Learning the dynamics and time-recursive boundary detection of deformable objects

We propose a principled framework for recursively segmenting deformable objects across a sequence of frames. We demonstrate the usefulness of this method on left ventricular segmentation across a cardiac cycle. The approach involves a technique for learning the system dynamics together with methods of particle-based smoothing as well as non-parametric belief propagation on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and the boundary estimation involves incorporating curve evolution into recursive state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. Although the paper focuses on left ventricle segmentation, the method generalizes to temporally segmenting any deformable object

Segmentation of the evolving left ventricle by learning the dynamics

We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences

Motion Compensation for Free-Breathing Abdominal Diffusion-Weighted Imaging (MoCo DWI)

Diffusion-weighted imaging (DWI) is a common technique in medical diagnostics. One challenge of thoracic and abdominal DWI is respiratory motion which can result in motion artifacts. To eliminate these artifacts, a new kind of retrospective, respiratory motion compensation for DWI was developed and tested. This new technique — MoCo DWI — is the first in DWI which provides fully-deformable motion compensation. To enable this, despite the low image quality of DWI, two free-breathing sequences were used: (1) a gradient echo sequence (GRE) with a configuration for optimal respiratory motion estimation and (2) a DWI in a configuration of clinical interest. The DWI acquisition was gated into 10 motion phases. Each motion phase was then co-aligned with the motion estimation. The implementation was tested with eleven volunteers. The results showed that MoCo DWI can reduce motion blurring in single b-value images, especially at the liver-lung interface. The improvement of ADC-maps was even more prominent. Individual slices showed motion induced artifacts which could be reduced or even eliminated by MoCo DWI. This was also reflected by expected more homogeneous ADC values in the liver in all data sets. These results promise to reduce measurements with limited diagnostic value while keeping or increasing patient comfort

preliminary clinical evaluation of the ASTRA4D algorithm

Objectives. To propose and evaluate a four-dimensional (4D) algorithm for joint motion elimination and spatiotemporal noise reduction in low-dose dynamic myocardial computed tomography perfusion (CTP). Methods. Thirty patients with suspected or confirmed coronary artery disease were prospectively included und underwent dynamic contrast-enhanced 320-row CTP. The presented deformable image registration method ASTRA4D identifies a low-dimensional linear model of contrast propagation (by principal component analysis, PCA) of the ex-ante temporally smoothed time-intensity curves (by local polynomial regression). Quantitative (standard deviation, signal-to-noise ratio (SNR), temporal variation, volumetric deformation) and qualitative (motion, contrast, contour sharpness; 1, poor; 5, excellent) measures of CTP quality were assessed for the original and motion-compensated volumes (without and with temporal filtering, PCA/ASTRA4D). Following visual myocardial perfusion deficit detection by two readers, diagnostic accuracy was evaluated using 1.5T magnetic resonance (MR) myocardial perfusion imaging as the reference standard in 15 patients. Results. Registration using ASTRA4D was successful in all 30 patients and resulted in comparison with the benchmark PCA in significantly (p<0.001) reduced noise over time (-83%, 178.5 vs 29.9) and spatially (-34%, 21.4 vs 14.1) as well as improved SNR (+47%, 3.6 vs 5.3) and subjective image quality (motion, contrast, contour sharpness: +1.0, +1.0, +0.5). ASTRA4D resulted in significantly improved per-segment sensitivity of 91% (58/64) and similar specificity of 96% (429/446) compared with PCA (52%, 33/64; 98%, 435/446; p=0.011) and the original sequence (45%, 29/64; 98%, 438/446; p=0.003) in the visual detection of perfusion deficits. Conclusions. The proposed functional approach to temporal denoising and morphologic alignment was shown to improve quality metrics and sensitivity of 4D CTP in the detection of myocardial ischemia.Zielsetzung. Die Entwicklung und Bewertung einer Methode zur simultanen Rauschreduktion und Bewegungskorrektur für niedrig dosierte dynamische CT Myokardperfusion. Methoden. Dreißig prospektiv eingeschlossene Patienten mit vermuteter oder bestätigter koronarer Herzkrankheit wurden einer dynamischen CT Myokardperfusionsuntersuchung unterzogen. Die präsentierte Registrierungsmethode ASTRA4D ermittelt ein niedrigdimensionales Modell des Kontrastmittelflusses (mittels einer Hauptkomponentenanalyse, PCA) der vorab zeitlich geglätteten Intensitätskurven (mittels lokaler polynomialer Regression). Quantitative (Standardabweichung, Signal-Rausch-Verhältnis (SNR), zeitliche Schwankung, räumliche Verformung) und qualitative (Bewegung, Kontrast, Kantenschärfe; 1, schlecht; 5, ausgezeichnet) Kennzahlen der unbearbeiteten und bewegungskorrigierten Perfusionsdatensätze (ohne und mit zeitlicher Glättung PCA/ASTRA4D) wurden ermittelt. Nach visueller Beurteilung von myokardialen Perfusionsdefiziten durch zwei Radiologen wurde die diagnostische Genauigkeit im Verhältnis zu 1.5T Magnetresonanztomographie in 15 Patienten ermittelt. Resultate. Bewegungskorrektur mit ASTRA4D war in allen 30 Patienten erfolgreich und resultierte im Vergleich mit der PCA Methode in signifikant (p<0.001) verringerter zeitlicher Schwankung (-83%, 178.5 gegenüber 29.9) und räumlichem Rauschen (-34%, 21.4 gegenüber 14.1) sowie verbesserter SNR (+47%, 3.6 gegenüber 5.3) und subjektiven Qualitätskriterien (Bewegung, Kontrast, Kantenschärfe: +1.0, +1.0, +0.5). ASTRA4D resultierte in signifikant verbesserter segmentweiser Sensitivität 91% (58/64) und ähnlicher Spezifizität 96% (429/446) verglichen mit der PCA Methode (52%, 33/64; 98%, 435/446; p=0.011) und dem unbearbeiteten Perfusionsdatensatz (45%, 29/64; 98%, 438/446; p=0.003) in der visuellen Beurteilung von myokardialen Perfusionsdefiziten. Schlussfolgerungen. Der vorgeschlagene funktionale Ansatz zur simultanen Rauschreduktion und Bewegungskorrektur verbesserte Qualitätskriterien und Sensitivität von dynamischer CT Perfusion in der visuellen Erkennung von Myokardischämie

