Sara Garbarino, Giacomo Caviglia, Massimo Brignone et al.
This paper describes a general approach to the compartmental modeling of nuclear data based on spectral analysis and statistical optimization. We utilize the renal physiology as test case and validate the method against both synthetic data and real measurements acquired during two micro-PET experiments with murine models.
Mara Scussolini, Sara Garbarino, Gianmario Sambuceti et al.
The present paper proposes a novel computational method for parametric imaging of nuclear medicine data. The mathematical procedure is general enough to work for compartmental models of diverse complexity and is effective in the determination of the parametric maps of all kinetic parameters governing tracer flow. We consider applications to [18F]-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) data and analyze the two-compartment catenary model describing the standard FDG metabolization by an homogeneous tissue, e.g. the liver, and the three-compartment non-catenary model representing the renal physiology. The proposed imaging method starts from the reconstructed FDG-PET images of tracer concentration and preliminarily applies image processing algorithms for noise reduction and image segmentation processes for selecting the region enclosing the organ of physiologic interest. The optimization scheme solves pixelwise the non-linear inverse problem of determining the kinetic parameters from dynamic concentration data through a Gauss-Newton iterative algorithm with a penalty term accounting for the ill-posedness of the problem. We tested our imaging approach on FDG-PET data of murine models obtained by means of a dedicated microPET system, and we analyzed different PET slices containing axial sections of the liver and axial sections of the kidneys. The reconstructed parametric images proved to be reliable and qualitatively effective in the description of the local FDG metabolism with respect to the different physiologies.
Francesco Fiz, Helmut Dittmann, Cristina Campi et al.
To meet the current need for skeletal tumor-load estimation in prostate cancer (mCRPC), we developed a novel approach, based on adaptive bone segmentation. In this study, we compared the program output with existing estimates and with the radiological outcome. Seventy-six whole-body 99mTc-DPD-SPECT/CT from mCRPC patients were analyzed. The software identified the whole skeletal volume (SVol) and classified it voxels metastases (MVol) or normal bone (BVol). SVol was compared with the estimation of a commercial software. MVol was compared with manual assessment and with PSA-level. Counts/voxel were extracted from MVol and BVol. After six cycles of 223RaCl2-therapy every patient was re-evaluated as progressing (PD), stabilized (SD) or responsive (PR). SVol correlated with the one of the commercial software (R=0,99, p<0,001). MVol correlated with manually-counted lesions (R=0,61, p<0,001) and PSA (R=0,46, p<0.01). PD had a lower counts/voxel in MVol than PR/SD (715 \pm 190 Vs. 975 \pm 215 and 1058 \pm 255, p<0,05 and p<0,01) and in BVol (PD 275 \pm 60, PR 515 \pm 188 and SD 528 \pm 162 counts/voxel, p<0,001). Segmentation-based tumor load correlated with radiological/laboratory indices. Uptake was linked with the clinical outcome, suggesting that metastases in PD patients have a lower affinity for bone-seeking radionuclides and might benefit less from bone-targeted radioisotope therapies.