Multilayered non-invasive temperature estimation from backscattered ultrasound

Roughly one in four breast cancer survivors report some degree of arm oedema. Lymphoedema is a build-up of excess lymph fluids in the tissues. Persistent lymphoedema leads to pain, diminished limb function, increased risk of infection, soft tissue fibrosis, and severe cases can be grossly disfiguring. From a mechanics perspective, the lymphoedemous tissue may be thought of as a two phase composite, consisting of both fluid and solid phases. Here we discuss the use of composites mixture theory to model the mechanics of lymphoedemous tissues. By treating the tissue as a fluid-solid composite, rules-of-mixtures may be used to estimate the effective moduli in terms of the properties of the individual components and their respective volume fractions in these two states.

Open source data sensing software for ultrasonic non-invasive temperature estimation

In this paper, an open source solution for measurement of temperature and ultrasonic signals (RF-lines) is proposed. This software is an alternative to the expensive commercial data acquisition software, enabling the user to tune applications to particular acquisition architectures. The collected ultrasonic and temperature signals were used for non-invasive temperature estimation using neural networks. The existence of precise temperature estimators is an essential point aiming at the secure and effective applica tion of thermal therapies in humans. If such estimators exist then effective controllers could be developed for the therapeutic instrumentation. In previous works the time-shift between RF-lines echoes were extracted, and used for creation of neural networks estimators. The obtained estimators successfully represent the temperature in the time-space domain, achieving a maximum absolute error inferior to the threshold value defined for hyperthermia/diathermia applications.