Bayesian multi-dipole modelling in the frequency domain.

BACKGROUND: Magneto- and Electro-encephalography record the electromagnetic field generated by neural currents with high temporal frequency and good spatial resolution, and are therefore well suited for source localization in the time and in the frequency domain. In particular, localization of the generators of neural oscillations is very important in the study of cognitive processes in the healthy and in the pathological brain. NEW METHOD: We introduce the use of a Bayesian multi-dipole localization method in the frequency domain. Given the Fourier Transform of the data at one or multiple frequencies and/or trials, the algorithm approximates numerically the posterior distribution with Monte Carlo techniques. RESULTS: We use synthetic data to show that the proposed method behaves well under a wide range of experimental conditions, including low signal-to-noise ratios and correlated sources. We use dipole clusters to mimic the effect of extended sources. In addition, we test the algorithm on real MEG data to confirm its feasibility. COMPARISON WITH EXISTING METHOD(S): Throughout the whole study, DICS (Dynamic Imaging of Coherent Sources) is used systematically as a benchmark. The two methods provide similar general pictures; the posterior distributions of the Bayesian approach contain much richer information at the price of a higher computational cost. CONCLUSIONS: The Bayesian method described in this paper represents a reliable approach for localization of multiple dipoles in the frequency domain.

Assessment of Skeletal Tumor Load in Metastasized Castration-Resistant Prostate Cancer Patients: A Review of Available Methods and an Overview on Future Perspectives.

Metastasized castration-resistant prostate cancer (mCRPC), is the most advanced form of prostate neoplasia, where massive spread to the skeletal tissue is frequent. Patients with this condition are benefiting from an increasing number of treatment options. However, assessing tumor response in patients with multiple localizations might be challenging. For this reason, many computational approaches have been developed in the last decades to quantify the skeletal tumor burden and treatment response. In this review, we analyzed the progressive development and diffusion of such approaches. A computerized literature search of the PubMed/Medline was conducted, including articles between January 2008 and March 2018. The search was expanded by manually reviewing the reference list of the chosen articles. Thirty-five studies were identified. The number of eligible studies greatly increased over time. Studies could be categorized in the following categories: automated analysis of 2D scans, SUV-based thresholding, hybrid CT- and SUV-based thresholding, and MRI-based thresholding. All methods are discussed in detail. Automated analysis of bone tumor burden in mCRPC is a growing field of research; when choosing the appropriate method of analysis, it is important to consider the possible advantages as well as the limitations thoroughly.