Data-Driven Methods for the Determination of Anterior-Posterior Motion in PET

Heß, Mirco; Büther, Florian; Schäfers, Klaus P.

doi:10.1109/tmi.2016.2611022

Cited by 7 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. Reduce (image reconstruction) processing effort by replacing the time consuming 'real' spatial (back-) projection of each coincidence event by a simplified back-positioning procedure similar to Hess et al (2017). 2.…”

Section: Extraction Of the Respiratory Signalmentioning

confidence: 99%

Robust real-time extraction of respiratory signals from PET list-mode data

et al. 2018

View full text Add to dashboard Cite

Respiratory motion, which typically cannot simply be suspended during PET image acquisition, affects lesions' detection and quantitative accuracy inside or in close vicinity to the lungs. Some motion compensation techniques address this issue via pre-sorting ('binning') of the acquired PET data into a set of temporal gates, where each gate is assumed to be minimally affected by respiratory motion. Tracking respiratory motion is typically realized using dedicated hardware (e.g. using respiratory belts and digital cameras). Extracting respiratory signals directly from the acquired PET data simplifies the clinical workflow as it avoids handling additional signal measurement equipment. We introduce a new data-driven method 'combined local motion detection' (CLMD). It uses the time-of-flight (TOF) information provided by state-of-the-art PET scanners in order to enable real-time respiratory signal extraction without additional hardware resources. CLMD applies center-of-mass detection in overlapping regions based on simple back-positioned TOF event sets acquired in short time frames. Following a signal filtering and quality-based pre-selection step, the remaining extracted individual position information over time is then combined to generate a global respiratory signal. The method is evaluated using seven measured FDG studies from single and multiple scan positions of the thorax region, and it is compared to other software-based methods regarding quantitative accuracy and statistical noise stability. Correlation coefficients around 90% between the reference and the extracted signal have been found for those PET scans where motion affected features such as tumors or hot regions were present in the PET field-of-view. For PET scans with a quarter of typically applied radiotracer doses, the CLMD method still provides similar high correlation coefficients which indicates its robustness to noise. Each CLMD processing needed less than 0.4 s in total on a standard multi-core CPU and thus provides a robust and accurate approach enabling real-time processing capabilities using standard PC hardware.

show abstract

Section: Extraction Of the Respiratory Signalmentioning

confidence: 99%

Robust real-time extraction of respiratory signals from PET list-mode data

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Ultrasound scanners can also be used to track internal motion, as tested in an MRI environment (Günther and Feinberg 2004, Petrusca et al 2013, Kording et al 2015; however, long cables and difficulties positioning the ultrasound probe on the patient's body inside the bore of a scanner can increase the complexity of the clinical workflow. Alternatively, self-gating or 'data-driven' approaches based on tracking changes in the PET counts over time within a given region of interest are available (Kesner et al 2014, Ren et al 2017, Schleyer et al 2011, Hess et al 2017, Liu et al 2011. However, data-driven PET methods function only when the patient happens to lie within a PET scanner and would not be relevant for multi-modality purposes.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound-based sensors for respiratory motion assessment in multimodality PET imaging

Madore¹,

Belsley²,

Cheng³

et al. 2022

Phys. Med. Biol.

View full text Add to dashboard Cite

Breathing motion can displace internal organs by up to several cm; as such, it is a primary factor limiting image quality in medical imaging. Motion can also complicate matters when trying to fuse images from different modalities, acquired at different locations and/or on different days. Currently available devices for monitoring breathing motion often do so indirectly, by detecting changes in the outline of the torso rather than the internal motion itself, and these devices are often fixed to floors, ceilings or walls, and thus cannot accompany patients from one location to another. We have developed small ultrasound-based sensors, referred to as ‘organ configuration motion’ (OCM) sensors, that attach to the skin and provide rich motion-sensitive information. In the present work we tested the ability of OCM sensors to enable respiratory gating during in vivo PET imaging. A motion phantom involving an FDG solution was assembled, and two cancer patients scheduled for a clinical PET/CT exam were recruited for this study. OCM signals were used to help reconstruct phantom and in vivo data into time series of motion-resolved images. As expected, the motion-resolved images captured the underlying motion. In Patient #1, a single large lesion proved to be mostly stationary through the breathing cycle. However, in Patient #2, several small lesions were mobile during breathing, and our proposed new approach captured their breathing-related displacements. In summary, a relatively inexpensive hardware solution was developed here for respiration monitoring. Because the proposed sensors attach to the skin, as opposed to walls or ceilings, they can accompany patients from one procedure to the next, potentially allowing data gathered in different places and at different times to be combined and compared in ways that account for breathing motion.

show abstract

“…A variety of strategies and devices have been developed to collect information about respiration. For example, medical imaging scanners such as magnetic resonance imaging (MRI) and positron emission tomography (PET) can gather imaging data and breathing‐related information more or less at the same time 12–15 . Also, spirometers 16 and belts that wrap around the torso, often called respiratory bellows, 17 can be used to monitor breathing.…”

Section: Introductionmentioning

confidence: 99%

“…For example, medical imaging scanners such as magnetic resonance imaging (MRI) and positron emission tomography (PET) can gather imaging data and breathing-related information more or less at the same time. [12][13][14][15] Also, spirometers 16 and belts that wrap around the torso, often called respiratory bellows, 17 can be used to monitor breathing. Furthermore, camera systems can optically track the motion of reflectors placed on the surface of the body, [18][19][20] or the surface of the body itself.…”

Section: Introductionmentioning

confidence: 99%

Ultrasound‐based sensors to monitor physiological motion

et al. 2021

View full text Add to dashboard Cite

Medical procedures can be difficult to perform on anatomy that is constantly moving. Respiration displaces internal organs by up to several centimeters with respect to the surface of the body, and patients often have limited ability to hold their breath. Strategies to compensate for motion during diagnostic and therapeutic procedures require reliable information to be available. However, current devices often monitor respiration indirectly, through changes on the outline of the body, and they may be fixed to floors or ceilings, and thus unable to follow a given patient through different locations. Here we show that small ultrasound-based sensors referred to as "organ configuration motion" (OCM) sensors can be fixed to the abdomen and/or chest and provide information-rich, breathing-related signals. Methods: By design, the proposed sensors are relatively inexpensive. Breathing waveforms were obtained from tissues at varying depths and/or using different sensor placements. Validation was performed against breathing waveforms derived from magnetic resonance imaging (MRI) and optical tracking signals in five and eight volunteers, respectively. Results: Breathing waveforms from different modalities were scaled so they could be directly compared. Differences between waveforms were expressed in the form of a percentage, as compared to the amplitude of a typical breath. Expressed in this manner, for shallow tissues, OCM-derived waveforms on average differed from MRI and optical tracking results by 13.1% and 15.5%, respectively. Conclusion:The present results suggest that the proposed sensors provide measurements that properly characterize breathing states. While OCM-based waveforms from shallow tissues proved similar in terms of information content to those derived from MRI or optical tracking, OCM further captured depth-dependent and position-dependent (i.e., chest and abdomen) information. In time, the richer information content of OCM-based waveforms may enable better respiratory gating to be performed, to allow diagnostic and therapeutic equipment to perform at their best.

show abstract

Data-Driven Methods for the Determination of Anterior-Posterior Motion in PET

Cited by 7 publications

References 28 publications

Robust real-time extraction of respiratory signals from PET list-mode data

Robust real-time extraction of respiratory signals from PET list-mode data

Ultrasound-based sensors for respiratory motion assessment in multimodality PET imaging

Ultrasound‐based sensors to monitor physiological motion

Contact Info

Product

Resources

About