Claudio Lamberti

Quantification of left (LV) and right (RV) ventricular volumes and masses from Cardiac Magnetic Resonance (CMR) images is of prime importance for the clinical assessment of a wide variety of cardiac diseases. Despite over a decade of research aimed at the development of fast and reliable tools for automated endo- and epicardial contours identification, the problem is still open, particularly for the RV as a consequence of its more irregular shape and its higher density of trabeculations. In this study, a novel near-automated technique for the segmentation of LV endo- and epicardial as well as RV endocardial contours is presented. The technique is based on a 3D narrow-band statistical level set and on 2D edge-based level set algorithms. The technique was tested on CMR images acquired at both end-diastolic and end-systolic phases. For performance evaluation, an experienced interpreter manually traced ventricular contours, which were used as reference. A series of quantitative error metrics (e.g. mean absolute distance, MAD) were computed between automatically identified and manually traced contours. The results showed the high accuracy of the proposed technique (MAD: LV Endo = 1.4±0.7 px; RV Endo = 1.6±1.2 px; LV Epi = 1.4±0.6 px), which could thus potentially lead to the implementation of a tool for fast and reliable identification of ventricular contours.

The first part of this chapter reviews the design, implementation, and customer experience with the OLDES SW tele-care platform developed within the EU project Older people’s e-services at home. The OLDES solution has been successfully tested at two different locations: in Italy with the participation of a group of 100 seniors (including 10 senior citizens suffering from heart disease), and in the Czech Republic, with the involvement of a group of 10 diabetic patients. The suggested OLDES approach proved to be an effective solution for municipalities, hospitals, and their contact centres for providing health and social services. The project partners therefore decided to develop a second generation of the system called SPES (Support to Patients through E-Service Solutions), which started in April 2011. The SPES project aims at transferring the original approach and results achieved in implementing the OLDES focusing on new target problem domains: dementia, mobility-challenged persons, respiratory problems, and social exclusion.

This study demonstrated that despite the extreme dynamic nature of contrast-enhanced cardiac MR image sequences and respiratory motion, near-automated frame-by-frame detection of myocardial segments and high-quality quantification of myocardial contrast is feasible both at rest and during vasodilator stress. PurposeTo develop and validate a technique for near-automated definition of myocardial regions of interest suitable for perfusion evaluation during vasodilator stress cardiac magnetic resonance (MR) imaging.Materials and MethodsThe institutional review board approved the study protocol, and all patients provided informed consent. Image noise density distribution was used as a basis for endocardial and epicardial border detection combined with nonrigid registration. This method was tested in 42 patients undergoing contrast material–enhanced cardiac MR imaging (at 1.5 T) at rest and during vasodilator (adenosine or regadenoson) stress, including 15 subjects with normal myocardial perfusion and 27 patients referred for coronary angiography. Contrast enhancement–time curves were near–automatically generated and were used to calculate perfusion indexes. The results were compared with results of conventional manual analysis, using quantitative coronary angiography results as a reference for stenosis greater tha...

The need for high-quality out-of-hospital healthcare is a known socioeconomic problem. Exploiting ICT's evolution, ad-hoc telemedicine solutions have been proposed in the past. Integrating such ad-hoc solutions in order to cost-effectively support the entire healthcare cycle is still a research challenge. In order to handle the heterogeneity of relevant information and to overcome the fragmentation of out-of-hospital instrumentation in person-centric healthcare systems, a shared and open source interoperability component can be adopted, which is ontology driven and based on the semantic web data model. The feasibility and the advantages of the proposed approach are demonstrated by presenting the use case of real-time monitoring of patients' health and their environmental context.

Although cardiac resynchronization therapy (CRT) is an effective treatment for chronic systolic heart failure with dyssynchrony, about one-third of patients do not respond favorably. The interaction between the pacing lead and the coronary sinus (CS) branches is of paramount importance for an effective resynchronization. Minor changes in lead position overtime could interfere with CRT mechanics, without affecting even biophysical parameters or ECG morphology. Although late post-implant CS lead dislodgement rate is consistent, lead movements have been little investigated and only with bi-dimensional methods. The aim of this study was (1) to develop a method for quantifying CS lead position in the 3D domain throughout the cardiac cycle and (2) to test it by comparing the CS lead position at implant and at follow-up, using chest fluoroscopy. Method performance, its accuracy and reproducibility were qualitatively and quantitatively assessed. Intra- and inter-observer percent discordance between trajectories were also computed. The accuracy of the procedure resulted in 0.3 ± 0.1 mm and its resolution was 0.5 mm. Intra- and inter-observer discordances were 2.2 ± 1.5 and 5.5 ± 3.6 mm, respectively. The proposed method for measuring the CS lead dynamic placement in 3D space seems accurate and reproducible. Investigating CS lead 3D dynamics could provide further insights into CRT mechanics.

