Voxel-based partial volume correction of amyloid PET images incorporating non-local means regularization

Purpose: To test whether correcting for unspecific signal from the cerebral white matter increases the sensitivity of amyloid-PET for early stages of cerebral amyloidosis.Methods: We analyzed 18F-Florbetapir-PET and cerebrospinal fluid (CSF) Aβ42 data from 600 older individuals enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), including people with normal cognition, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) dementia. We determined whether three compartmental partial volume correction (PVC-3), explicitly modeling signal spill-in from white matter, significantly improved the association of CSF Aβ42 levels with global 18F-Florbetapir-PET values compared with standard processing without PVC (non-PVC) and a widely used two-compartmental PVC method (PVC-2). In additional voxel-wise analyses, we determined the sensitivity of PVC-3 compared with non-PVC and PVC-2 for detecting early regional amyloid build-up as modeled by decreasing CSF Aβ42 levels. For replication, we included an independent sample of 43 older individuals with subjective memory complaints from the INveStIGation of AlzHeimer’s PredicTors cohort (INSIGHT-preAD study).Results: In the ADNI sample, PVC-3 18F-Florbetapir-PET values normalized to whole cerebellum signal showed significantly stronger associations with CSF Aβ42 levels than non-PVC or PVC-2, particularly in the lower range of amyloid levels. These effects were replicated in the INSIGHT-preAD sample. PVC-3 18F-Florbetapir-PET data detected regional amyloid build-up already at higher (less abnormal) CSF Aβ42 levels than non-PVC or PVC-2 data.Conclusion: A PVC approach that explicitly models unspecific white matter binding improves the sensitivity of amyloid-PET for identifying the earliest stages of cerebral amyloid pathology which has implications for future primary prevention trials.

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Segmentation-Based Partial Volume Correction for Volume Estimation of Solid Lesions in CT

IEEE Transactions on Medical Imaging ◽

10.1109/tmi.2013.2287374 ◽

2014 ◽

Vol 33 (2) ◽

pp. 462-480 ◽

Cited By ~ 21

Author(s):

Frank Heckel ◽

Hans Meine ◽

Jan H. Moltz ◽

Jan-Martin Kuhnigk ◽

Johannes T. Heverhagen ◽

...

Keyword(s):

Volume Estimation ◽

Partial Volume Correction ◽

Partial Volume ◽

Volume Correction ◽

Solid Lesions

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The effect of calibration factors and recovery coefficients on 177Lu SPECT activity quantification accuracy: a Monte Carlo study

EJNMMI Physics ◽

10.1186/s40658-021-00365-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Keamogetswe Ramonaheng ◽

Johannes A. van Staden ◽

Hanlie du Raan

Keyword(s):

Monte Carlo ◽

Monte Carlo Study ◽

Partial Volume Correction ◽

Detector Response ◽

Physical Size ◽

Partial Volume ◽

Volume Correction ◽

Radioactive Water ◽

Quantification Accuracy ◽

Cylindrical Volume

Abstract Background Different gamma camera calibration factor (CF) geometries have been proposed to convert SPECT data into units of activity concentration. However, no consensus has been reached on a standardised geometry. The CF is dependent on the selected geometry and is further affected by partial volume effects. This study investigated the effect of two CF geometries and their corresponding recovery coefficients (RCs) on the quantification accuracy of 177Lu SPECT images using Monte Carlo simulations. Methods The CF geometries investigated were (i) a radioactive-sphere surrounded by non-radioactive water (sphere-CF) and (ii) a cylindrical phantom uniformly filled with radioactive water (cylinder-CF). Recovery coefficients were obtained using the sphere-CF and cylinder-CF, yielding the sphere-RC and cylinder-RC values, respectively, for partial volume correction (PVC). The quantification accuracy was evaluated using four different-sized spheres (15.6–65.4 ml) and a kidney model with known activity concentrations inside a cylindrical, torso and patient phantom. Images were reconstructed with the 3D OS-EM algorithm incorporating attenuation, scatter and detector-response corrections. Segmentation was performed using the physical size and a small cylindrical volume inside the cylinder for the sphere-CF and cylinder-CF, respectively. Results The sphere quantification error (without PVC) was better for the sphere-CF (≤ − 5.54%) compared to the cylinder-CF (≤ − 20.90%), attributed to the similar geometry of the quantified and CF spheres. Partial volume correction yielded comparable results for the sphere-CF-RC (≤ 3.47%) and cylinder-CF-RC (≤ 3.53%). The accuracy of the kidney quantification was poorer (≤ 22.34%) for the sphere-CF without PVC compared to the cylinder-CF (≤ 2.44%). With PVC, the kidney quantification results improved and compared well for the sphere-CF-RC (≤ 3.50%) and the cylinder-CF-RC (≤ 3.45%). Conclusion The study demonstrated that upon careful selection of CF-RC combinations, comparable quantification errors (≤ 3.53%) were obtained between the sphere-CF-RC and cylinder-CF-RC, when all corrections were applied.

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MR-Based Correction of Brain PET Measurements for Heterogeneous Gray Matter Radioactivity Distribution

Journal of Cerebral Blood Flow & Metabolism ◽

10.1097/00004647-199607000-00016 ◽

1996 ◽

Vol 16 (4) ◽

pp. 650-658 ◽

Cited By ~ 96

Author(s):

Carolyn Cidis Meltzer ◽

Jon Kar Zubieta ◽

Jonathan M. Links ◽

Paul Brakeman ◽

Martin J. Stumpf ◽

...

Keyword(s):

Gray Matter ◽

Accurate Determination ◽

Brain Structures ◽

Volume Averaging ◽

Partial Volume Correction ◽

Sampling Errors ◽

Partial Volume ◽

Volume Correction ◽

Healthy Elderly ◽

Wide Range

Partial volume and mixed tissue sampling errors can cause significant inaccuracy in quantitative positron emission tomographic (PET) measurements. We previously described a method of correcting PET data for the effects of partial volume averaging on gray matter (GM) quantitation; however, this method may incompletely correct GM structures when local tissue concentrations are highly heterogeneous. We have extended this three-compartment algorithm to include a fourth compartment: a GM volume of interest (VOI) that can be delineated on magnetic resonance (MR) imaging. Computer simulations of PET images created from human MR data demonstrated errors of up to 120% in assigned activity values in small brain structures in uncorrected data. Four-compartment correction achieved full recovery of a wide range of coded activity in GM VOIs such as the amygdala, caudate, and thalamus. Further validation was performed in an agarose brain phantom in actual PET acquisitions. Implementation of this partial volume correction approach in [18F]fluorodeoxyglucose and [11C]-carfentanil PET data acquired in a healthy elderly human subject was also performed. This newly developed MR-based partial volume correction algorithm permits the accurate determination of the true radioactivity concentration in specific structures that can be defined by MR by accounting for the influence of heterogeneity of GM radioactivity.

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