian coring estimates of the images. Further constraints for smoothness, stability, and validity of the non-uniformity estimates are also imposed. The effectiveness and accuracy of the method has been demonstrated extensively with both BrainWeb phantom images as well as with real brain anatomic data of 29 Parkinson’s disease patients

Science-based restoration monitoring of coastal habitats, Volume Two: Tools for monitoring coastal habitats

Healthy coastal habitats are not only important ecologically; they also support healthy coastal communities and improve the quality of people’s lives. Despite their many benefits and values, coastal habitats have been systematically modified, degraded, and destroyed throughout the United States and its protectorates beginning with European colonization in the 1600’s (Dahl 1990). As a result, many coastal habitats around the United States are in desperate need of restoration. The monitoring of restoration projects, the focus of this document, is necessary to ensure that restoration efforts are successful, to further the science, and to increase the efficiency of future restoration efforts

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

The reconstruction of MRI data assumes a uniform radio-frequency field. However, in practice, the radio-frequency field is inhomogeneous and leads to anatomically inconsequential intensity non-uniformities across an image. An anatomic region can be imaged with multiple contrasts reconstructed independently and be suffering from different non-uniformities. These artifacts can complicate the further automated analysis of the images. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled and used for their non-stationary restoration followed by their back-projection to the images. Several constraints that ensure a stable restoration are also imposed. Moreover, the method considers the inevitable differences between the signal regions of the two images. The method has been evaluated extensively with BrainWeb phantom brain data as well as with brain anatomic data from the Human Connectome Project (HCP) and with data of Parkinson’s disease patients. The performance of the proposed method has been compared with that of the N4ITK tool. The proposed method increases tissues contrast at least 4 . 62 times more than the N4ITK tool for the BrainWeb images. The dynamic range with the N4ITK method for the same images is increased by up to +29.77%, whereas, for the proposed method, it has a corresponding limited decrease of - 1 . 15 % , as expected. The validation has demonstrated the accuracy and stability of the proposed method and hence its ability to reduce the requirements for additional calibration scans