Robust motion correction for respiratory gated PET/CT using weighted averaging

Thielemans, Kris; Gopalakrishnan, Girish; Roy, Arunabha; Srikrishnan, V.; Thiruvenkadam, Sheshadri; Wollenweber, Scott D.; Manjeshwar, Ravindra

doi:10.1109/nssmic.2011.6152529

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, we show example results after motion correction. Briefly, the Reconstruct, Reg ister, weighted Average (RRwA) method [36], [37] registers the reconstructed gated images to a target gate and combines the 5 resulting images using weighted averaging. The weights take the total counts in each gate into account.…”

Section: B Pet Resultsmentioning

confidence: 99%

Device-less gating for PET/CT using PCA

Thielemans¹,

Rathore²,

Engbrant³

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

Self Cite

106

118

View full text Add to dashboard Cite

Movement degrades image quality in PET/CT. The first step in correcting for movement is to gate the data into dif ferent motion states. The gating is usually based on information from external devices, such as the chest position for respiratory movement, or an ECG signal for cardiac gating. Various groups have proposed methods to extract a gating signal out of the PET and/or CT data. Most of these methods are slow or require prior information (and associated tuning of parameters). Here we propose and evaluate a method that uses a well-known technique for data analysis called Principal Component Analysis (PCA). We test the method on clinical PET list mode data and CINE CT images to extract a gating signal. We show good correlation with the chest position as measured by the Varian RPM system. Total processing time for PET data is less than half a minute of which most is 10 related. I. IN TRODUCTIONIn many cases in medical imaging, motion is unavoid able. For example, in diagnostic PET, acquisition duration is currently roughly 2 minutes per bed position. Respiratory motion in patients during PET acquisition leads to blurring in the resulting (static) PET images. This may in turn lead to lower detectability of tumours, inaccurate SUV calculation, and incorrect tum or planning volumes in radiation therapy [1], [2], [3], [4]. The first step towards reducing the amount of motion in the images normally involves the use of respiratory gating [5] which results in a 4-D data set, in which multiple gates containing data from the various respiratory states, are usually individually reconstructed. This is then potentially followed by motion correction.In other applications, the different motion states are of interest, for instance in cardiac studies to find the ejection fraction [6] and/or wall motion [7], or in radio-therapy [8].The gating is usually based on information from exter nal devices, such as a spyrometer or the chest position for respiratory movement, or an ECG signal for cardiac gating. Due to the extra cost and patient management associated to the additional device, but also because of some evidence of hysteresis between the internal movement and external device [9], [10], [11], various groups have proposed methods to extract a gating signal out of the PET, SPECT and/or CT data. Many groups compute the centre-of-mass (COM) in an ROI and use this as an indicator of motion, mostly of respiration, for instance in PET [12], [13] and cardiac SPECT [14]. Filtering allows separation of a respiratory and cardiac signal in cardiac PET [15] and cone-beam CINECT [16]. These methods need a high contrast region that can be tracked over time.For PET data, Visvikis et at.[17] placed an ROI over edges of boundaries (using non-attenuation corrected images) and studied the Time Activity Curve (TACs). A characteristic frequency was derived via the Fourier Transform (FT) which then allowed finding amplitude and phase images. This method worked well on phantom data with periodic movement but was not evaluated for patients. Schleyer ...

show abstract

Section: B Pet Resultsmentioning

confidence: 99%

Device-less gating for PET/CT using PCA

Thielemans¹,

Rathore²,

Engbrant³

et al. 2011

2011 IEEE Nuclear Science Symposium Conference Record

Self Cite

106

118

View full text Add to dashboard Cite

show abstract

“…used a global factor per gate based on mutual-information metrics. Thielemans, Gopalakrishnan, Roy, Srikrishnan, Thiruvenkadam, Wollenweber & Manjeshwar (2011) used local weights based on the deformation fields. The first version of Q.Freeze, the commercial product of GE Healthcare, used a median of the registered images as opposed to a sum.…”

Section: Post-reconstruction Registrationmentioning

confidence: 99%

PET respiratory motion correction: quo vadis?

Lamare¹,

Bousse²,

Thielemans³

et al. 2022

Phys. Med. Biol.

View full text Add to dashboard Cite

Positron emission tomography (PET) respiratory motion correction has been a subject of great interest for the last twenty years, prompted mainly by the development of multimodality imaging devices such as PET/computed tomography (CT) and PET/magnetic resonance imaging (MRI). PET respiratory motion correction involves a number of steps including acquisition synchronization, motion estimation and finally motion correction. The synchronization steps include the use of different external device systems or data driven approaches which have been gaining ground over the last few years. Patient specific or generic motion models using the respiratory synchronized datasets can be subsequently derived and used for correction either in the image space or within the image reconstruction process. Similar overall approaches can be considered and have been proposed for both PET/CT and PET/MRI devices. Certain variations in the case of PET/MRI include the use of MRI specific sequences for the registration of respiratory motion information. The proposed review includes a comprehensive coverage of all these areas of development in field of PET respiratory motion for different multimodality imaging devices and approaches in terms of synchronization, estimation and subsequent motion correction. Finally, a section on perspectives including the potential clinical usage of these approaches is included.

show abstract

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

Lu,

Kang,

Zhang

et al. 2024

Eur J Nucl Med Mol Imaging

View full text Add to dashboard Cite

Purpose Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all the major issues caused by RM, i.e., PET resolution loss, attenuation correction artifacts, and PET-CT misalignment. Methods In a retrospective study, 737 patients underwent [18F]FDG PET/CT scans using the uMI Panorama PET/CT scanner. Ninety-nine patients, who also had respiration monitoring device (VSM), formed the validation set. The remaining data of the 638 patients were used to train neural networks used in the uRMC. The uRMC primarily consists of three key components: (1) data-driven respiratory signal extraction, (2) attenuation map generation, and (3) PET-CT alignment. SUV metrics were calculated within 906 lesions for three approaches, i.e., data-driven uRMC (proposed), VSM-based uRMC, and OSEM without motion correction (NMC). RM magnitude of major organs were estimated. Results uRMC enhanced diagnostic capabilities by revealing previously undetected lesions, sharpening lesion contours, increasing SUV values, and improving PET-CT alignment. Compared to NMC, uRMC showed increases of 10% and 17% in SUVmax and SUVmean across 906 lesions. Sub-group analysis showed significant SUV increases in small and medium-sized lesions with uRMC. Minor differences were found between VSM-based and data-driven uRMC methods, with the SUVmax was found statistically marginal significant or insignificant between the two methods. The study observed varied motion amplitudes in major organs, typically ranging from 10 to 20 mm. Conclusion A data-driven solution for respiratory motion in PET/CT has been developed, validated and evaluated. To the best of our knowledge, this is the first unified solution that compensates for the motion blur within PET, the attenuation mismatch artifacts caused by PET-CT misalignment, and the misalignment between PET and CT.

show abstract

Robust motion correction for respiratory gated PET/CT using weighted averaging

Cited by 3 publications

References 12 publications

Device-less gating for PET/CT using PCA

Device-less gating for PET/CT using PCA

PET respiratory motion correction: quo vadis?

Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment

Contact Info

Product

Resources

About