Model-Based Ultrasound Temperature Visualization During and Following Hifu Exposure

Ye, Guoliang; Probert, Ian; Noble, J. Alison

doi:10.1016/j.ultrasmedbio.2009.10.001

Cited by 31 publications

(23 citation statements)

References 24 publications

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“…In the absence of large vessels, the parameter w is often neglected since no perfusion is expected to contribute to the heat evacuation [9]. A convenient solution of the BHTE can be found in the Fourier domain since the problem can be transformed into a linear differential equation as follows:…”

Section: Temperature Modeling Using the Bio Heat Transfer Equation (Bmentioning

confidence: 99%

“…The filter was essentially not designed for noise removal (since only four measurement points were available) but to estimate both blood perfusion and temperature information at unmeasured locations (spatial extrapolation). More recently, Ye et al [9] employed Kalman filtering for improved model-based ultrasound temperature visualization. The employed predictor model was based on the construction of isothermal ellipsoids around the heated area.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Extended Kalman filtering for MR-thermometry guided high intensity focused ultrasound using the bio heat transfer equation

Roujol¹,

Hey²,

Moonen³

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. However, the precision of real time MR-thermometry is generally limited by the available signal to noise ratio (SNR). In order to improve the efficiency of applications, which are based on online temperature measurements, temporal filtering can be employed. Here, we propose a novel digital filter combining extended Kalman filtering with a temperature predictive model based on the bio heat transfer equation. The proposed approach is evaluated on simulated datasets and on MR-guided HIFU ablation experiments on ex-vivo porcine muscle. The proposed filter showed a significantly improved precision and accuracy in comparison to an optimized FIR filter.

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Section: Temperature Modeling Using the Bio Heat Transfer Equation (Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended Kalman filtering for MR-thermometry guided high intensity focused ultrasound using the bio heat transfer equation

Roujol¹,

Hey²,

Moonen³

et al. 2011

2011 18th IEEE International Conference on Image Processing

View full text Add to dashboard Cite

show abstract

“…The filter was essentially not designed for noise removal (since only four measurement points were available) but to estimate both blood perfusion and temperature information at unmeasured locations (spatial extrapolation). More recently, Ye et al [11] employed Kalman filtering for improved model-based ultrasound temperature visualization. The employed predictor model was based on the construction of isothermal ellipsoids around the heated area.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Extended Kalman Filtering for Real Time MR-Thermometry Guided HIFU Interventions

Roujol

Senneville

Hey

et al. 2012

IEEE Trans. Med. Imaging

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Abstract-Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint.The requirement to resove both physiological motion on mobile organs and the rapid temperature variations induced by state-ofthe art high-power HIFU systems requires fast MRI-acquisition schemes, which are generally hampered by low signal to noise ratios (SNR). This directly limits the precision of real time MR-thermometry and thus in many cases the feasability of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data.Here, we propose a novel model based digital filter combining an extended Kalman filter (EKF) with a predictive model of the temperature based on the bio heat transfer equation. This filter aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy using the formalism of adaptive extended Kalman filtering. An additional outlier rejection addresses the problem of sparse artifacted temperature points. The filter was evaluated and compared to a matched FIR filter using simulated data, HIFU experiments on phantoms and in vivo data obtained during HIFU ablations on porcine kidneys. The filter provides improved artefact and noise reduction, while having a minimal impact on accuracy and latency.

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“…[1][2][3] The clinical use of FUS for hemostasis, 4 neurosurgery, 5,6 and the treatment of tumors located in various tissues, including the uterine fibroids, 7 brain, 8 liver, 9 kidney, 10 prostate, 11 and breast, 12 are now being investigated due to the development of the medical imaging techniques for targeting and monitoring FUS during recent years, such as magnetic resonance imaging (MRI) [13][14][15] and ultrasound (US) imaging. [16][17][18] Earlier, Hynynen et al 19 used MRI to detect the tissue necrosis induced by focused US beam. The temperature sensitivity of the proton resonance frequency (PRF) or the relaxation time T1 is used to monitor the temperature distribution.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of using Nakagami distribution in evaluating the formation of ultrasound-induced thermal lesions

Zhang

Zhou

Wan

et al. 2012

The Journal of the Acoustical Society of America

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The acoustic posterior shadowing effects of bubbles influence the accuracy for defining the location and range of ablated thermal lesions during focused ultrasound surgery when using ultrasonic monitoring imaging. This paper explored the feasibility of using Nakagami distribution to evaluate the ablated region induced by focused ultrasound exposures at different acoustic power levels in transparent tissue-mimicking phantoms. The mean value of the Nakagami parameter m was about 0.5 in the cavitation region and increased to around 1 in the ablated region. Nakagami images were not subject to significant shadowing effects of bubbles. Ultrasound-induced thermal lesions observed in the photos and Nakagami images were overshadowed by bubbles in the B-mode images. The lesion size predicted in the Nakagami images was smaller than that predicted in the photos due to the sub resolvable effect of Nakagami imaging at the interface. This preliminary study on tissue-mimicking phantom suggested that the Nakagami parameter m may have the potential use in evaluating the formation of ultrasound-induced thermal lesion when the shadowing effect of bubbles is strong while the thermal lesion was small. Further studies in vivo and in vitro will be needed to evaluate the potential application.

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Model-Based Ultrasound Temperature Visualization During and Following Hifu Exposure

Cited by 31 publications

References 24 publications

Extended Kalman filtering for MR-thermometry guided high intensity focused ultrasound using the bio heat transfer equation

Extended Kalman filtering for MR-thermometry guided high intensity focused ultrasound using the bio heat transfer equation

Robust Adaptive Extended Kalman Filtering for Real Time MR-Thermometry Guided HIFU Interventions

Feasibility of using Nakagami distribution in evaluating the formation of ultrasound-induced thermal lesions

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