Sci-Fri AM: Imaging - 04: Experimental Validation of a Two Dimensional Partial Volume Correction Strategy for PET Imaging in Mice with Simulated and Measured Data

PURPOSE To evaluate the diagnostic value of position emission tomographic (PET) imaging with F-18 fluorodeoxyglucose (FDG) in differentiating between benign and malignant breast tumors. PATIENTS AND METHODS Fifty-one patients, with suspicious breast lesions newly discovered either by physical examination or by mammography, underwent PET imaging before exploratory surgery. FDG-PET images of the breast were analyzed visually and quantitatively for objective assessment of regional tracer uptake. RESULTS Primary breast cancer was identified visually with a sensitivity of 68% to 94% and a specificity of 84% to 97% depending on criteria used for image interpretation. Quantitative analysis of FDG uptake in tumors using standardized uptake values (SUV) showed a significant difference between benign (1.4 +/- 0.5) and malignant (3.3 +/- 1.8) breast tumors (P < .01). Receiver operating characteristic (ROC) curve analysis exhibited a sensitivity of 75% and a specificity of 100% at a threshold SUV value of 2.5. Sensitivity increased to 92% with a corresponding specificity of 97% when partial volume correction of FDG uptake was performed based on independent anatomic information. CONCLUSION PET imaging allowed accurate differentiation between benign and malignant breast tumors providing a high specificity. Sensitivity for detection of small breast cancer ( < 1 cm) was limited due to partial volume effects. Quantitative image analysis combined with partial volume correction may be necessary to exploit fully the diagnostic accuracy. PET imaging may be helpful as a complimentary method in a subgroup of patients with indeterminate results of conventional breast imaging.

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Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

EJNMMI Physics ◽

10.1186/s40658-020-00324-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Senri Oyama ◽

Ayumu Hosoi ◽

Masanobu Ibaraki ◽

Colm J. McGinnity ◽

Keisuke Matsubara ◽

...

Keyword(s):

Image Registration ◽

Pet Imaging ◽

Error Propagation ◽

Volume Effect ◽

Clinical Applications ◽

Partial Volume Correction ◽

Error Sources ◽

Partial Volume ◽

Volume Correction ◽

Brain Pet

Abstract Background Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. Methods We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer’s disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. Results Uncorrected SUVRs were underestimated against true SUVRs (− 15.7 and − 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. Conclusions We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.

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