Combining active cavitation detection with B-mode Images to improve the automatic spatial localization of hyperechoic regions Chang-yu Hsieh 1, Penny Probert Smith 1, Tom Leslie 2, James Kennedy 2, Fares Mayia 3 and Gouliang Ye 1 1 Wolfson Medical Vision Laboratory, Department of Engineering Science, University of Oxford 2 The HIFU Unit, Churchill Hospital, Oxford Radcliffe Health Trust, Oxford 3 Department of Medical Physics Churchill Hospital, Headington, Oxford Introduction In some HIFU therapy protocols, the evidence of cavitation is taken as a strong indicator of tissue lesions. Cavitation is associated with hyperechoic regions (‘bright up’) in the image. The use of ultrasound (US) visualization for the guidance and monitoring of HIFU therapies most often relies on the appearance of a bright hyper- echoic region in the US B-mode image. Therefore reliable detection and the availability of a history of the occurrence and properties of cavitation is important for monitoring treatment. An underlying probabilistic method has been presented previously which automatically identifies hyperechoic regions spatially and temporally. However a number of difficulties are present in identifying cavitation regions. Variation in attenuation from bubble formation. Uncertainty near bright boundaries. Hyperechoic regions may appear for reasons other than cavitation – e.g. tissue interfaces Figure 1: B-mode image taken from HIFU liver experiment. The hyperechoic region indicated suggests cavitation. Other hyperechoic regions (known not be caused by cavitation) are shown too. Aim We introduce an active method of cavitation detection which analyses the spectral response of the r.f. echo signals underlying a B-mode image.. The technique attains higher spatial resolution than the conventional passive cavitation detection (hydrophone), and operates over a larger region. However the bandwidth is reduced because of the availability of medical transducers. The sensitivity of these two techniques to cavitation will be discussed, together with the advantages of combining the two methods to automatically delineate and monitor cavitation regions. Method Acoustic power was applied to a sample of pig liver using a HIFU transducer at around 1.1MHz until a hyperechoic region was observed. The HIFU power was switched off and r.f. (A-line) data was captured using an Analogic CasaEngine and a BK-Medical linear array transducer (bandwidth from 5-12MHz). Two methods were investigated to analyse the spectral content of the signal in each A-line set, aiming for a spatial resolution similar to a single pixel (around 8 samples). Two methods were investigated to find the spectrum: The Windowed FFT, the most common method to evaluate spectra although known to deteriorate fast in precision with few samples. The Autoregressive Moving Average (ARMA), a statistical model based method which maintains high spatial resolution through assumptions on the signal statistics. Fast Fourier Transform A clear shift of spectral energy to higher frequencies is seen when cavitation is observed in the corresponding B-mode image (Fig 1).However because the transducer operates over a bandwidth of less than an octave much of the energy from the spectral activity of bubbles is lost. Figure 2: Frequency response from Sample line 1, 35, 62, 100, 120 and 123 of row 190 – row 230 in Fig1. Image Transducer is operating around 7MHz centre frequency with sampling frequency 40MHz. Problems in using the FFT Cannot achieve suitable spatial resolution. Does not incorporate any prior knowledge. Autoregressive Moving Average (ARMA) Model Commonly used in signal processing to find spectra from small samples Models stochastic signals as time series data, predicting future values as sum of deterministic value and random process (noise) Assumes process to be stationary Requires assumptions on the order of the model Details of Model : Parameters of AR and MA model : Innovation (error term) is a white noise process with zero mean & variance Base on. Parameters may be calculated using Yule-Walker equation (yielding p+1 equations) and solve it as a matrix. Choice of model order p and q: AR order can be determined from partial autocorrelation function whereas MA order is estimated in autocorrelation function. Simulation: The spatial accuracy depends on the order of the model, which is chosen using intensity of the B-mode image (auto detection between 5 and 8). References 1.Chen J, Vandewalle J, Sansen W, Vantrappen G and Janssens J, ” Adaptive spectral analysis of cutaneous electrogastric signals using autoregressive moving average modelling”, Medical and Biological Engineering and Computing, vol 28, no.6, Nov Larouk A, Sennaoui A, Kehli A, Pautou C and Boynard M, ” Application of autoregressive moving average spectral analysis for red blood cell sedimentation rate estimation by an ultrasonic interferometry method”, 18th Annual International Conference of the IEEE, vol 3, Nov With thanks to Constantin Couusios for his help in setting up the HIFU system Results Spatial resolution in the spectrum can be achieved about 1mm in (i.e. one pixel of the B-mode image) Figure 3: Energy colour map of high frequency (above 10 MHz) emission, corresponding to the image in fig 1. The algorithm has distinguished between the hyperechoic regions in the liver with and without cavitation. Energy distribution is from strong red to yellow in cavitation region. Figure 4: Magnified B-mode with corresponding energy colour map Figure 5: Image taken at 1s with corresponding energy colour map Figure 6: Image taken at 2s with corresponding energy colour map Figure 7: Image taken at 3s with corresponding energy colour map Figure 8: Normalized energy map over 10MHz, at time 0ssecs.A clear speckle effect has been removed and only high frequency components left. MCR : 8.3 ％ MCR : 7.1 ％ Conclusion and Further Work This work investigates the possibility of providing reliable energy colour map from spectral analysis to validate the existence of cavitation in a resolution of 1mm/pixel. The results are encouraging, although limited by the transducer bandwidth. Small hyperechoic regions not associated with cavitation have been removed although the boundary region persists. Resolution of about 1 pixel has been achieved using the ARMA method. The results will be integrated with intensity analysis from the B-mode image derived from the same data, which uses a statistical model (Hidden Markov Random Field – Expectation Maximisation algorithm) to segment the hyperechoic regions from intensity alone..