BSREM Reconstruction for Improved Detection of In-Transit Metastases With Digital FDG-PET/CT in Patients With Malignant Melanoma

ABSTRACTBlock sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the d...

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Bibliographic Details
Published in:Clinical nuclear medicine 2018-05, Vol.43 (5), p.370-371
Main Authors: Aljared, Arwa, Alharbi, Abdullah Awad, Huellner, Martin W
Format: Article
Language:English
Subjects:
Aged
Fluorodeoxyglucose F18
Humans
Image Processing, Computer-Assisted - methods
Lymphatic Metastasis
Male
Melanoma - diagnostic imaging
Melanoma - pathology
Melanoma, Cutaneous Malignant
Positron Emission Tomography Computed Tomography - methods
Radiopharmaceuticals
Skin Neoplasms - diagnostic imaging
Skin Neoplasms - pathology
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Summary:ABSTRACTBlock sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the detection of small lesions, which may be beneficial in melanoma patients. Our case of a 70-year-old man with metastasized malignant melanoma illustrates the impact of such a novel iterative PET reconstruction algorithm. Whereas the lymph node metastases are seen with the latest generation ordered subset expectation maximization reconstruction, the in-transit metastases are identified straightforward only with BSREM reconstruction.
ISSN:0363-9762
1536-0229
DOI:10.1097/RLU.0000000000002024