Dosik Hwang

KIKI-net exhibits superior performance over state-of-the-art conventional algorithms in terms of restoring tissue structures and removing aliasing artifacts. The results demonstrate that KIKI-net is applicable up to a reduction factor of 3 to 4 based on variable-density Cartesian undersampling.

The MWF variability due to the measurement noise can be substantially reduced and the detection of small focal lesions can be improved by using the srNNLS algorithm.

We propose a new deep learning network capable of successfully segmenting intervertebral discs and their complex boundaries from magnetic resonance (MR) spine images. The existing U-network (U-net) is known to perform well in various segmentation tasks in medical images; however, its performance with respect to details of segmentation such as boundaries is limited by the structural limitations of a max-pooling layer that plays a key role in feature extraction process in the U-net. We designed a

Human lumbar spine is composed of multiple tissue components that serve to provide structural stability and proper nutrition. Conventional magnetic resonance (MR) imaging techniques have been useful for evaluation of IVD, but inadequate at imaging the discovertebral junction and ligamentous tissues due primarily to their short T2 nature. Ultrashort time to echo (UTE) MR techniques acquire sufficient MR signal from these short T2 tissues, thereby allowing direct and quantitative evaluation. This

We propose a deep learning-based spike sorting method for extracellular recordings. For analysis of extracellular single unit activity, the process of detecting and classifying action potentials called “spike sorting” has become essential. This is achieved through distinguishing the morphological differences of the spikes from each neuron, which arises from the differences of the surrounding environment and characteristics of the neurons. However, cases of high structural similarity and noise ma

DPI-net was effective in reconstructing 3D TOF MRA from highly undersampled multichannel MR data, achieving superior performance, both quantitatively and qualitatively, over conventional parallel imaging and other deep-learning methods.

Purpose This study aimed to develop a fully automatic, no‐reference image‐quality assessment (IQA) method for MR images. Methods New quality‐aware features were obtained by applying multidirectional filters to MR images and examining the feature statistics. A histogram of these features was then fitted to a generalized Gaussian distribution function for which the shape parameters yielded different values depending on the type of distortion in the MR image. Standard feature statistics were establ

This study demonstrates that the benefits of ADF and srNNLS algorithms can be effectively combined in a synergic way for robust mapping of MWF in the presence of noise. Substantial improvements to MWF mapping can be made using the proposed method.

We propose a semi-automatic algorithm for the segmentation of vertebral bodies in magnetic resonance (MR) images of the human lumbar spine. Quantitative analysis of spine MR images often necessitate segmentation of the image into specific regions representing anatomic structures of interest. Existing algorithms for vertebral body segmentation require heavy inputs from the user, which is a disadvantage. For example, the user needs to define individual regions of interest (ROIs) for each vertebral