UZH-Logo

Maintenance Infos

Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner- comparison with clinical single atlas- and CT-based attenuation correction


Sekine, Tetsuro; Burgos, Ninon; Warnock, Geoffrey; Huellner, Martin W; Buck, Alfred; Ter Voert, Edwin E G W; Cardoso, M Jorge; Hutton, Brian F; Ourselin, Sebastien; Veit-Haibach, Patrick; Delso, Gaspar (2016). Multi atlas-based attenuation correction for brain FDG-PET imaging using a TOF-PET/MR scanner- comparison with clinical single atlas- and CT-based attenuation correction. Journal of Nuclear Medicine, 57(8):1258-1264.

Abstract

To assess the feasibility of attenuation correction (AC) based on a multi atlas-based method (m-Atlas) by comparing it with a clinical AC method (single atlas-based method (s-Atlas)), on a time of flight (TOF)-PET/MRI system. METHODS We enrolled 12 patients. The median patient age was 62 years [range 31 to 80]. All patients underwent a clinically indicated whole-body(18)F-FDG-PET/CT (GE Healthcare Discovery 690 PET/CT) for staging, re-staging or follow-up of malignant disease. All patients volunteered for an additional PET/MR scan of the head (GE Healthcare SIGNA PET/MR) (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-FLEX T1-weighted (T1w) images, being acquired by default on the PET/MR scanner during the first 18s of the PET scan. S-Atlas AC map was extracted by the PET/MR scanner, and m-Atlas AC map was created using a web service which automatically generates m-Atlas pseudo-CT images. For comparison, the CT-AC map generated by PET/CT was registered and used as gold standard. Using each AC map, PET images were reconstructed from raw data on the TOF-PET/MRI scanner. All PET images were normalized to the SPM5 PET template, and FDG accumulation was quantified in 67 volumes-of-interest (VOIs; automated anatomical labeling (AAL), atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT AC were calculated. FDG uptake in all VOIs and generalized merged VOIs were compared using paired t-test and Bland-Altman test. RESULTS The range of error on m-Atlas in all 804 VOIs was -4.98% ~ 4.09%. |%diff| on m-Atlas was improved by about 30% compared to s-Atlas (s-Atlas vs. m-Atlas; 1.5±1.1% vs. 1.2 ± 0.9%,P< 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum were significantly smaller (s-Atlas vs. m-Atlas; temporal lobe, 1.31±1.38% vs. -0.38±1.46%,P< 0.01; cerebellum, 1.46±2.12% vs. -1.07±1.87%,P< 0.01) CONCLUSION: The errors introduced using m-Atlas did not exceed 5 % in any brain region investigated. When compared to the clinical s-Atlas, m-Atlas is more accurate especially in regions close to the skull base.

Abstract

To assess the feasibility of attenuation correction (AC) based on a multi atlas-based method (m-Atlas) by comparing it with a clinical AC method (single atlas-based method (s-Atlas)), on a time of flight (TOF)-PET/MRI system. METHODS We enrolled 12 patients. The median patient age was 62 years [range 31 to 80]. All patients underwent a clinically indicated whole-body(18)F-FDG-PET/CT (GE Healthcare Discovery 690 PET/CT) for staging, re-staging or follow-up of malignant disease. All patients volunteered for an additional PET/MR scan of the head (GE Healthcare SIGNA PET/MR) (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-FLEX T1-weighted (T1w) images, being acquired by default on the PET/MR scanner during the first 18s of the PET scan. S-Atlas AC map was extracted by the PET/MR scanner, and m-Atlas AC map was created using a web service which automatically generates m-Atlas pseudo-CT images. For comparison, the CT-AC map generated by PET/CT was registered and used as gold standard. Using each AC map, PET images were reconstructed from raw data on the TOF-PET/MRI scanner. All PET images were normalized to the SPM5 PET template, and FDG accumulation was quantified in 67 volumes-of-interest (VOIs; automated anatomical labeling (AAL), atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT AC were calculated. FDG uptake in all VOIs and generalized merged VOIs were compared using paired t-test and Bland-Altman test. RESULTS The range of error on m-Atlas in all 804 VOIs was -4.98% ~ 4.09%. |%diff| on m-Atlas was improved by about 30% compared to s-Atlas (s-Atlas vs. m-Atlas; 1.5±1.1% vs. 1.2 ± 0.9%,P< 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum were significantly smaller (s-Atlas vs. m-Atlas; temporal lobe, 1.31±1.38% vs. -0.38±1.46%,P< 0.01; cerebellum, 1.46±2.12% vs. -1.07±1.87%,P< 0.01) CONCLUSION: The errors introduced using m-Atlas did not exceed 5 % in any brain region investigated. When compared to the clinical s-Atlas, m-Atlas is more accurate especially in regions close to the skull base.

Altmetrics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > University Hospital Zurich > Clinic for Nuclear Medicine
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:24 March 2016
Deposited On:03 Jun 2016 08:56
Last Modified:31 Oct 2016 08:21
Publisher:Society of Nuclear Medicine
ISSN:0161-5505
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.2967/jnumed.115.169045
PubMed ID:27013697

Download

Full text not available from this repository.
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations