Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach.

Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently.

New Similarity Metric for Registration of MRI to Histology: Golden Retriever Muscular Dystrophy Imaging.

Objective: Histology is often used as a gold standard to evaluate noninvasive imaging modalities such as a magnetic resonance imaging (MRI). Spatial correspondence between histology and MRI is a critical step in quantitative evaluation of skeletal muscle in golden retriever muscular dystrophy (GRMD). Registration becomes technically challenging due to nonorthogonal histology section orientation, section distortion, and the different image contrast and resolution.

Correlating 2D histological slice with 3D MRI image volume using smart phone as an interactive tool for muscle study.

In muscle dystrophy studies, registration of histological image with MRI image volume enables cross validation of MRI biomarkers using pathological result. However, correlation of 2D histology slice with 3D MRI volume is technically challenging due to the potentially non-orthogonal slice plane and incomplete or distorted histological slice. This paper presents an efficient method to directly perform the 2D-3D registration.

Parameter estimation in arterial spin labeling MRI: comparing the Four Phase model and the Buxton model with Fourier transform.

This paper presents a comparison between two algorithms that analyze and extract brain perfusion parameters from pulsed arterial spin labeling (ASL). One algorithm is based on the Four Phase Single Capillary Stepwise (FPSCS) model, which divides the time course of the signal difference between the control and labeled image into four phases. The other algorithm utilizes the Buxton model and Fourier transformation (FTB).

Spatial and temporal resolution effects on dynamic contrast-enhanced magnetic resonance mammography.

We tested the hypothesis that partial volume effects due to poor in-plane resolution and/or low temporal resolution used in clinical dynamic contrast-enhanced magnetic resonance imaging results in erroneous diagnostic information based on inaccurate estimates of tumor contrast agent extravasation and tested whether reduced encoding techniques can correct for dynamic data volume averaging. Image spatial resolution was reduced from 469 x 469 microm2 to those reported below by selecting a subset of k-space data. We then compared the top five K(trans)/V(T) "hot spots" obtained from the original data set, 469 x 469-microm in-plane spatial resolution and an 18-s temporal resolution processed by fast Fourier transform (FFT), with values obtained from data sets having in-plane spatial resolutions of 938 x 938, 1875 x 1875 and 2500 x 2500 microm2 and a temporal resolution of 18 s, or data sets with temporal resolutions of 36, 54 and 72 and a spatial resolution of 469 x 469 microm2, and found them to statistically differ from the parent data sets.

Further analysis of interpolation effects in mutual information-based image registration.

This paper presents an analysis of the mutual information (MI) metric in rigid-body registration of two digital images, in particular, local fluctuations of the MI value due to interpolation. In contrast to existing work in this area, this paper starts with two hypothetical continuous images, based on which both sampling and interpolation effects are analyzed. This analysis indicates that an "ideal" interpolator may not be able to completely suppress the undesirable local minima of the MI metric if the sampling effect is not negligible.