The (freely available) MIRDfit software has been developed as a community tool to support the fitting of biodistribution time–activity data for internal dosimetry applications. MIRDfit enables accurate and reproducible TIAC estimation by offering nuclear medicine–specific fit functions, objective metrics for model selection, and robust uncertainty calculations. The software provides a user-friendly, Excel-based interface that integrates seamlessly with MIRDcalc for dose calculation and includes standardized options for QC and data archiving. MIRDfit supports a broad range of radiopharmaceuticals and is designed to enhance the reliability of internal dosimetry assessments.
More information can be found in the software manual.
FAQ
- MIRDfit is freely available for download
- MIRDfit is written/compiled in the Mircosoft Excel platform
- Features of MIRDfit: nuclear medicine–appropriate fit functions, objective metrics for best-fit selection, TIAC uncertainty estimation, trapezoidal integration options, quality control and data archiving, seamless integration with MIRDcalc, and a user-friendly, Excel-based interface.
- Future versions are expected
Reference Publications
Carter LM, Ocampo Ramos JC, Schuerrle SB, Marquis H, Lassmann M, Bolch WE, Kesner AL. MIRD Pamphlet No. 30: MIRDfit-A Tool for Fitting of Biodistribution Time-Activity Data for Internal Dosimetry. J Nucl Med. 2024 Sep 26:jnumed.124.268011. doi: 10.2967/jnumed.124.268011. Epub ahead of print. PMID: 39327016.
https://pubmed.ncbi.nlm.nih.gov/39327016/
Documentation & Instruction Manuals
Document manual (version 1.0.0.0)
System Requirements
MIRDfit is written and compiled within Microsoft Excel, and runs in the Excel environment. A licensed version of Microsoft Excel must be installed on the host computer. MIRDfit requires MS Excel version 2019 or later.
Currently, MIRDfit has only been developed for PC.
More information regarding system requirement can be found in the software manual.
MIRDfit user notes
Acknowledgements & Authors
The MIRDfit software is a product of an ongoing collaboration between University of Florida, Gainesville, FL, USA and Memorial Sloan Kettering Cancer Center, New York, USA.
This research was part of a funded project support by the NIH U01 EB028234 grant.
Major contributors to this development of this software include:
Lukas Carter (MSKCC),
Adam Kesner (MSKCC, co-PI), Wesley Bolch (UF, co-PI), Harry Marquis (MSKCC), Juan C. Ocampo Ramos (MSKCC), John Humm (MSKCC), Joe O’Donoghue (MSKCC), Pat Zanzonico (MSKCC)
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