Where and when

The "International Congress on Industrial and Applied Mathematics" (ICIAM) is a global gathering dedicated to researchers, academics, scholars, professionals and experts in the field of applied mathematics and various related sectors. The 10th edition takes place in Japan, hosted at Waseda University in Tokyo. ICIAM represents a genuine opportunity for the sharing of knowledge and progress in applied mathematics across diverse scientific, technological, engineering and industrial sectors.

The MIDA group is taking part in this meeting with the active participation of Federico Benvenuto, Valentina Candiani and Chiara Razzetta.

Valentina Candiani and Federico Benvenuto organized the mini symposium "Recent advances in Ultrasound Biomedical Imaging” (details here), where Chiara Razzetta gave her talk "A local space-invariant approximation for DAS Point Spread Function computation”.

• Abstract: The Delay And Sum (DAS) algorithm is the standard technique for ultrasound image reconstruction, it is usually implemented on the hardware of the ultrasound device and it depends on several parameters set in the machine. This makes it possible to produce real time images but at the same time it is a limitation in studying parameter optimization to obtain better reconstructions. In this talk, we propose an approximation of the computation of the DAS algorithm by decomposing it into a sum of space-invariant operators by means of a partition of the unity. This approximation allows parameter optimization algorithms to be applied to the DAS in order to increase the resolution of the reconstruction.

Valentina Candiani also participated in the mini symposium “Recent Advancements in Electrical Impedance Tomography” (details here) with her talk “Exploration of deep generative modelling approaches to electrical impedance tomography”.

• Abstract: Reconstruction of conductivity images in electrical impedance tomography (EIT) requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and solution algorithms need either simplifying assumptions or regularization based on a priori knowledge. In this work we study the applicability, the challenges and the limitations of some relatively new deep generative models such as score-based generative diffusion models and normalising flows, for both image reconstruction and medical anomaly detection. This talk will present some preliminary results obtained with such approaches in the application of EIT to the detection of stroke.

Program Overview