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Machine learning-ba...
Machine learning-based prediction of conversion coefficients for I-123 metaiodobenzylguanidine heart-to-mediastinum ratio
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- Okuda, Koichi (författare)
- Hirosaki University
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- Nakajima, Kenichi (författare)
- Kanazawa University
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- Kitamura, Chiemi (författare)
- PDRadiopharma Inc
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- Ljungberg, Michael (författare)
- Lund University,Lunds universitet,Medicinsk strålningsfysik, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Nuclear Medicine Physics,Forskargrupper vid Lunds universitet,Medical Radiation Physics, Lund,Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,Lund University Research Groups
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- Hosoya, Tetsuo (författare)
- PDRadiopharma Inc
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- Kirihara, Yumiko (författare)
- PDRadiopharma Inc
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- Hashimoto, Mitsumasa (författare)
- Kanazawa University
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(creator_code:org_t)
- 2023-02-05
- 2023
- Engelska.
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Ingår i: Journal of Nuclear Cardiology. - : Springer Science and Business Media LLC. - 1071-3581 .- 1532-6551. ; 30:4, s. 1630-1641
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
Nyckelord
- I-MIBG
- collimator
- heart-to-mediastinum ratio
- Monte Carlo simulation
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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