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Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

Jonmarker, O (author)
Karolinska Institutet
Axelsson, R (author)
Karolinska Institutet
Nilsson, T (author)
Karolinska Institutet
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Gabrielson, S (author)
Karolinska Institutet
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 (creator_code:org_t)
2021-03-31
2021
English.
In: Diagnostics (Basel, Switzerland). - : MDPI AG. - 2075-4418. ; 11:4
  • Journal article (peer-reviewed)
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  • In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: 68Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of 68Ga PMSA PET staging or restaging is comparable to OSEM.

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Jonmarker, O
Axelsson, R
Nilsson, T
Gabrielson, S
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Diagnostics (Bas ...
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Karolinska Institutet

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