Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

King, Andrew P.; Buerger, Christian; Tsoumpas, Charalampos; Marsden, Paul; Schaeffter, Tobias

doi:10.1016/j.media.2011.08.003

Cited by 114 publications

(112 citation statements)

References 30 publications

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“…In the case of nonrigid respiratory motion, the use of 2-dimensional (2D) and 3-dimensional (3D) MR sequences for nonrigid motion compensation has been proposed and evaluated using simulated PET data (17,18), phantom studies (19,20), and rabbits and primates (21). To our knowledge, the only study for respiratory motion correction in clinical PET/ MR imaging using patient datasets was described by Wurslin et al (22).…”

mentioning

confidence: 99%

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

Fayad

Schmidt²,

Wuerslin³

et al. 2015

J Nucl Med

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Simultaneous PET and MR imaging is a promising new technique allowing the fusion of functional (PET) and anatomic/functional (MR) information. In the thoracic-abdominal regions, respiratory motion is a major challenge leading to reduced quantitative and qualitative image accuracy. Correction methodologies include the use of gated frames that lead to low signal-to-noise ratio considering the associated low statistics. More advanced correction approaches, previously developed for PET/CT imaging, consist of either registering all the reconstructed gated frames to the reference frame or incorporating motion parameters into the iterative reconstruction process to produce a single motioncompensated PET image. The goal of this work was to compare these two-previously implemented in PET/CT-correction approaches within the context of PET/MR motion correction for oncology applications using clinical 4-dimensional PET/MR acquisitions. Two different correction approaches were evaluated comparing the incorporation of elastic transformations extracted from 4-dimensional MR imaging datasets during PET list-mode image reconstruction to a postreconstruction imagebased approach. Methods: Eleven patient datasets acquired on a PET/MR system were used. T1-weighted 4D MR images were registered to the end-expiration image using a nonrigid B-spline registration algorithm to derive deformation matrices accounting for respiratory motion. The derived matrices were subsequently incorporated within a PET image reconstruction of the original emission list-mode data (reconstruction space [RS] method). The corrected images were compared with those produced by applying the deformation matrices in the image space (IS method) followed by summing the realigned gated frames, as well as with uncorrected motion-averaged images. Results: Both correction techniques led to significant improvement in accounting for respiratory motion artifacts when compared with uncorrected motionaveraged images. These improvements included signal-to-noise ratio (mean increase of 28.0% and 24.2% for the RS and IS methods, respectively), lesion size (reduction of 60.4% and 47.9%, respectively), lesion contrast (increase of 70.1% and 57.2%, respectively), and lesion position (changes of 60.9% and 46.7%, respectively). Conclusion: Our results demonstrate significant respiratory motion compensation using both methods, with superior results from a 4D PET RS approach.

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confidence: 99%

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

Fayad

Schmidt²,

Wuerslin³

et al. 2015

J Nucl Med

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“…From the methodological point of view, the proposed strategy ensures a reduced sensitivity to breathing pattern changes during the acquisition, if compared to conventional techniques for motion compensation in 4D PET. Although the robustness of the virtual 4D PET against breathing irregularities was not specifically investigated (26), the influence of breathing pattern changes is intrinsically limited in this strategy. A future evaluation of the increased robustness of the virtual 4D PET against breathing irregularities would require to consider 4D attenuation and 4D scatter corrections, since the PET activity distribution (i.e., the 4D emission map) is modified according to information coming from the CT image (i.e., the 4D attenuation map) (27).…”

Section: Discussionmentioning

confidence: 99%

Optimized PET Imaging for 4D Treatment Planning in Radiotherapy: the Virtual 4D PET Strategy

Gianoli

Riboldi

Fontana

et al. 2014

Technol Cancer Res Treat

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The purpose of the study is to evaluate the performance of a novel strategy, referred to as "virtual 4D PET", aiming at the optimization of hybrid 4D CT-PET scan for radiotherapy treatment planning. The virtual 4D PET strategy applies 4D CT motion modeling to avoid time-resolved PET image acquisition. This leads to a reduction of radioactive tracer administered to the patient and to a total acquisition time comparable to free-breathing PET studies.The proposed method exploits a motion model derived from 4D CT, which is applied to the free-breathing PET to recover respiratory motion and motion blur. The free-breathing PET is warped according to the motion model, in order to generate the virtual 4D PET. The virtual 4D PET strategy was tested on images obtained from a 4D computational anthropomorphic phantom. The performance was compared to conventional motion compensated 4D PET.Tests were also carried out on clinical 4D CT-PET scans coming from seven lung and liver cancer patients. The virtual 4D PET strategy was able to recover lesion motion, with comparable performance with respect to the motion compensated 4D PET. The compensation of the activity blurring due to motion was successfully achieved in terms of spill out removal.Specific limitations were highlighted in terms of partial volume compensation. Results on clinical 4D CT-PET scans confirmed the efficacy in 4D PET count statistics optimization, as equal to the free-breathing PET, and recovery of lesion motion. Compared to conventional motion compensation strategies that explicitly require 4D PET imaging, the virtual 4D PET strategy reduces clinical workload and computational costs, resulting in significant advantages for radiotherapy treatment planning.

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“…One source of surrogate data is external respiratory monitors, such as RPM (Real-time Position Management, Varian Medical Systems Inc.), pressure belt, or spirometer. Some work uses MR-derived measurements as the surrogate, by building a correspondence model between 3D MR-based deformations and either interleaved 2D navigator images (King et al 2012), or a measure derived directly from the dynamic MR images (Balfour et al 2015). In these cases, other MR sequences need to be altered to allow for a continuous acquisition of the surrogate data.…”

Section: Introductionmentioning

confidence: 99%

Joint PET-MR respiratory motion models for clinical PET motion correction

et al. 2016

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Patient motion due to respiration can lead to artefacts and blurring in positron emission tomography (PET) images, in addition to quantification errors. The integration of PET with magnetic resonance (MR) imaging in PET-MR scanners provides complementary clinical information, and allows the use of high spatial resolution and high contrast MR images to monitor and correct motion-corrupted PET data. In this paper we build on previous work to form a methodology for respiratory motion correction of PET data, and show it can improve PET image quality whilst having minimal impact on clinical PET-MR protocols.We introduce a joint PET-MR motion model, using only 1 min per PET bed position of simultaneously acquired PET and MR data to provide a respiratory motion correspondence model that captures inter-cycle and intracycle breathing variations. In the model setup, 2D multi-slice MR provides the dynamic imaging component, and PET data, via low spatial resolution framing and principal component analysis, provides the model surrogate.We evaluate different motion models (1D and 2D linear, and 1D and 2D polynomial) by computing model-fit and model-prediction errors on dynamic Institute of Physics and Engineering in MedicineOriginal content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. We demonstrate the capability of a joint PET-MR motion model to predict respiratory motion by showing significantly improved image quality of PET data acquired before the motion model data. The method can be used to incorporate motion into the reconstruction of any length of PET acquisition, with only 1 min of extra scan time, and with no external hardware required.

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Thoracic respiratory motion estimation from MRI using a statistical model and a 2-D image navigator

Cited by 114 publications

References 30 publications

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

Reconstruction-Incorporated Respiratory Motion Correction in Clinical Simultaneous PET/MR Imaging for Oncology Applications

Optimized PET Imaging for 4D Treatment Planning in Radiotherapy: the Virtual 4D PET Strategy

Joint PET-MR respiratory motion models for clinical PET motion correction

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