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Premium Description and assessment of a registration‐based approach to include bones for attenuation correction of whole‐body PET/MRI
Marshall Harry R.,
Patrick John,
Laidley David,
Prato Frank S.,
Butler John,
Théberge Jean,
Thompson R. Terry,
Stodilka Robert Z.
Publication year2013
Publication title
medical physics
Resource typeJournals
PublisherAmerican Association of Physicists in Medicine
Purpose: Attenuation correction for whole‐body PET/MRI is challenging. Most commercial systems compute the attenuation map from MRI using a four‐tissue segmentation approach. Bones, the most electron‐dense tissue, are neglected because they are difficult to segment. In this work, the authors build on this segmentation approach by adding bones using a registration technique and assessing its performance on human PET images.Methods: Twelve oncology patients were imaged with FDG PET/CT and MRI using a Turbo‐FLASH pulse sequence. A database of 121 attenuation correction quality CT scans was also collected. Each patient MRI was compared to the CT database via weighted heuristic measures to find the “most similar” CT in terms of body geometry. The similar CT was aligned to the MRI with a deformable registration method. Two MRI‐based attenuation maps were computed. One was a standard four‐tissue segmentation (air, lung, fat, and lean tissue) using basic image processing techniques. The other was identical, except the bones from the aligned CT were added. The PET data were reconstructed with the patient's CT‐based attenuation map (the silver standard) and both MRI‐based attenuation maps. The relative errors of the MRI‐based attenuation corrections were computed in 14 standardized volumes of interest, in lesions, and over whole tissues. The squared Pearson correlation coefficient was also calculated over whole tissues. Statistical testing was done with ANOVAs and paired t ‐tests.Results: The MRI‐based attenuation correction ignoring bone had relative errors ranging from −37% to −8% in volumes of interest containing bone. By including bone, the magnitude of the relative error was reduced in all cases (  p < 0.001), ranging from −3% to 4%. Further, the relative error in volumes of interest adjacent to bone was improved from a mean of −7.5% to 2% (  p < 0.001). In the other seven volumes of interest, including bone reduced the magnitude of relative error in three cases (  p < 0.001), had no effect in three cases, and increased relative error in one case. There was no statistically significant difference in the relative error in lesions. Over whole tissues, including bone slightly increased relative error in lung from 7.7% to 8.0% (  p = 0.002), in fat from 8.5% to 9.2%(  p < 0.001), and in lean tissue from −2.1% to 2.6% (  p < 0.001), but reduced the magnitude of relative error in bone from −14.6% to 1.3% (  p < 0.001). The correlation coefficient was essentially unchanged in all tissues regardless of whether bone was included or not.Conclusions: The approach to include bones in MRI‐based attenuation maps described in this work improves quantification of whole‐body PET images in and around bony anatomy. The reduction in error is often large (tens of percents), and could alter image interpretation and subsequent patient care. Changes in other parts of the PET image are minimal and likely not of clinical significance.
Subject(s)artificial intelligence , attenuation , biomedical engineering , computer science , correction for attenuation , image (mathematics) , image quality , image registration , medicine , nuclear medicine , optics , physics , positron emission tomography , segmentation , whole body imaging
SCImago Journal Rank1.473

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