The road to clinical use of high-intensity focused ultrasound for liver cancer: technical and clinical consensus

Aubry, Jean‐François; Pauly, Kim Butts; Moonen, Chrit; Haar, Gail ter; Ries, Mario; Salomir, Rarès; Sokka, S.; Sekins, K. Michael; Shapira, Yeruham; Ye, Fangwei; Huff-Simonin, Heather; Eames, Matt; Hananel, Arik; Kassell, Neal F.; Napoli, Alessandro; Hwang, Joo Ha; Wu, Feng; Zhang, Lian; Melzer, Andreas; Kim, Youngsun; Gedroyc, Wladyslaw

doi:10.1186/2050-5736-1-13

Cited by 82 publications

(59 citation statements)

References 69 publications

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“…The inability to monitor lesion formation remains a limitation (Zhou, 2011). Currently, MRI thermometry and B-mode ultrasound are used clinically to predict lesion formation (Aubry et al, 2013). MRI thermometry can accurately and quantitatively determine the temperature rise from HIFU but necessitates the use of both an expensive MRI system and MRI-compatible HIFU arrays (Tempany et al, 2003; Chapman and ter Haar, 2007; Köhler et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Using Passive Cavitation Images to Classify High-Intensity Focused Ultrasound Lesions

Haworth

Salgaonkar

Corregan

et al. 2015

Ultrasound in Medicine & Biology

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Passive cavitation imaging provides spatially resolved monitoring of cavitation emissions. However the diffraction limit of a linear imaging array results in relatively poor range resolution. Poor range resolution has limited prior analyses of the spatial specificity and sensitivity of passive cavitation imaging for predicting thermal lesion formation. In this study, this limitation is overcome by orienting a linear array orthogonal to the HIFU propagation direction and performing passive imaging. Fourteen lesions were formed in ex vivo bovine liver samples as a result of 1.1 MHz continuous-wave ultrasound exposure. The lesions were classified as focal, “tadpole”, or pre-focal based on their shape and location. Passive cavitation images were beam-formed from emissions at the fundamental, harmonic, ultraharmonic, and inharmonic frequencies with an established algorithm. Using the area under a receiver operator characteristic curve (AUROC), fundamental, harmonic, and ultraharmonic emissions were shown to be significant predictors of lesion formation for all lesion types. For both harmonic and ultraharmonic emissions, pre-focal lesions were classified most successfully (AUROC values of 0.87 and 0.88, respectively), followed by tadpole lesions (AUROC values of 0.77 and 0.64, respectively), and focal lesions (AUROC values of 0.65 and 0.60, respectively).

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Section: Introductionmentioning

confidence: 99%

Using Passive Cavitation Images to Classify High-Intensity Focused Ultrasound Lesions

Haworth

Salgaonkar

Corregan

et al. 2015

Ultrasound in Medicine & Biology

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“…Applications range from the thermal treatment of benign and malignant lesions to targeted drug delivery up to the treatment of thromboses (sonothrombolysis), neurological disorders and much more [2]. Great challenges for the focusing of the ultrasound waves are posed by partial or full occlusion of the target site by bones or air-filled structures as well as target motion [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

An integrated model-based software for FUS in moving abdominal organs

Schwenke

Strehlow

Haase

et al. 2015

International Journal of Hyperthermia

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Focused ultrasound surgery (FUS) is a non-invasive method for tissue ablation that has the potential for complete and controlled local tumour destruction with minimal side effects. The treatment of abdominal organs such as the liver, however, requires particular technological support in order to enable a safe, efficient and effective treatment. As FUS is applied from outside the patient's body, suitable imaging methods, such as magnetic resonance imaging or diagnostic ultrasound, are needed to guide and track the procedure. To facilitate an efficient FUS procedure in the liver, the organ motion during breathing and the partial occlusion by the rib cage need to be taken into account in real time, demanding a continuous patient-specific adaptation of the treatment configuration. Modelling the patient's respiratory motion and combining this with tracking data improves the accuracy of motion predictions. Modelling and simulation of the FUS effects within the body allows the use of treatment planning and has the potential to be used within therapy to increase knowledge about the patient status. This article describes integrated model-based software for patient-specific modelling and prediction for FUS treatments of moving abdominal organs.

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“…17 A comprehensive description of these hurdles has been reviewed elsewhere. 18,19 One of the most significant limitations is the problems associated with the motion of abdominal organs.…”

Section: Introductionmentioning

confidence: 99%

Motion Correction Techniques for MR-Guided HIFU Ablation of Abdominal Organs

Roujol

Moonen

2014

Frontiers of Medical Imaging

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Magnetic resonance guided high intensity focused ultrasound (MRgHIFU) ablation is a promising technique for the treatment of hepatic and renal cancers. In this approach, the HIFU system is used for ablating the tumor using local heating while the MRI scanner provides online temperature measurements which enable continuous monitoring of the delivered thermal dose. The application of MRgHIFU ablation for abdominal organs is challenging. Many technical developments have addressed the major issues associated with this procedure such as the physiological motion of abdominal organs. In this chapter, we present the different techniques which have been developed to alleviate the problem of physiological motion for MRgHIFU ablation of abdominal organs.

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The road to clinical use of high-intensity focused ultrasound for liver cancer: technical and clinical consensus

Cited by 82 publications

References 69 publications

Using Passive Cavitation Images to Classify High-Intensity Focused Ultrasound Lesions

Using Passive Cavitation Images to Classify High-Intensity Focused Ultrasound Lesions

An integrated model-based software for FUS in moving abdominal organs

Motion Correction Techniques for MR-Guided HIFU Ablation of Abdominal Organs

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