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). Stress perfusion defect was quantified by visual evaluation of SPECT MPI. SPECT MPI was also used for the quantification of rest and stress MPE. The primary end point was major adverse cardiac events (MACEs) defined as cardiac death, myocardial infarction (MI), and myocardial revascularization > 3 months after SPECT. RESULTS: Forty-four MACEs were observed during a 4.6-year median follow-up. Significant differences in stress MPE were observed between patients with and without MACEs (4.19 ± 0.46 vs. 3.93 ± 0.40; P ≤ .01). By Kaplan-Meier analysis, the risk of MACEs was significantly higher in patients with higher stress MPE (log-rank P ≤ 01). Stress MPE and stress perfusion defect (SSS ≥ 4) were significantly associated with the risk of MACEs (hazard ratio 2.77 and 2.06, respectively, P < .05 for both) after adjustment for clinical and imaging risk predictors as identified from preliminary univariate analysis. MPE demonstrated incremental prognostic value over clinical risk factors, stress test EKG and SSS as evidenced by nested models showing improved Akaike information criterion (AIC), reclassification (global continuous net reclassification improvement [NRI]: 63), global integrated discrimination improvement (IDI: 6%), and discrimination (change in c-statistic: 0.66 vs 0.74). CONCLUSIONS: Stress MPE provided independent and incremental prognostic information for the prediction of MACEs in diabetic patients. TRIAL REGISTRATION NUMBER: NCT02316054 (12/12/2014).

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. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the state-of-the-art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.

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. Source separation methods, such as factor analysis of dynamic sequences, have been developed to unmix such data. However, the underlying problem is underdetermined and the model used is not relevant in the whole image. This non-uniqueness issue was overcome by introducing prior knowledge, such as sparsity or smoothness, in the separation model. In practice, these methods are barely used because of the lack of reliability of their results. Previously developed methods aimed to be fully automatic, but efficiency can be improved with additional prior knowledge. Some methods using ROIs knowledge in a straightforward way have been proposed. In this paper, we propose an unmixing method, based on an objective function minimization and integrating these ROIs in a different and robust manner. The objective function promotes consistent solutions regarding ROIs while relaxing the model outside ROIs. In order to reduce user-dependent effects, ROIs are used as soft constraints in a robust way through the use of a distance matrix. Consistency, effectiveness, and robustness to the ROIs selection are demonstrated on a toy example, a highly realistic simulated renography data set and a clinical data set. Performance is compared with the competitive methods.

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. The coefficients of this basis are found by optimizing iteratively a fidelity term under the constraint of a known sub-region. Using a coarse basis reduces the degrees of freedom of the problem while actually correcting the cupping effect. Simulations show that the known region constraint yields an unbiased reconstruction, in accordance with uniqueness theorems stated in local tomography.

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. Efficient restoration and denoising algorithms should overcome this drawback. We propose a spatio-temporal filter operating in a multi-scales space. This filter relies on a first order, motion compensated, recursive temporal denoising. Temporal high frequency content is first detected and then matched over time to allow for a strong denoising in the temporal axis. We study this filter in the curvelet domain and in the dual-tree complex wavelet domain, and compare those results to state of the art methods. Quantitative and qualitative analysis on both synthetic and real fluoroscopic sequences demonstrate that the proposed filter allows a great dose reduction.

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. The clustering approach we take to simplify images prior to segmentation strikes a good balance between boundary adaptivity and cluster compactness criteria furthermore allowing to control the trade-off. Compared with a direct application of segmentation on voxels, the clustering step improves the overall runtime and memory footprint of the segmentation process up to an order of magnitude without compromising the quality of the result.