Validation of model-based pelvis bone segmentation from MR images for PET/MR attenuation correction

2012 ◽  
Author(s):  
S. Renisch ◽  
T. Blaffert ◽  
J. Tang ◽  
Z. Hu
2012 ◽  
Vol 11 (2) ◽  
pp. 7290.2011.00036 ◽  
Author(s):  
Vincent Keereman ◽  
Yves Fierens ◽  
Christian Vanhove ◽  
Tony Lahoutte ◽  
Stefaan Vandenberghe

Attenuation correction is necessary for quantification in micro–single-photon emission computed tomography (micro-SPECT). In general, this is done based on micro–computed tomographic (micro-CT) images. Derivation of the attenuation map from magnetic resonance (MR) images is difficult because bone and lung are invisible in conventional MR images and hence indistinguishable from air. An ultrashort echo time (UTE) sequence yields signal in bone and lungs. Micro-SPECT, micro-CT, and MR images of 18 rats were acquired. Different tracers were used: hexamethylpropyleneamine oxime (brain), dimercaptosuccinic acid (kidney), colloids (liver and spleen), and macroaggregated albumin (lung). The micro-SPECT images were reconstructed without attenuation correction, with micro-CT-based attenuation maps, and with three MR-based attenuation maps: uniform, non-UTE-MR based (air, soft tissue), and UTE-MR based (air, lung, soft tissue, bone). The average difference with the micro-CT-based reconstruction was calculated. The UTE-MR-based attenuation correction performed best, with average errors ≤ 8% in the brain scans and ≤ 3% in the body scans. It yields nonsignificant differences for the body scans. The uniform map yields errors of ≤ 6% in the body scans. No attenuation correction yields errors ≥ 15% in the brain scans and ≥ 25% in the body scans. Attenuation correction should always be performed for quantification. The feasibility of MR-based attenuation correction was shown. When accurate quantification is necessary, a UTE-MR-based attenuation correction should be used.


2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Elin Wallstén ◽  
Jan Axelsson ◽  
Joakim Jonsson ◽  
Camilla Thellenberg Karlsson ◽  
Tufve Nyholm ◽  
...  

Abstract Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.


2010 ◽  
Vol 55 (16) ◽  
pp. 4755-4769 ◽  
Author(s):  
Derek Magee ◽  
Steven F Tanner ◽  
Michael Waller ◽  
Ai Lyn Tan ◽  
Dennis McGonagle ◽  
...  

2021 ◽  
Author(s):  
Yaopeng Peng ◽  
Hao Zheng ◽  
Fahim Zaman ◽  
Lichun Zhang ◽  
Xiaodong Wu ◽  
...  

<div>Knee cartilage and bone segmentation is critical for physicians to analyze and diagnose articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical image segmentation have largely outperformed traditional methods, but they often need large amounts of annotated data for model training, which is very costly and time-consuming for medical experts, especially on 3D images. In this paper, we report a new knee cartilage and bone segmentation framework, KCB-Net, for 3D MR images based on sparse annotation. KCB-Net selects a small subset of slices from 3D images for annotation, and seeks to bridge the performance gap between sparse annotation and full annotation. Specifically, it first identifies a subset of the most effective and representative slices with an unsupervised scheme; it then trains an ensemble model using the annotated slices; next, it self-trains the model using 3D images containing pseudo-labels generated by the ensemble method and improved by a bi-directional hierarchical earth mover’s distance (bi-HEMD) algorithm; finally, it fine-tunes the segmentation results using the primal-dual Internal Point Method (IPM). Experiments on two 3D MR knee joint datasets (the Iowa dataset and iMorphics dataset) show that our new framework outperforms state-of-the-art methods on full annotation, and yields high quality results even for annotation ratios as low as 5%.<br></div>


2011 ◽  
Author(s):  
Shouhei Hanaoka ◽  
Karl Fritscher ◽  
Benedikt Schuler ◽  
Yoshitaka Masutani ◽  
Naoto Hayashi ◽  
...  

2016 ◽  
Vol 57 (10) ◽  
pp. 1635-1641 ◽  
Author(s):  
Y. Wu ◽  
W. Yang ◽  
L. Lu ◽  
Z. Lu ◽  
L. Zhong ◽  
...  

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