Purpose: To develop a technique based on image noise distribution for automated endocardial border detection from cardiac magnetic resonance (CMR) images throughout the cardiac cycle, validate it, and test its clinical utility.Materials and Methods: Images obtained in 36 patients were analyzed using custom software to obtain left ventricular (LV) volume throughout the cardiac cycle, end‐systolic and end‐diastolic LV volumes, and ejection fraction (EF). Validation against manually‐traced endocardial boundaries included intertechnique comparisons of LV volumes, slice areas, and border positions. Then, the clinical feasibility of the dynamic automated analysis of LV function was tested in 14 patients with normal LV function, 12 patients with systolic dysfunction, and 10 patients with diastolic dysfunction.Results: Analysis time for one cardiac cycle was 0.96), small biases (volumes: –6 mL; EF: 4.6%) and narrow limits of agreement (volumes: 17.6 mL; EF: 9.2%). We found significant intergroup differences in multiple quantitative indices of systolic and diastolic function.Conclusion: Fast, automated, dynamic detection of LV endocardial boundaries is feasible and allows accurate quantification of LV size and function, which is potentially clinically useful for objective assessment of systolic and diastolic dysfunction. J. Magn. Reson. Imaging 2009;29:560–568.

Mitral and aortic valves are known to be coupled via fibrous tissue connecting the two annuli. Previous studies evaluating this coupling have been limited to experimental animals using invasive techniques. The new matrix array transesophageal transducer provides high-resolution real-time 3D images of both valves simultaneously. We sought to develop and test a technique for quantitative assessment of mitral and aortic valve dynamics and coupling. Matrix array transesophageal (Philips iE33) imaging was performed in 24 patients with normal valves who underwent clinically indicated transesophageal echocardiography. Custom software was used to detect and track the mitral and aortic annuli in 3D space throughout the cardiac cycle, allowing automated measurement of changes in mitral and aortic valve morphology. Mitral annulus surface area and aortic annulus projected area changed reciprocally over time. Mitral annulus surface area was 8.0+/-2.1 cm(2) at end-diastole and decreased to 7.7+/-2.1 cm(2) in systole, reaching its maximum (10.0+/-2.2 cm(2)) at mitral valve opening. Aortic annulus projected area was 4.1+/-1.2 cm(2) at end-diastole, then increased during isovolumic contraction reaching its maximum (4.8+/-1.3 cm(2)) in the first third of systole and its minimum (3.6+/-1.0 cm(2)) during isovolumic relaxation. The angle between the mitral and aortic annuli was maximum (136+/-13 degrees ) at end-diastole and decreased to its minimum value (129+/-11 degrees ) during systole. This is the first study to report quantitative 3D assessment of the mitral and aortic valve dynamics from matrix array transesophageal images and describe the mitral-aortic coupling in a beating human heart. This ability may have impact on patient evaluation for valvular surgical interventions and prosthesis design.

Despite the potential of real-time three-dimensional (3D) echocardiography (RT3DE) to assess myocardial perfusion, there is no quantification method available for perfusion analysis from RT3DE images. Such method would require 3D regions of interest (ROI) to be defined and adjusted frame-by-frame to compensate for cardiac translation and deformation. Our aims were to develop and test a technique for automated identification of 3D myocardial ROI suitable for translation-free quantification of myocardial videointensity over time, MVI( t ), from contrast-enhanced RT3DE images. Twelve transthoracic RT3DE (Philips) data sets obtained in pigs during transition from no contrast to steady-state enhancement (Definity) were analysed using custom software. Analysis included: (i) semi-automated detection of left ventricular endo- and epicardial surfaces using level-set techniques in one frame to define a 3D myocardial ROI, (ii) rigid 3D registration to reduce translation and rotation, (iii) elastic 3D registration to compensate for deformation, and (iv) quantification of MVI( t ) in the 3D ROI from the registered and non-registered data sets to assess the effectiveness of registration. For each MVI( t ) curve we computed % variability during steady-state enhancement (100 × SD/mean) and goodness of fit ( r² ) to the indicator dilution equation MVI( t ) = A [1−exp(− βt )]. Analysis of myocardial contrast throughout contrast inflow was feasible in all data sets. Three-dimensional registration improved MVI( t ) curves in terms of both % variability (2.8 ± 1.8 to 1.5 ± 0.9%; P < 0.05) and goodness of fit ( r² from 0.79 ± 0.2 to 0.90 ± 0.1; P < 0.05). This is the first study to describe a new technique for semi-automated volumetric quantification of myocardial contrast from RT3DE images that includes registration and thus provides the basis for 3D measurement of myocardial perfusion.