Spinal cord injury: assessment of autonomic state-dependent control of cardiovascular system and body core temperature

Spinal cord injury (SCI) results not only in paralysis; but it is also associated with a range of autonomic dysregulation that can interfere with cardiovascular, bladder, bowel, temperature, and sexual function. The entity of the autonomic dysfunction is related to the level and severity of injury to descending autonomic (sympathetic) pathways. For many years there was limited awareness of these issues and the attention given to them by the scientific and medical community was scarce. Yet, even if a new system to document the impact of SCI on autonomic function has recently been proposed, the current standard of assessment of SCI (American Spinal Injury Association (ASIA) examination) evaluates motor and sensory pathways, but not severity of injury to autonomic pathways. Beside the severe impact on quality of life, autonomic dysfunction in persons with SCI is associated with increased risk of cardiovascular disease and mortality. Therefore, obtaining information regarding autonomic function in persons with SCI is pivotal and clinical examinations and laboratory evaluations to detect the presence of autonomic dysfunction and quantitate its severity are mandatory. Furthermore, previous studies demonstrated that there is an intimate relationship between the autonomic nervous system and sleep from anatomical, physiological, and neurochemical points of view. Although, even if previous epidemiological studies demonstrated that sleep problems are common in spinal cord injury (SCI), so far only limited polysomnographic (PSG) data are available. Finally, until now, circadian and state dependent autonomic regulation of blood pressure (BP), heart rate (HR) and body core temperature (BcT) were never assessed in SCI patients. Aim of the current study was to establish the association between the autonomic control of the cardiovascular function and thermoregulation, sleep parameters and increased cardiovascular risk in SCI patients.

Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)

The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.