Improved motion correction in PROPELLER by using grouped blades as reference

2013 ◽  
Vol 39 (3) ◽  
pp. 700-707 ◽  
Author(s):  
Zhe Liu ◽  
Zhe Zhang ◽  
Kui Ying ◽  
Chun Yuan ◽  
Hua Guo
Keyword(s):  
Motion Correction ◽  
Grouped Blades

Motion correction eliminates discontinuities in parametric PET images of neuroreceptor binding

2005 ◽  
Vol 25 (1_suppl) ◽  
pp. S622-S622
Author(s):  
Hans R Herzog ◽  
Lutz Tellmann ◽  
Roger Fulton ◽  
Isabelle Stangier ◽  
Elena Rota Kops ◽  
...  
Keyword(s):  
Motion Correction

Motion correction in PET/CT

2005 ◽  
Vol 44 (S 01) ◽  
pp. S46-S50 ◽  
Author(s):  
M. Dawood ◽  
N. Lang ◽  
F. Büther ◽  
M. Schäfers ◽  
O. Schober ◽  
...  
Keyword(s):  
Optical Flow ◽  
Motion Correction ◽  
Motion Vector ◽  
Emission Tomography ◽  
Cardiac Pet ◽  
Novel Approach ◽  
Positron Emission ◽  
Pet Ct ◽  
Flow Algorithm ◽  
Motion Vector Field

Summary:Motion in PET/CT leads to artifacts in the reconstructed PET images due to the different acquisition times of positron emission tomography and computed tomography. The effect of motion on cardiac PET/CT images is evaluated in this study and a novel approach for motion correction based on optical flow methods is outlined. The Lukas-Kanade optical flow algorithm is used to calculate the motion vector field on both simulated phantom data as well as measured human PET data. The motion of the myocardium is corrected by non-linear registration techniques and results are compared to uncorrected images.

Image-based Motion Correction for the Siemens hybrid-MR/BrainPET Scanner

2019 ◽  
Author(s):  
J Scheins ◽  
CR Brambilla ◽  
J Mauler ◽  
E Rota kops ◽  
L Tellmann ◽  
...  
Keyword(s):  
Motion Correction

scholarly journals Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Virginia Liberini ◽  
Fotis Kotasidis ◽  
Valerie Treyer ◽  
Michael Messerli ◽  
Erika Orita ◽  
...  
Keyword(s):  
Neuroendocrine Tumors ◽  
Motion Correction ◽  
Accessory Spleen ◽  
Pancreatic Neuroendocrine Tumor ◽  
Reconstruction Algorithm ◽  
Data Driven ◽  
Operating Characteristics ◽  
Quantitative Pet ◽  
Pet Ct ◽  
Accessory Spleens

AbstractTo evaluate whether quantitative PET parameters of motion-corrected 68Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent 68Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM1000). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM1000 reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM1000 reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM1000/OSEM + DDG/BSREM1000 + DDG, respectively. BSREM1000 + DDG can accurately differentiate pNET from accessory spleen. Both BSREM1000 and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.

scholarly journals Motion correction of free-breathing magnetic resonance renography using model-driven registration

2021 ◽  
Author(s):  
Dimitra Flouri ◽  
Daniel Lesnic ◽  
Constantina Chrysochou ◽  
Jehill Parikh ◽  
Peter Thelwall ◽  
...  
Keyword(s):  
Motion Correction ◽  
Ground Truth ◽  
Free Breathing ◽  
Patient Data ◽  
Reference Object ◽  
Free Form ◽  
Model Driven ◽  
Time Courses ◽  
Signal Time ◽  
Parametric Maps

Abstract Introduction Model-driven registration (MDR) is a general approach to remove patient motion in quantitative imaging. In this study, we investigate whether MDR can effectively correct the motion in free-breathing MR renography (MRR). Materials and methods MDR was generalised to linear tracer-kinetic models and implemented using 2D or 3D free-form deformations (FFD) with multi-resolution and gradient descent optimization. MDR was evaluated using a kidney-mimicking digital reference object (DRO) and free-breathing patient data acquired at high temporal resolution in multi-slice 2D (5 patients) and 3D acquisitions (8 patients). Registration accuracy was assessed using comparison to ground truth DRO, calculating the Hausdorff distance (HD) between ground truth masks with segmentations and visual evaluation of dynamic images, signal-time courses and parametric maps (all data). Results DRO data showed that the bias and precision of parameter maps after MDR are indistinguishable from motion-free data. MDR led to reduction in HD (HDunregistered = 9.98 ± 9.76, HDregistered = 1.63 ± 0.49). Visual inspection showed that MDR effectively removed motion effects in the dynamic data, leading to a clear improvement in anatomical delineation on parametric maps and a reduction in motion-induced oscillations on signal-time courses. Discussion MDR provides effective motion correction of MRR in synthetic and patient data. Future work is needed to compare the performance against other more established methods.

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