Method for transforming CT images for attenuation correction in PET/CT imaging

2006 ◽  
Vol 33 (4) ◽  
pp. 976-983 ◽  
Author(s):  
Jonathan P. J. Carney ◽  
David W. Townsend ◽  
Vitaliy Rappoport ◽  
Bernard Bendriem
Keyword(s):  
Attenuation Correction ◽  
Ct Images ◽  
Ct Imaging ◽  
Pet Ct

scholarly journals Calcified Lymph Nodes Causing Clinically Relevant Attenuation Correction Artifacts on PET/CT Imaging

2010 ◽  
Vol 4 (2) ◽  
Author(s):  
Todd Blodgett ◽  
Amar Singh Mehta ◽  
Ajeet Mehta ◽  
Charles Laymon
Keyword(s):  
Lymph Nodes ◽  
Attenuation Correction ◽  
Ct Imaging ◽  
Pet Ct

scholarly journals Metal artifact reduction strategies for improved attenuation correction in hybrid PET/CT imaging

2012 ◽  
Vol 39 (6Part1) ◽  
pp. 3343-3360 ◽  
Author(s):  
Mehrsima Abdoli ◽  
Rudi A. J. O. Dierckx ◽  
Habib Zaidi
Keyword(s):  
Attenuation Correction ◽  
Ct Imaging ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Pet Ct ◽  
Reduction Strategies

4.54Inspection of the CT images in PET/CT prior to conversion into transmission map for attenuation correction: worth the extra effort?

2007 ◽  
Vol 14 (2) ◽  
pp. S34-S34
Author(s):  
R THOMPSON ◽  
B HSU ◽  
L DOUVILLE ◽  
J OKEEFE ◽  
K BYBEE ◽  
...  
Keyword(s):  
Attenuation Correction ◽  
Ct Images ◽  
Extra Effort ◽  
Pet Ct

Multi-modality imaging of uveal melanomas using combined PET/CT, high-resolution PET and MR imaging

2008 ◽  
Vol 47 (02) ◽  
pp. 73-79 ◽  
Author(s):  
U. Pietrzyk ◽  
C. Knoess ◽  
S. Vollmar ◽  
K. Wienhard ◽  
L. Kracht ◽  
...  
Keyword(s):  
High Resolution ◽  
Fdg Pet ◽  
Ct Images ◽  
Ct Imaging ◽  
Whole Body ◽  
Dynamic Mode ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
Tumour Activity ◽  
Uveal Melanomas

SummaryWe investigated the efficacy of combined FDG-PET/CT imaging for the diagnosis of small-size uveal melanomas and the feasibility of combining separate, high-resolution (HR) FDG-PET with MRI for its improved localization and detection. Patients, methods: 3 patients with small-size uveal melanomas (0.2–1.5 ml) were imaged on a combined whole-body PET/CT, a HR brain-PET, and a 1.5 T MRI. Static, contrast-enhanced FDG-PET/CT imaging was performed of head and torso with CT contrast enhancement. HR PET imaging was performed in dynamic mode 0–180 min post-injection of FDG. MRI imaging was performed using a high-resolution small-loop-coil placed over the eye in question with T2–3D-TSE and T1–3D-SE with 18 ml Gd-contrast. Patients had their eyes shaded during the scans. Lesion visibility on high-resolution FDGPET images was graded for confidence: 1: none, 2: suggestive, 3: clear. Mean tumour activity was calculated for summed image frames that resulted in confidence grades 2 and 3. Whole-body FDG-PET/CT images were reviewed for lesions. PET-MRI and PET/ CT-MRI images of the head were co-registered for potentially improved lesion delineation. Results: Whole-body FDG-PET/CT images of 3/3 patients were positive for uveal melanomas and negative for disseminated disease. HR FDG-PET was positive already in the early time frames. One patient exhibited rising tumour activity with increasing uptake time on FDG-PET. MRI images of the eye were co-registered successfully to FDG-PET/CT using a manual alignment approach. Conclusions: Small-size uveal melanomas can be detected with whole-body FDG-PET/CT. This feasibility study suggests the exploration of HR FDG-PET in order to provide additional diagnostic information on patients with uveal melanomas. First results support extended uptake times and high-sensitivity PET for improved tumour visibility. MRI/PET co-registration is feasible and provides correlated functional and anatomical information that may support alternative therapy regimens.

The Usefulness of Calculated Attenuation Correction in the Evaluation of Metallic Artifacts on Brain PET/CT Imaging

2006 ◽  
Vol 31 (9) ◽  
pp. 554-555 ◽  
Author(s):  
Samuel Almodovar ◽  
Sharon L. White ◽  
Homayoun Modarresifar ◽  
Buddhiwardhan C. Ojha
Keyword(s):  
Attenuation Correction ◽  
Ct Imaging ◽  
Brain Pet ◽  
Pet Ct ◽  
Metallic Artifacts

scholarly journals Deep learning–based metal artefact reduction in PET/CT imaging

2021 ◽  
Author(s):  
Hossein Arabi ◽  
Habib Zaidi
Keyword(s):  
Deep Learning ◽  
Scatter Correction ◽  
Ct Images ◽  
Ct Imaging ◽  
Metal Artefact Reduction ◽  
Metal Artefacts ◽  
Quantitative Pet ◽  
Pet Ct ◽  
Metal Artefact ◽  
Artefact Reduction

Abstract Objectives The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging. Methods Deep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques. Results The evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR. Conclusion The DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images. Key Points • The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

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