Prognostic value of SPECT myocardial perfusion entropy in high-risk type 2 diabetic patients.

Purpose: Risk stratification of patients with type 2 diabetes mellitus (T2D) remains suboptimal. We hypothesized that myocardial perfusion entropy (MPE) quantified from SPECT myocardial perfusion images may provide incremental prognostic value in T2D patients independently from myocardial ischemia.

Methods: T2D patients with very high and high cardiovascular risk were prospectively included (n = 166, 65 ± 12 years).

Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut.

We propose an automatic multiorgan segmentation method for 3-D radiological images of different anatomical contents and modalities. The approach is based on a simultaneous multilabel graph cut optimization of location, appearance, and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple four-parameter transformation.

Robust Unmixing of Dynamic Sequences Using Regions of Interest.

In dynamic planar imaging, extraction of signals specific to structures is complicated by structures superposition. Due to overlapping, signals extraction with classic regions of interest (ROIs) methods suffers from inaccuracy, as extracted signals are a mixture of targeted signals. Partial volume effect raises the same issue in dynamic tomography.

A practical local tomography reconstruction algorithm based on a known sub-region.

A new method to reconstruct data acquired in a local tomography setup is proposed. This method uses an initial reconstruction and refines it by correcting the low-frequency artifacts, known as the cupping effect. A basis of Gaussian functions is used to correct the initial reconstruction.

Spatio-Temporal Multiscale Denoising of Fluoroscopic Sequence.

In the past 20 years, a wide range of complex fluoroscopically guided procedures have shown considerable growth. Biologic effects of the exposure (radiation induced burn, cancer) lead to reduce the dose during the intervention, for the safety of patients and medical staff. However, when the dose is reduced, image quality decreases, with a high level of noise and a very low contrast.

Shortest-path constraints for 3D multiobject semiautomatic segmentation via clustering and Graph Cut.

We derive shortest-path constraints from graph models of structure adjacency relations and introduce them in a joint centroidal Voronoi image clustering and Graph Cut multiobject semiautomatic segmentation framework. The vicinity prior model thus defined is a piecewise-constant model incurring multiple levels of penalization capturing the spatial configuration of structures in multiobject segmentation. Qualitative and quantitative analyses and comparison with a Potts prior-based approach and our previous contribution on synthetic, simulated, and real medical images show that the vicinity prior allows for the correct segmentation of distinct structures having identical intensity profiles and improves the precision of segmentation boundary placement while being fairly robust to clustering resolution.