Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of ima...

When the standard approach to predict protein function by sequence homology fails, other alternative methods can be used that require only the amino acid sequence for predicting function. One such approach uses machine learning to predict protein function directly from amino acid sequence features. However, there are two issues to consider before successful functional prediction can take place:...

Assuming sparsity or compressibility of the underlying signals, compressed sensing or compressive sampling (CS) exploits the informational efficiency of under-sampled measurements for increased efficiency yet acceptable accuracy in information gathering, transmission and processing, though it often incurs extra computational cost in signal reconstruction. Shannon information quantities and theo...

Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positi...

Background/introduction Many applications of thermal therapy would benefit from temperature distribution measurements with high spatial and temporal resolution that cover a large 3D volume. Although MRI can acquire 3D temperature measurements, it is not possible to obtain fully sampled 3D MRI measurements that cover the insonified field of view with sufficient spatial and temporal resolution fo...

This paper takes a new look at two sampling schemes commonly used to adapt machine algorithms to imbalanced classes and misclassification costs. It uses a performance analysis technique called cost curves to explore the interaction of over and under-sampling with the decision tree learner C4.5. C4.5 was chosen as, when combined with one of the sampling schemes, it is quickly becoming the commun...

Background Ungated myocardial perfusion is a promising alternative for simplifying CMR protocols [1]. Spatio-temporal total variation (TV) constrained reconstruction with radial undersampling was used in a pilot study [2]. And TV constraints combined with a low-rank constraint have shown improvement in some cases for gated perfusion imaging [3]. However, the temporal total-variation constraint ...

This paper proposes a robust algorithm to compute caustics caused by multiple reflections and refractions on the GPU. The proposed algorithm solves two problems of previous methods, caustic light leaks and caustic undersampling. In order to eliminate caustic light leaks, terminal photon hits are stored and caustic patterns are reconstructed on the faces of a layered distance map attached to the...