Phase dyssynchrony and 123I-meta-iodobenzylguanidine innervation imaging towards standardization

Machine learning-based prediction of conversion coefficients for I-123 metaiodobenzylguanidine heart-to-mediastinum ratio

PURPOSE

We developed a method of standardizing the heart-to-mediastinal ratio in 123I-labeled meta-iodobenzylguanidine (MIBG) images using a conversion coefficient derived from a dedicated phantom. This study aimed to create a machine-learning (ML) model to estimate conversion coefficients without using a phantom.

METHODS

210 Monte Carlo (MC) simulations of 123I-MIBG images to obtain conversion coefficients using collimators that differed in terms of hole diameter, septal thickness, and length. Simulated conversion coefficients and collimator parameters were prepared as training datasets, then a gradient-boosting ML was trained to estimate conversion coefficients from collimator parameters. Conversion coefficients derived by ML were compared with those that were MC simulated and experimentally derived from 613 phantom images.

RESULTS

Conversion coefficients were superior when estimated by ML compared with the classical multiple linear regression model (root mean square deviations: 0.021 and 0.059, respectively). The experimental, MC simulated, and ML-estimated conversion coefficients agreed, being, respectively, 0.54, 0.55, and 0.55 for the low-; 0.74, 0.70, and 0.72 for the low-middle; and 0.88, 0.88, and 0.88 for the medium-energy collimators.

CONCLUSIONS

The ML model estimated conversion coefficients without the need for phantom experiments. This means that conversion coefficients were comparable when estimated based on collimator parameters and on experiments.

Calibrated scintigraphic imaging procedures improve quantitative assessment of the cardiac sympathetic nerve activity

The ¹²³I-labeled meta-iodobenzylguanidine (MIBG) is an analogue of noradrenaline that can evaluate cardiac sympathetic activity in scintigraphy. Quantitative analysis of ¹²³I-MIBG images has been verified in patients with heart failure and neurodegenerative diseases. However, quantitative results differ due to variations in scintigraphic imaging procedures. Here, we created and assessed the clinical feasibility of a calibration method for ¹²³I-MIBG imaging. The characteristics of scintigraphic imaging systems were determined using an acrylic calibration phantom to generate a multicenter phantom imaging database. Calibration factors corresponding to the scintigraphic imaging procedures were calculated from the database and applied to a clinical study. The results of this study showed that the calibrated analysis eliminated inter-institutional differences among normal individuals. In summary, our standardization methodology for ¹²³I-MIBG scintigraphy could provide the basis for improved diagnostic precision and better outcomes for patients.

Assessment of left ventricular ejection fraction with cardiofocal collimators: Comparison between IQ-SPECT, planar equilibrium radionuclide angiography, and cardiac magnetic resonance

BACKGROUND

IQ-SPECT has been shown to significantly reduce acquisition time and administered dose while preserving image quality in myocardial perfusion imaging. Whether IQ-SPECT provides accurate left ventricular ejection fractions (LVEF) with gated blood pool SPECT (GBPS) remains unknown.

METHODS

Sixty patients underwent IQ-SPECT GBPS and planar imaging. Among those patients, 11 underwent both cMRI and GBPS. GBPS LVEF, LVEDV, and LVESV were calculated using 2 validated software; QBS (Cedars-Sinai Medical Center, Los Angeles, USA) and MHI (Montreal Heart Institute, Montreal, Canada). LVEF, LVEDV, and LVESV obtained with the different modalities were compared.

RESULTS

Average planar LVEF was 48 ± 11% (mean ± SD), average LVEDV was 177 ± 59 mL (range 63 to 342 mL), and average LVESV was 96 ± 46 mL (range 16 to 234 mL). GBPS LVEF and their correlation coefficient with planar LVEF were 40 ± 12% (r = 0.70) and 44 ± 12% (r = 0.83) with QBS and MHI, respectively. Correlation coefficient between cMRI and planar LVEF was 0.65 and were 0.69 and 0.52 between cMRI and GBPS using QBS and MHI, respectively.

CONCLUSIONS

LVEF calculated with GBPS using IQ-SPECT correlates with planar measurements. Correlation is best using the MHI method and variation is independent of LVEDV.