Investigating the potential magnetic origin of wind variability in OB stars

In this thesis, the origin of large-scale structures in hot star winds, believed to be responsible for the presence of discrete absorption components (DACs) in the absorption troughs of ultraviolet resonance lines, is constrained using both observations and numerical simulations. These structures are understood as arising from bright regions on the stellar surface, although their physical cause remains unknown. First, we use high quality circular spectropolarimetric observations of 13 well-studied OB stars to evaluate the potential role of dipolar magnetic fields in producing DACs. We perform longitudinal field measurements and place limits on the field strength using Bayesian inference, assuming that it is dipolar. No magnetic field was detected within this sample. The derived constraints statistically refute any significant dynamical influence from a magnetic dipole on the wind for all of these stars, ruling out such fields as a cause for DACs. Second, we perform numerical simulations using bright spots constrained by broadband optical photometric observations. We calculate hydrodynamical wind models using three sets of spot sizes and strengths. Co-rotating interaction regions are yielded in each model, and radiative transfer shows that the properties of the variations in the UV resonance lines synthesized from these models are consistent with those found in observed UV spectra, establishing the first consistent link between UV spectroscopic line profile variability and photometric variations and thus supporting the bright spot paradigm (BSP). Finally, we develop and apply a phenomenological model to quantify the measurable effects co-rotating bright spots would have on broadband optical photometry and on the profiles of photopheric lines in optical spectra. This model can be used to evaluate the existence of these spots, and, in the event of their detection, characterize them. Furthermore, a tentative spot evolution model is presented. A preliminary analysis of its output, compared to the observed photometric variations of xi Persei, suggests the possible existence of “active longitudes” on the surface of this star. Future work will expand the range of observational diagnostics that can be interpreted within the BSP, and link phenomenology (bright spots) to physical processes (magnetic spots or non-radial pulsations).

Detection and Grading of Prostate Cancer in Temporal Enhanced Ultrasound using Convolutional Neural Networks

Prostate cancer is the most common non-dermatological cancer amongst men in the developed world. The current definitive diagnosis is core needle biopsy guided by transrectal ultrasound. However, this method suffers from low sensitivity and specificity in detecting cancer. Recently, a new ultrasound based tissue typing approach has been proposed, known as temporal enhanced ultrasound (TeUS). In this approach, a set of temporal ultrasound frames is collected from a stationary tissue location without any intentional mechanical excitation. The main aim of this thesis is to implement a deep learning-based solution for prostate cancer detection and grading using TeUS data. In the proposed solution, convolutional neural networks are trained to extract high-level features from time domain TeUS data in temporally and spatially adjacent frames in nine in vivo prostatectomy cases. This approach avoids information loss due to feature extraction and also improves cancer detection rate. The output likelihoods of two TeUS arrangements are then combined to form our novel decision support system. This deep learning-based approach results in the area under the receiver operating characteristic curve (AUC) of 0.80 and 0.73 for prostate cancer detection and grading, respectively, in leave-one-patient-out cross-validation. Recently, multi-parametric magnetic resonance imaging (mp-MRI) has been utilized to improve detection rate of aggressive prostate cancer. In this thesis, for the first time, we present the fusion of mp-MRI and TeUS for characterization of prostate cancer to compensates the deficiencies of each image modalities and improve cancer detection rate. The results obtained using TeUS are fused with those attained using consolidated mp-MRI maps from multiple MR modalities and cancer delineations on those by multiple clinicians. The proposed fusion approach yields the AUC of 0.86 in prostate cancer detection. The outcomes of this thesis emphasize the viable potential of TeUS as a tissue typing method. Employing this ultrasound-based intervention, which is non-invasive and inexpensive, can be a valuable and practical addition to enhance the current prostate cancer detection.