Anatomy-based correction of kidney PVE on [Formula: see text] SPECT images

Abstract

Background: In peptide receptor radionuclide therapy (PRRT), accurate quantification of kidney activity on post-treatment SPECT images paves the way for patient-specific treatment. Due to the limited spatial resolution of SPECT images, the partial volume effect (PVE) is a significant source of quantitative bias. In this study, we aimed to evaluate the performance and robustness of anatomy-based partial volume correction (PVC) algorithms to recover the accurate activity concentration of realistic kidney geometries on [Formula: see text]Lu SPECT images recorded under clinical conditions.

Methods: Based on the CT scan data from patients, three sets of fillable kidneys with surface-to-volume (S:V) ratios ranging from 1.5 to 2.8 cm^-1, were 3D printed and attached in a IEC phantom. Quantitative [Formula: see text]Lu SPECT/CT acquisitions were performed on a GE Discovery NM CT 870 DR camera for the three modified IEC phantoms and for 6 different Target-To-Background ratios (TBRs: 2, 4, 6, 8, 10, 12). Two region-based (GTM and Labbé) and five voxel-based (GTM + MTC, Labbé + MTC, GTM + RBV, Labbé + RBV and IY) methods were evaluated with this data set. Additionally, the robustness of PVC methods to Point Spread Function (PSF) discrepancies, registration mismatches and background heterogeneity was evaluated.

Results: Without PVC, the average kidney RCs across all TBRs ranged from 0.66 ± 0.05 (smallest kidney) to 0.80 ± 0.03 (largest kidney). For a TBR of 12, all anatomy-based method were able to recover the kidneys activity concentration with an error < 6%. All methods result in a comparable decline in RC restoration with decreasing TBR. The Labbé method was the most robust against PSF and registration mismatches but was also the most sensitive to background heterogeneity. Among the voxel-based methods, MTC images were less uniform than RBV and IY images at the outer edge of high uptake areas (kidneys and spheres).

Conclusion: Anatomy-based PVE correction allows for accurate SPECT quantification of the [Formula: see text]Lu activity concentration with realistic kidney geometries. Combined with recent progress in deep-learning algorithms for automatic anatomic segmentation of whole-body CT, these methods could be of particular interest for a fully automated OAR dosimetry pipeline with PVE correction.

Keywords: Lu; Partial volume correction; Partial volume effect; Peptide receptor radionuclide therapy; SPECT imaging.

Fig. 1

Illustration of kidney insert design and conception. A Manual segmentation of three kidney pairs from CT scans of patients referred for $_{}^{68}$Ga-DOTATOC PET/CT examination (3D-Slicer). B Modeling of a kidney filling and fixation system, along with a modular support tailored for the IEC phantom (Blender). C MSLA 3D printing of kidney inserts and associated fixation system using CHITUBOX slicer software and ELEGOO SATURN printer. D Final assembly of the IEC phantom incorporating the patient-derived kidney inserts

Fig. 2

Illustration of the procedure used to evaluate the seven PVC algorithms. A Fully quantitative SPECT-CT acquisitions of the 3 modified IEC phantoms for 6 TBR values. B Manual segmentation of structures using 3D-Slicer on the CT images corresponding to a TBR of 12 ($C{T}_{TBR\_12}$). The segmented structures are then exported in RTSTRUCT format ($RTSTRUC{T}_{TBR\_12}$). C Within MATLAB, $RTSTRUC{T}_{TBR\_12}$ is converted into a labeled mask at SPECT resolution ($Mas{k}_{TBR\_12}^{\mathit{SPECT}}$). This mask is rigidly propagated onto the masks corresponding to the other TBRs ($Mas{k}_{TBR\_i}^{\mathit{SPECT}}$) using the transformations derived from the alignment of $C{T}_{TBR\_12}$ with $C{T}_{TBR\_i}$ D Fully quantitative SPECT recordings ($SPEC{T}_{TBR\_i}$), structure masks ($Mas{k}_{TBR\_i}^{\mathit{SPECT}}$), and camera PSF serve as inputs to the PETPVC software

Fig. 3

A Tomographic calibration factors derived from acquisitions of a homogeneous $_{}^{177}$Lu-DOTATATE Jaszczak phantom over a wide range of activity values, using our clinical recording and reconstruction protocol for $_{}^{177}$Lu-dosimetry. B Background RC, computed within a region eroded by 3 cm from the original background segmentation, for the 18 measurements of the modified IEC phantom (3 kidney pairs and 6 TBRs). C Uncorrected RC plotted against TBR for the kidneys (black crosses, mean and std over the 6 kidney inserts) and for spheres ranging in size from 13-mm (orange circles) to 37-mm in diameter (black circles). D Uncorrected RC plotted against S:V ratio for the 6 kidney inserts (blue symbols, mean and std over the 6 TBRs) and the 5 spheres (red symbols, mean and std over the 18 recordings), complemented by the associated linear regression

Fig. 4

PVC results for a TBR of 12. A Uncorrected RCs (cross symbols) and PV-corrected RCs (point symbols, mean and std over the 7 PVC methods) plotted against the S:V ratio, for both the 6 kidney inserts (blue symbols) and the 5 spheres (red symbols). B Kidney RCs before and after PVC

Fig. 5

A Uncorrected (black solid squares) and corrected (empty squares) RC (averaged over the 6 kidneys) plotted against the TBR. B Relationship between the recovery of kidney RC (averaged over the 6 kidneys) and TBR, displayed with normalization by the highest TBR value (TBR_12) in the right panel and without normalization in the left panel

Fig. 6

Representative example for two of the three modified IEC phantoms (IEC 1 and 3) of an axial slice passing through the center of the spheres and a coronal slice passing through the two kidneys, before (first line) and after voxel-based PVC

Fig. 7

Representative examples of the IEC 1 phantom are presented for three TBR values ($TBR\_12$, $TBR\_6$, and $TBR\_2$). The images illustrate an axial slice passing through the sphere centers and a coronal slice through the two kidneys. The left column presents uncorrected images, the central columns display images after correction with MTC and RBV, and the right column depicts the voxel-to-voxel difference between MTC- and RBV-corrected images

Fig. 8

Influence of PSF mismatch (A), registration mismatch (B), and background heterogeneity (C) on the performances of PVC methods for a TBR of 12. A Kidney RC bias (averaged over the 6 kidneys) with respect to PSF mismatch (± 6 mm, 2 mm step, upper panel) and S:V ratio (4 mm PSF error, lower panel). B Kidney RC bias with respect to registration mismatch (1, 2 and 3 voxel sizes translations). C Kidney RC bias with respect to background heterogeneity (heterogeneity ratio ranging from 1 to 4 with 0.25 step, upper panel) and S:V ratio (heterogeneity ratio of 3, lower panel)