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. 2022 Jul;23(7):e13626.
doi: 10.1002/acm2.13626. Epub 2022 May 10.

Novel approach to semi-quantification of tracer accumulation in dopamine transporter scan

Affiliations

Novel approach to semi-quantification of tracer accumulation in dopamine transporter scan

Yoshinori Ito et al. J Appl Clin Med Phys. 2022 Jul.

Abstract

Purpose: Accurate tracer accumulation evaluation is difficult owing to the partial volume effect (PVE). We proposed a novel semi-quantitative approach for measuring the accumulation amount by examining the approximate image. Using a striatal phantom, we verified the validity of a newly proposed method to accurately evaluate the tracer accumulations in the caudate and putamen separately. Moreover, we compared the proposed method with the conventional methods.

Methods: The left and right caudate/putamen regions and the whole brain region as background were identified in computed tomography (CT) images obtained by single-photon emission computed tomography (SPECT)/CT and acquired the positional information of each region. SPECT-like images were generated by assigning assumed accumulation amounts to each region. The SPECT-like image, approximated to the actual measured SPECT image, was examined by changing the assumed accumulation amounts assigned to each region. When the generated SPECT-like image most approximated the actual measured SPECT image, the accumulation amounts assumed were determined as the accumulation amounts in each region. We evaluated the correlation between the count density calculated by the proposed method and the actual count density of the 123 I solution filled in the phantom. Conventional methods (CT-guide method, geometric transfer matrix [GTM] method, region-based voxel-wise [RBV] method, and Southampton method) were also evaluated. The significance of differences between the correlation coefficients of various methods (except the Southampton method) was evaluated.

Results: The correlation coefficients between the actual count density and the SPECT count densities were 0.997, 0.973, 0.951, 0.950, and 0.996 for the proposed method, CT-guide method, GTM method, RBV method, and Southampton method, respectively. The correlation of the proposed method was significantly higher than those of the other methods.

Conclusions: The proposed method could calculate accurate accumulation amounts in the caudate and putamen separately, considering the PVE.

Keywords: FP-CIT; SPECT; dopamine transporter; quantification; specific binding ratio.

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Conflict of interest statement

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Figures

FIGURE 1
FIGURE 1
Overview of a striatal phantom. A striatal phantom consists of the left and right caudate/putamen and whole‐brain compartments
FIGURE 2
FIGURE 2
Flow of the calculation process by the proposed method. The proposed method consists of seven steps. This figure shows the flow from Step (1)–(7) of the proposed method. The series from Steps 2–5 was performed 10 times to compute more accurate accumulation amounts
FIGURE 3
FIGURE 3
The establishment of the volume of interests (VOIs). The left image shows the establishment of VOIs of the caudate, putamen, and the whole brain as background (BG) regions on computed tomography. The right image shows the VOIs of each region on single‐photon emission computed tomography
FIGURE 4
FIGURE 4
Comparison between the “real image” and the “generated image”. The “real image” and the “generated image” obtained from Phantom 1 (a) and the image obtained by Phantom 2 (b)
FIGURE 5
FIGURE 5
The correlation between the actual count density and the single‐photon emission computed tomography (SPECT) count density. The figure shows the correlation between the actual count density of the 123I solution filled in the phantom and the SPECT count densities computed by (a) the proposed method, (b) the computed tomography guide method, (c) the geometric transfer matrix method, (d) the region‐based voxel‐wise method, and (e) the Southampton method
FIGURE 6
FIGURE 6
The relationship between the number of processing times and the variability rate. (a) Shows the relation between the number of processing times and the variability of specific binding ratio (SBR). SBRs were converged as the processing was repeated. When the number of processing times was more than seven, the variability rates of the SBR were less than 5%. (b) Shows the relation between the number of processing times and the caudate/putamen ratio variability. This relation showed a similar trend to the SBR

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