Joint cardiac and respiratory motion correction and super-resolution reconstruction in coronary PET/CT

Ambwani, Sonal; Karl, W.C.; Tawakol, Ahmed; Pien, Homer

doi:10.1109/isbi.2011.5872732

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…RTA techniques can also incorporate nonrigid transformations. Motion models have been proposed to provide the motion estimates for MCIR (Ambwani et al, 2011;Fayad et al, 2010;Reyes et al, 2007) and RTA King et al, 2012), but to the authors' knowledge, no motion model based technique has been proposed using LOR correction. In addition, a number of motion model techniques have been proposed for use in PET imaging without being specific as to which motion correction approach they are intended for (McQuaid et al, 2009;McQuaid and Lambrou, 2011;Rahni et al, 2011).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Therefore this is not strictly a motion model as defined in Section 1.1. One approach has been to tackle the problem of noisy data by using a motion model to constrain registrations between different respiratory-gated PET images (Ambwani et al, 2011;Klein et al, 2001).…”

Section: Acquiring Motion Datamentioning

confidence: 99%

“…CBCT projections) With implanted markers: Cho et al, 2010 Without implanted markers: Martin et al, 2012 Two views, fixed With implanted markers: Schweikard et al, 2000;Schweikard et al, 2004a;Berbeco et al, 2005;Ionascu et al, 2007;Seppenwoolde et al, 2007;Cho et al, 2008;Ruan et al, 2008;Hoogeman et al, 2009;Cho et al, 2010;Torshabi et al, 2010;Cho et al, 2011 Without implanted markers: Schweikard et al, 2004b;Shechter et al, 2004;Schweikard et al, 2005;Shechter et al, 2005;Shechter et al, 2006;Schneider et al, 2010PET/SPECT Klein et al, 2001Ambwani et al, 2011US Atkinson et al, 2001Ernst et al, 2011Miscellaneous Electromagnetic tracking Timinger et al, 2004 From 4-D XCAT phantom Rahni et al, 2011 and type of surrogate signals used, as well as the choice of correspondence model. For indirect correspondence models (see Section 5.3.2) the internal variables that parameterise the model determine what type of motion and variation can be modelled.…”

Section: Input Signal(s)mentioning

confidence: 99%

“…One way in which this has been done is to maintain a separate transformation for each image, but to include a temporal smoothness cost term which penalises transformations that do not change smoothly in time (Ambwani et al, 2011;Blume et al, 2010;Klein et al, 2001). Another way is to extend the transformation model to the temporal dimension, i.e.…”

Section: Motion Datamentioning

confidence: 99%

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Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

348

343

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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: Image Acquisitionmentioning

confidence: 99%

Section: Acquiring Motion Datamentioning

confidence: 99%

Section: Input Signal(s)mentioning

confidence: 99%

Section: Motion Datamentioning

confidence: 99%

See 2 more Smart Citations

Respiratory motion models: A review

McClelland

Hawkes

Schaeffter

et al. 2013

Medical Image Analysis

348

343

View full text Add to dashboard Cite

show abstract

“…Thus, PET quantification is im paired and lesions in the resulting PET image are blurred and 978-1-4673-6997-8/15/$31.00 ©2015 IEEE 788 sometimes no longer visible [I]. In the past, motion correc tion of PET data was realized using CT [2] or PET data itself [3]. The emerge of simultaneous PET/MR system offer new possibilities to correct the motion-distorted PET data again by an MR-derived motion model which is applied to deform the PET images [4,5,6].…”

Section: Introductionmentioning

confidence: 99%

Combining Compressed Sensing with motion correction in acquisition and reconstruction for PET/MR

Küstner

Würslin

Schmidt

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In the field of oncology, simultaneous Positron-Emission Tomography/Magnetic Resonance (PET/MR) scanners offer a great potential for improving diagnostic accuracy. How ever, to achieve a high Signal-to-Noise Ratio (SNR) for an accurate lesion detection and quantification in the PET/MR images, one has to overcome the induced respiratory motion artifacts. The simultaneous acquisition allows performing a MR-based non-rigid motion correction of the PET data. It is essential to acquire a 4D (3D + time) motion model as accurate and fast as possible to minimize additional MR scan time overhead. Therefore, a Compressed Sensing (CS) acqui sition by means of a variable-density Gaussian subsampling is employed to achieve high accelerations. Reformulating the sparse reconstruction as a combination of the inverse CS problem with a non-rigid motion correction improves the ac curacy by alternately projecting the reconstruction results on either the motion-compensated CS reconstruction or on the motion model optimization. In-vivo patient data substantiates the diagnostic improvement.

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PET/CT Imaging of Inflammation and Calcification

Emami

Tawakol

2014

Cardiovascular Imaging

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Joint cardiac and respiratory motion correction and super-resolution reconstruction in coronary PET/CT

Cited by 8 publications

References 10 publications

Respiratory motion models: A review

Respiratory motion models: A review

Combining Compressed Sensing with motion correction in acquisition and reconstruction for PET/MR

PET/CT Imaging of Inflammation and Calcification

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