Frequency-dependent attenuation reconstruction with an acoustic reflector.

Attenuation of ultrasound waves varies with tissue composition, hence its estimation offers great potential for tissue characterization and diagnosis and staging of pathology. We recently proposed a method that allows to spatially reconstruct the distribution of the overall ultrasound attenuation in tissue based on computed tomography, using reflections from a passive acoustic reflector. This requires a standard ultrasound transducer operating in pulse-echo mode and a calibration protocol using water measurements, thus it can be implemented on conventional ultrasound systems with minor adaptations. Herein, we extend this method by additionally estimating and imaging the frequency-dependent nature of local ultrasound attenuation for the first time. Spatial distributions of attenuation coefficient and exponent are reconstructed, enabling an elaborate and expressive tissue-specific characterization. With simulations, we demonstrate that our proposed method yields a low reconstruction error of 0.04 dB/cm at 1 MHz for attenuation coefficient and 0.08 for the frequency exponent. With tissue-mimicking phantoms and ex-vivo bovine muscle samples, a high reconstruction contrast as well as reproducibility are demonstrated. Attenuation exponents of a gelatin-cellulose mixture and an ex-vivo bovine muscle sample were found to be, respectively, 1.4 and 0.5 on average, consistently from different images of their heterogeneous compositions. Such frequency-dependent parametrization could enable novel imaging and diagnostic techniques, as well as facilitate attenuation compensation of other ultrasound-based imaging techniques.

Inference on homeostatic belief precision.

Interoception and homeostatic/allostatic control are intertwined branches of closed-loop brain-body interactions (BBI). Given their importance in mental and psychosomatic disorders, establishing computational assays of BBI represents a clinically important but methodologically challenging endeavor. This technical note presents a novel approach, derived from a generic computational model of homeostatic/allostatic control that underpins (meta)cognitive theories of affective and psychosomatic disorders. This model views homeostatic setpoints as probability distributions ("homeostatic beliefs") whose parameters determine regulatory efforts and change dynamically under allostatic predictions. In particular, changes in homeostatic belief precision, triggered by anticipated threats to homeostasis, are thought to alter cerebral regulation of bodily states. Here, we present statistical procedures for inferring homeostatic belief precision from measured bodily states and/or regulatory (action) signals. We analyze the inference problem, derive two alternative estimators of homeostatic belief precision, and apply our method to simulated data. Our proposed approach may prove useful for assessing BBI in individual subjects.