Effects of Respiratory Liver Motion on Heating for Gated and Model-Based Motion-Compensated High-Intensity Focused Ultrasound Ablation

Rijkhorst, Erik-Jan; Rivens, Ian; Haar, Gail ter; Hawkes, David J.; Barratt, Dean C.

doi:10.1007/978-3-642-23623-5_76

Cited by 17 publications

(21 citation statements)

References 11 publications

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“…A key work here was that of Blackall et al (2005), who built on the earlier preliminary work in Blackall et al (2001) and King et al (2001) to propose a system based on a MR derived motion model and intraprocedure US images. More recent work has been carried out by Rijkhorst et al (2010) and Rijkhorst et al (2011).…”

Section: Category Application Examplesmentioning

confidence: 98%

“…This section discusses sources of measurements of the true respiratory motion that are used, typically together with the surrogate data, to determine the correspondence model. Section 5.2 Wang et al, 1995;Blackall et al, 2001;King et al, 2001;Manke et al, 2002b;McLeish et al, 2002;Blackall et al, 2005;Blackall et al, 2006;Reyes et al, 2007 Respiratory gated images Manke et al, 2002a;Ablitt et al, 2004;Wu et al, 2006;Buerger et al, 2012;Preiswerk et al, 2012 Dynamic images Manke et al, 2003;Khamene et al, 2004;Koch et al, 2004;Liu et al, 2004;Sundaram et al, 2004;Jahnke et al, 2005;Nehrke and Bornert, 2005;Plathow et al, 2005;Blackall et al, 2006;Fischer et al, 2006;Jahnke et al, 2007;Sharif and Bresler, 2007;Gao et al, 2008;King et al, 2008a;King et al, 2008b;King et al, 2009a;King et al, 2009b;White et al, 2009;King et al, 2010b;King et al, 2010c;Rijkhorst et al, 2010;King et al, 2011;McGlashan and King, 2011;Rijkhorst et al, 2011;Savill et al, 2011;King et al, 2012;Peressutti et al, 2012…”

Section: Acquiring Motion Datamentioning

confidence: 98%

“…al., 2010;Cho et al, 2010;King et al, 2010b;King et al, 2010c;King et al, 2010a;Fayad et al, 2011;Geneser et al, 2011;Rijkhorst et al, 2011;Buerger et al, 2012 Respiratory phase Shechter et al, 2004McClelland et al, 2005;Shechter et al, 2005;McClelland et al, 2006;Shechter et al, 2006;McClelland et al, 2007;Colgan et al, 2008;Rit et al, 2009;Rijkhorst et al, 2010;McClelland et al, 2011 Single signal and binary signal to distinguish inhalation and exhalationBlackall et al, 2006;Seppenwoolde et al, 2007;King et al, 2008a;Hoogeman et al, 2009;King et al, 2009a;King et al, 2009b;Ernst et al, 2011;King et al, 2011;McClelland et al, 2011;McGlashan and King, 2011;Savill et al, 2011 Single signal + precursor Manke et al, 2003Nehrke and Bornert, 2005;Fischer et al, 2006;Zhang et al, 2007;Ruan et al, 2008;Zhang et al, 2010;Cho et al, 2011 Multiple precursors: Isaksson et al, 2005Cervino et al, 2009; al., 2010 Single signal + gradient Low et al, 2005; Odille et al, 2008b; Yang et al, 2008; Zhao et al, 2009; Low et al, 2010; McClelland et al, 2011; Martin et al, 2012 Single signal + amplitude King et al, 2009b Single signal + respiratory phase Fayad et al, 2009c Multiple signalsManke et al, 2003;…”

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confidence: 97%

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Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

333

334

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The problem of respiratory motion has proved a serious obstacle in developing techniques to acquire images or guide interventions in abdominal and thoracic organs. Motion models offer a possible solution to these problems, and as a result the field of respiratory motion modelling has become an active one over the past 15 years. A motion model can be defined as a process that takes some surrogate data as input and produces a motion estimate as output. Many techniques have been proposed in the literature, differing in the data used to form the models, the type of model employed, how this model is computed, the type of surrogate data used as input to the model in order to make motion estimates and what form this output should take. In addition, a wide range of different application areas have been proposed. In this paper we summarise the state of the art in this important field and in the process highlight the key papers that have driven its advance. The intention is that this will serve as a timely review and comparison of the different techniques proposed to date and as a basis to inform future research in this area.

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Section: Category Application Examplesmentioning

confidence: 98%

Section: Acquiring Motion Datamentioning

confidence: 98%

mentioning

confidence: 97%

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Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

333

334

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show abstract

“…Some other published methods accounting for abdominal motion make use of the model of the motion for the abdominal organs [5] or image-based algorithms based on correspondence between features [6]. The most ideal method would be to track the temporal changes of the treatment target during treatment.…”

Section: Introductionmentioning

confidence: 99%

Validation of a non-rigid registration method for motion compensation in 4D ultrasound of the liver

Vijayan

Klein

Hofstad

et al. 2013

2013 IEEE 10th International Symposium on Biomedical Imaging

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Future therapy using focused ultrasound (FUS) to treat tumors in abdominal organs, such as the liver, must incorporate motion tracking of these organs due to breathing and drift caused by gravity and intestines (peristalsis). Motion tracking of the target (e.g. tumor) is needed to ensure accurately located sonications. We have performed a quantitative validation of a methodology for motion tracking of the liver with 4D (3D+time) ultrasound. The offline analysis was done using a recently published non-rigid registration algorithm that was specifically designed for motion estimation from dynamic imaging data. The method registers the entire 4D sequence in a group-wise optimization fashion, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are enforced by using a 4D free-form B-spline deformation model. For our evaluation, three healthy volunteers were scanned over several breath cycles from three different positions and angles on the abdomen (totally nine 4D scans). A skilled physician performed the scanning and manually annotated well-defined anatomic landmarks for assessment of the automatic algorithm. Four engineers each annotated these points in all time frames, the mean of which was taken as a gold standard. The error of the automatic motion estimation method was compared with inter-observer variability. The registration method estimated liver motion better than the observers and had an error (75% percentile over all datasets) of 1 mm. We conclude that the methodology was able to accurately track the motion of the liver in the 4D ultrasound data.

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“…For instance, it may be individually used for image acquisition to capture the respiration-induced moving liver [3], respiratory gating for liver ablation interventions [4,5] and radiation therapies [6], and accuracy improvement of quantitative evaluation of the hepatic perfusion [7]. In addition, the respiratory signal can also be combined with a 4D respiratory motion model for motion correction or prediction during radiation therapies [8,9] and high intensity focused ultrasound (HIFU) [10,11]. For this case, the signal is firstly used in the pre-operative procedure for establishing a correspondence model with 3D motion of the whole liver motion or specific liver part (e.g.…”

Section: Introductionmentioning

confidence: 99%