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging

Cardiovascular diseases are among the leading causes of death and frequently result in local myocardial dysfunction. Among the numerous imaging modalities available to detect these dysfunctional regions, cardiac deformation imaging through tagged magnetic resonance imaging (t-MRI) has been an attractive approach. Nevertheless, fully automatic analysis of these data sets is still challenging. In this work, we present a fully automatic framework to estimate left ventricular myocardial deformation from t-MRI. This strategy performs automatic myocardial segmentation based on B-spline explicit active surfaces, which are initialized using an annular model. A non-rigid image-registration technique is then used to assess myocardial deformation. Three experiments were set up to validate the proposed framework using a clinical database of 75 patients. First, automatic segmentation accuracy was evaluated by comparing against manual delineations at one specific cardiac phase. The proposed solution showed an average perpendicular distance error of 2.35 +/- 1.21 mm and 2.27 +/- 1.02 mm for the endo- and epicardium, respectively. Second, starting from either manual or automatic segmentation, myocardial tracking was performed and the resulting strain curves were compared. It is shown that the automatic segmentation adds negligible differences during the strain-estimation stage, corroborating its accuracy. Finally, segmental strain was compared with scar tissue extent determined by delay-enhanced MRI. The results proved that both strain components were able to distinguish between normal and infarct regions. Overall, the proposed framework was shown to be accurate, robust, and attractive for clinical practice, as it overcomes several limitations of a manual analysis.FCT—Fundacão para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/95438/2013 (P Morais) and SFRH/BD/93443/2013 (S Queirós). This work was supported by the projects NORTE-07-0124-FEDER-000017 and NORTE-01-0145-FEDER-000013, co-funded by Programa Operacional Regional do Norte, Quadro de Referência Estratégico Nacional, through Fundo Europeu de Desenvolvimento Regional (FEDER). The authors would also like to acknowledge the EU (FP7) framework program, for the financial support of the DOPPLER-CIP project (grant no. 223615)info:eu-repo/semantics/publishedVersio