The mitral valve computational anatomy and geometry analysis

We present a novel methodology to characterize and quantify the Mitral Valve (MV) geometry and physical attributes in a multi-resolution framework. A multi-scale decomposition was implemented to model the MV geometry by using superquadric shape primitives and spectral reconstruction of the finer-scale geometric details. Superquadrics provide a basis to normalize the size and approximate a basic model of the MV geometry. The point-wise difference between the original geometry and the superquadric model denotes the finer-scale geometric details, which can be modeled as a scalar attribute for the MV model development. The additive decomposition of the basic MV geometry from geometric details (attributes) allows recovering the actual geometry by superposition of the superquadric approximation and the finer-details model. We implemented a lasso optimization algorithm to perform spectral analysis and develop the Fourier reconstruction of the geometric details. The spectral modeling enabled us to resample the geometric details or use spectral filters in order to adjust the spatial resolution in the model reconstruction. It also provides the basis to control the level of detail in the final model reconstruction by applying low-pass filters in the frequency domain. The higher-order attributes such as internal fiber architecture can be integrated with the geometric models using the same framework. We applied our pipeline to create models of three ovine MVs based on computed-tomography 3D images with micrometer resolution. We were able to quantify the MV leaflet geometry, reconstruct models with custom level of geometric details, and develop medial representation of the MV leaflet structure. The results show that our methodology for geometry analysis provides a basis for assessing patient-specific geometries and facilitates developing population-averaged models. Ultimately, this approach allows building personalized image-based computational models for medical device design and surgical treatment simulations.Mechanical Engineerin

Comparison of Crocus sativus L. and imipramine in the treatment of mild to moderate depression: A pilot double-blind randomized trial [ISRCTN45683816]

BACKGROUND: The morbidity and mortality associated with depression are considerable and continue to increase. Depression currently ranks fourth among the major causes of disability worldwide, after lower respiratory infections, prenatal conditions, and HIV/AIDS. Crocus sativus L. is used to treat depression. Many medicinal plants textbooks refer to this indication whereas there is no evidence-based document. Our objective was to compare the efficacy of stigmas of Crocus sativus (saffron) with imipramine in the treatment of mild to moderate depression in a 6-week pilot double-blind randomized trial. METHODS: Thirty adult outpatients who met the Diagnostic and Statistical Manual of Mental Disorders, 4(th )edition for major depression based on the structured clinical interview for DSM IV participated in the trial. Patients have a baseline Hamilton Rating Scale for Depression score of at least 18. In this double-blind, single-center trial, patients were randomly assigned to receive capsule of saffron 30 mg/day (TDS) (Group 1) and capsule of imipramine 100 mg/day (TDS) (Group 2) for a 6-week study. RESULTS: Saffron at this dose was found to be effective similar to imipramine in the treatment of mild to moderate depression (F = 2.91, d.f. = 1, P = 0.09). In the imipramine group anticholinergic effects such as dry mouth and also sedation were observed more often that was predictable. CONCLUSION: The main overall finding from this study is that saffron may be of therapeutic benefit in the treatment of mild to moderate depression. To the best of our knowledge this is the first clinical trial that supports this indication for saffron. A large-scale trial with placebo control is warranted

Multi-resolution modeling of the mitral valve : a novel computational pipeline for patient-specific simulations of valve repair

The mitral valve (MV) is the left atrio-ventricular heart valve that regulates blood flow direction during the cardiac cycle. Among the four heart valves, MV is the most problematic one, with MV-related pathologies directly afflicting 5% of the population in the industrialized world. Over the past 25 years, computational simulations of the MV based on biomechanical models have gained significant credibility in understanding valve function and improving surgical treatments. However, MV models with proven predictive power have yet to be developed on a patient-specific basis from clinical imaging data. The main challenge is that ultrasound, which is the prevailing imaging modality in the clinic, struggles to capture the full MV shape and its fine-scale geometric details. Thus, computational modeling of the MV for clinical applications first requires overcoming the obstacle that complete MV models cannot be developed directly from clinical images. In this Ph.D. project, we tackled this challenge through a detailed anatomical analysis of the MV constituents to better understand the comprising components of the MV apparatus and their impact on the MV modeling. This knowledge was then used to systematically identify the key characteristic of predictive MV modeling, build patient-specific models, and perform simulations of the MV repair. Remarkably, we established a framework to build faithful computational models of the MV for predictive surgical simulations based only on the information that can be acquired in the clinic and prior to the operation.Mechanical Engineerin