<italic>In Vivo</italic> Ultrasound Thermography in Presence of Temperature Heterogeneity and Natural Motions

Abstract

Real-time ultrasound thermography has been recently demonstrated on commercially-available diagnostic imaging probes. In vitro experimental results demonstrate high sensitivity to small, localized temperature changes induced by subtherapeutic focused ultrasound. Most of the published results, however, are based on a thermally-induced echo strain model that assumes infinitesimal change in temperature between imaging frames. Under this assumption, the echo strain is computed using a lowpass axial differentiator which is implemented by a finite impulse response (FIR) digital filter. In this paper, we introduce a new model for temperature estimation which employs a recursive axial filter that acts as a spatial differentiator-integrator of echo shifts. The filter is derived from first principles and it accounts for a nonuniform temperature baseline when computing the spatial temperature change between two frames. This is a major difference from the previously proposed infinitesimal echo strain filter (δ-ESF) approach. We show that the new approach can be implemented by a first-order infinite impulse response (IIR) digital filter with depth-dependent spatial frequency response. Experimental results in vitro demonstrate the advantages over the δ-ESF approach in terms of suppressing the spatial variations in the estimated temperature without resorting to ad hoc lowpass filtering of echo strains. The performance of the new recursive echo strain filter (RESF) is also illustrated using echo data obtained during sub-therapeutic localized heating in the hind limb of Copenhagen rat in vivo. In addition to the RESF, we have used an adaptive spatial filter to remove motion and deformation artifacts during real-time data collection. The adaptive filtering algorithm is described and comparisons with uncompensated estimated spatio-temporal temperature profiles are given. The results demonstrate the feasibility of in vivo ultrasound thermography with high sensitivity and specificity.

Acoustical method of whole-body hydration status monitoring

“…It is impossible to achieve in the through-transmission method but there are other modes of acoustic measurements to get ultrasound velocity in a small volume within the muscle, eliminating the contributions of surrounding structures. There are numerous devices developed for remote monitoring of the change of temperature in tissues during thermotherapy by measuring local ultrasound velocity [31–35]. In the physiological range of temperatures (35–40°C), the velocity/temperature slope is about 1 m/s per 1°C.…”

Real-Time Ultrasound Thermography and Thermometry [Life Sciences]

“…The details of the derivation can be found in [2] for what is now called the infinitesimal echo-strain filter δ -ESF. More recently, [3] introduced a modified version that resulted in a recursive echo-strain filter (RESF), which includes the δ -ESF as a special case.…”

“…Setting z = z m and assuming α, β, & c are constants, we transform (6) into a constant-coefficient differential equation. Using τ = 2 z / c ⟹ ∂τ = 2 ∂z/c allows us to discretize the equation on the uniform RF sampling grid ${\left\{{\tau }_i=i{T}_s\right\}}_{i=1}^{N_s}$ to arrive at the recursive equation [3]: $$\delta \theta false({\tau }_{i}false)=a\cdot \delta \theta false({\tau }_{i-1}false)+b\cdot false(\delta \tau false({\tau }_{i}false)-\delta \tau false({\tau }_{i-1}false)false),$$with the filter coefficients given by: $$a=\frac{\alpha {\tau }_m}{\alpha {\tau }_m-T_{s}false(\alpha -\beta false)},\mathrm{italicand}b=-\frac{1}{\alpha {\tau }_m-T_{s}false(\alpha -\beta false)}$$where τ m = 2 z m /c 0 . The higher the value of z m the narrower the bandwidth of the lowpass filter defined by the recursive term.…”

“…The approach that received the most attention is based on the detection and estimation of minute temperature-induced echo shifts, which could be processed to generate the desired 2D maps of temperature change. The principle of measurement has been confirmed by a number of groups in tissue-mimicking phantoms, ex vivo models and recently in vivo small animal models [3]. Furthermore, GPU-enabled rtUST systems have been developed and demonstrated in pre-clinical settings.…”

“…The methods listed in Table I have been validated experimentally in various media, including in vivo [1], [3], [5]. As indicated in the table, all these methods exhibit some level of tissue dependence.…”