Body Deformation Correction for SPECT Imaging

Gu, Songxiang; McNamara, Joseph E.; Mitra, J.; Gifford, Howard C.; Johnson, Karen; Gennert, Michael A.; King, Michael A.

doi:10.1109/tns.2009.2031114

Cited by 17 publications

(3 citation statements)

References 28 publications

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“…A reference respiratory signal was obtained using an infrared stereo-camera VTS, tracking retroreflective markers on the anterior surface of patient's abdomen. 19,36 This reference signal is used as the target signal in DL given its well-tested capability of accurately capturing patient's motion. 37,38 Further details on the VTS and SPECT data acquisition as well as the synchronizations were explained in reference.…”

Section: Spect and Vts Data Acquisitionmentioning

confidence: 99%

Respiratory signal estimation for cardiac perfusion SPECT using deep learning

Chen,

Pretorius,

Lindsay

et al. 2023

Medical Physics

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BackgroundRespiratory motion induces artifacts in reconstructed cardiac perfusion SPECT images. Correction for respiratory motion often relies on a respiratory signal describing the heart displacements during breathing. However, using external tracking devices to estimate respiratory signals can add cost and operational complications in a clinical setting.PurposeWe aim to develop a deep learning (DL) approach that uses only SPECT projection data for respiratory signal estimation.MethodsA modified U‐Net was implemented that takes temporally finely sampled SPECT sub‐projection data (100 ms) as input. These sub‐projections are obtained by reframing the 20‐s list‐mode data, resulting in 200 sub‐projections, at each projection angle for each SPECT camera head. The network outputs a 200‐time‐point motion signal for each projection angle, which was later aggregated over all angles to give a full respiratory signal. The target signal for DL model training was from an external stereo‐camera visual tracking system (VTS). In addition to comparing DL and VTS, we also included a data‐driven approach based on the center‐of‐mass (CoM) strategy. This CoM method estimates respiratory signals by monitoring the axial changes of CoM for counts in the heart region of the sub‐projections. We utilized 900 subjects with stress cardiac perfusion SPECT studies, with 302 subjects for testing and the remaining 598 subjects for training and validation.ResultsThe Pearson's correlation coefficient between the DL respiratory signal and the reference VTS signal was 0.90, compared to 0.70 between the CoM signal and the reference. For respiratory motion correction on SPECT images, all VTS, DL, and CoM approaches partially de‐blured the heart wall, resulting in a thinner wall thickness and increased recovered maximal image intensity within the wall, with VTS reducing blurring the most followed by the DL approach. Uptake quantification for the combined anterior and inferior segments of polar maps showed a mean absolute difference from the reference VTS of 1.7% for the DL method for patients with motion >12 mm, compared to 2.6% for the CoM method and 8.5% for no correction.ConclusionWe demonstrate the capability of a DL approach to estimate respiratory signal from SPECT projection data for cardiac perfusion imaging. Our results show that the DL based respiratory motion correction reduces artefacts and achieves similar regional quantification to that obtained using the stereo‐camera VTS signals. This may enable fully automatic data‐driven respiratory motion correction without relying on external motion tracking devices.

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Section: Spect and Vts Data Acquisitionmentioning

confidence: 99%

Respiratory signal estimation for cardiac perfusion SPECT using deep learning

Chen,

Pretorius,

Lindsay

et al. 2023

Medical Physics

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“…The Vicon (Vicon Motion Systems Ltd, Oxford, UK) is a multi-camera IR system using passive reflective markers. A 5-camera Vicon set-up was used to track arrays of reflective spheres on chest and abdominal belts for respiratory motion estimation (McNamara et al 2009, Gu et al 2010. The trinocular Optotrak systems (Northern Digital, Ontario, Canada), which use active, pulsed IR markers have been used as benchmark systems due to their extremely high positional accuracy (Barnes et al 2008).…”

Section: Stereo-visionmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

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Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and x-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art deep learning methods may have a unique role to play in this context.

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“…[4][5][6][7][8] and other forms of patient motion during SPECT. [9][10][11][12] List-mode correction techniques have also been reported for both animal motion in preclinical positron emission tomography (PET) (Ref. 13) and patient head motion in neuro-PET.…”

Section: Introductionmentioning

confidence: 99%

A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list‐mode acquisitions

et al. 2013

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Purpose: This technical note documents a method that the authors developed for combining a signal to synchronize a patient-monitoring device with a second physiological signal for inclusion into listmode acquisition. Our specific application requires synchronizing an external patient motion-tracking system with a medical imaging system by multiplexing the tracking input with the ECG input. The authors believe that their methodology can be adapted for use in a variety of medical imaging modalities including single photon emission computed tomography (SPECT) and positron emission tomography (PET). Methods: The authors insert a unique pulse sequence into a single physiological input channel. This sequence is then recorded in the list-mode acquisition along with the R-wave pulse used for ECG gating. The specific form of our pulse sequence allows for recognition of the time point being synchronized even when portions of the pulse sequence are lost due to collisions with R-wave pulses. This was achieved by altering our software used in binning the list-mode data to recognize even a portion of our pulse sequence. Limitations on heart rates at which our pulse sequence could be reliably detected were investigated by simulating the mixing of the two signals as a function of heart rate and time point during the cardiac cycle at which our pulse sequence is mixed with the cardiac signal. Results: The authors have successfully achieved accurate temporal synchronization of our motiontracking system with acquisition of SPECT projections used in 17 recent clinical research cases. In our simulation analysis the authors determined that synchronization to enable compensation for body and respiratory motion could be achieved for heart rates up to 125 beats-per-minute (bpm). Conclusions: Synchronization of list-mode acquisition with external patient monitoring devices such as those employed in motion-tracking can reliably be achieved using a simple method that can be implemented using minimal external hardware and software modification through a single input channel, while still recording cardiac gating signals.

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Body Deformation Correction for SPECT Imaging

Cited by 17 publications

References 28 publications

Respiratory signal estimation for cardiac perfusion SPECT using deep learning

Respiratory signal estimation for cardiac perfusion SPECT using deep learning

Motion estimation and correction in SPECT, PET and CT

A method to synchronize signals from multiple patient monitoring devices through a single input channel for inclusion in list‐mode acquisitions

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