Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients View Full Text


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Article Info

DATE

2015-07-30

AUTHORS

Elske Quak, Pierre-Yves Le Roux, Michael S. Hofman, Philippe Robin, David Bourhis, Jason Callahan, David Binns, Cédric Desmonts, Pierre-Yves Salaun, Rodney J. Hicks, Nicolas Aide

ABSTRACT

PurposePoint-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction may improve lesion detection in oncologic PET, but can alter quantitation resulting in variable standardized uptake values (SUVs) between different PET systems. This study aims to validate a proprietary software tool (EQ.PET) to harmonize SUVs across different PET systems independent of the reconstruction algorithm used.MethodsNEMA NU2 phantom data were used to calculate the appropriate filter for each PSF or PSF+TOF reconstruction from three different PET systems, in order to obtain EANM compliant recovery coefficients. PET data from 517 oncology patients were reconstructed with a PSF or PSF+TOF reconstruction for optimal tumour detection and an ordered subset expectation maximization (OSEM3D) reconstruction known to fulfil EANM guidelines. Post-reconstruction, the proprietary filter was applied to the PSF or PSF+TOF data (PSFEQ or PSF+TOFEQ). SUVs for PSF or PSF+TOF and PSFEQ or PSF+TOFEQ were compared to SUVs for the OSEM3D reconstruction. The impact of potential confounders on the EQ.PET methodology including lesion and patient characteristics was studied, as was the adherence to imaging guidelines.ResultsFor the 1380 tumour lesions studied, Bland-Altman analysis showed a mean ratio between PSF or PSF+TOF and OSEM3D of 1.46 (95 %CI: 0.86–2.06) and 1.23 (95 %CI: 0.95–1.51) for SUVmax and SUVpeak, respectively. Application of the proprietary filter improved these ratios to 1.02 (95 %CI: 0.88–1.16) and 1.04 (95 %CI: 0.92–1.17) for SUVmax and SUVpeak, respectively. The influence of the different confounding factors studied (lesion size, location, radial offset and patient’s BMI) was less than 5 %. Adherence to the European Association of Nuclear Medicine (EANM) guidelines for tumour imaging was good.ConclusionThese data indicate that it is not necessary to sacrifice the superior lesion detection and image quality achieved by newer reconstruction techniques in the quest for harmonizing quantitative comparability between PET systems. More... »

PAGES

2072-2082

References to SciGraph publications

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    28 schema:description PurposePoint-spread function (PSF) or PSF + time-of-flight (TOF) reconstruction may improve lesion detection in oncologic PET, but can alter quantitation resulting in variable standardized uptake values (SUVs) between different PET systems. This study aims to validate a proprietary software tool (EQ.PET) to harmonize SUVs across different PET systems independent of the reconstruction algorithm used.MethodsNEMA NU2 phantom data were used to calculate the appropriate filter for each PSF or PSF+TOF reconstruction from three different PET systems, in order to obtain EANM compliant recovery coefficients. PET data from 517 oncology patients were reconstructed with a PSF or PSF+TOF reconstruction for optimal tumour detection and an ordered subset expectation maximization (OSEM3D) reconstruction known to fulfil EANM guidelines. Post-reconstruction, the proprietary filter was applied to the PSF or PSF+TOF data (PSFEQ or PSF+TOFEQ). SUVs for PSF or PSF+TOF and PSFEQ or PSF+TOFEQ were compared to SUVs for the OSEM3D reconstruction. The impact of potential confounders on the EQ.PET methodology including lesion and patient characteristics was studied, as was the adherence to imaging guidelines.ResultsFor the 1380 tumour lesions studied, Bland-Altman analysis showed a mean ratio between PSF or PSF+TOF and OSEM3D of 1.46 (95 %CI: 0.86–2.06) and 1.23 (95 %CI: 0.95–1.51) for SUVmax and SUVpeak, respectively. Application of the proprietary filter improved these ratios to 1.02 (95 %CI: 0.88–1.16) and 1.04 (95 %CI: 0.92–1.17) for SUVmax and SUVpeak, respectively. The influence of the different confounding factors studied (lesion size, location, radial offset and patient’s BMI) was less than 5 %. Adherence to the European Association of Nuclear Medicine (EANM) guidelines for tumour imaging was good.ConclusionThese data indicate that it is not necessary to sacrifice the superior lesion detection and image quality achieved by newer reconstruction techniques in the quest for harmonizing quantitative comparability between PET systems.
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    35 ConclusionThese data
    36 EANM guidelines
    37 European Association
    38 FDG-PET quantification
    39 Medicine guidelines
    40 Nuclear Medicine (EANM) guidelines
    41 OSEM3D
    42 PET
    43 PET data
    44 PET methodology
    45 PET quantification
    46 PET system
    47 ResultsFor
    48 SUVmax
    49 SUVpeak
    50 adherence
    51 algorithm
    52 analysis
    53 applications
    54 appropriate filters
    55 association
    56 characteristics
    57 coefficient
    58 comparability
    59 confounders
    60 confounding factors
    61 data
    62 detection
    63 different PET systems
    64 different confounding factors
    65 eq
    66 expectation maximization reconstruction
    67 factors
    68 filter
    69 flight reconstruction
    70 function
    71 guidelines
    72 image quality
    73 imaging
    74 impact
    75 influence
    76 lesion detection
    77 lesions
    78 mean ratio
    79 methodology
    80 multicentre validation
    81 new reconstruction technique
    82 oncologic PET
    83 oncology patients
    84 optimal lesion detection
    85 optimal tumour detection
    86 order
    87 patient characteristics
    88 patients
    89 phantom data
    90 potential confounders
    91 proprietary software tools
    92 quality
    93 quantification
    94 quantitation
    95 quantitative comparability
    96 quest
    97 ratio
    98 reconstruction
    99 reconstruction algorithm
    100 reconstruction technique
    101 recovery coefficient
    102 software tools
    103 standardized uptake value
    104 study
    105 subsets expectation maximization (OSEM) reconstruction
    106 superior lesion detection
    107 system
    108 technique
    109 time
    110 tool
    111 tumor detection
    112 tumor imaging
    113 tumor lesions
    114 uptake value
    115 validation
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    117 schema:name Harmonizing FDG PET quantification while maintaining optimal lesion detection: prospective multicentre validation in 517 oncology patients
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