ReviewEJNMMI physics2026
Recent advances and current landscape of software tools for image analysis and dosimetry in nuclear medicine.
Review in EJNMMI physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Targeted Alpha Therapy as a Multiscale Design Problem: From Radioactive Decay to Therapeutic Outcome.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence across oncologic theranostics: evidence for patient stratification, dosimetry, and adaptive radiopharmaceutical therapy.Frontiers in nuclear medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in nuclear medicine, particularly in personalised radiopharmaceutical therapy, have emphasised the growing need for precise assessments of therapeutic safety and efficacy. These evaluations depend heavily on individual patient pharmacokinetics and dosimetry studies. Pharmacokinetics are typically assessed using whole-body SPECT/CT or PET/CT time-point imaging, preceded by rigorous calibration procedures to ensure the accuracy of absorbed dose calculations. The growing need for reliable imaging data has driven the development and adoption of various software tools aimed at optimising the processing, analysis, and dosimetry of nuclear medicine images. Open-source solutions are increasingly bridging the gap in accessibility, especially in resource-constrained environments, while AI-driven segmentation and time-activity curve modelling are emerging as critical innovations for improving workflow efficiency. Future efforts should prioritise validation, standardisation, and the development of robust tools tailored to complex dosimetry scenarios, including alpha and Auger therapies. This review evaluates several available software tools, both open-source and commercial, for processing calibration phantoms and patient images with an emphasis on quantitative analyses. It also examines tools used for post-imaging dosimetry. Key advancements in computational techniques are highlighted, including algorithms for dose calculation, computational models, and applications in deep learning. Furthermore, the review addresses existing limitations and ongoing efforts to enhance the accuracy, reproducibility, and clinical integration of these technologies. Future directions include integration of ultra-high-sensitivity detectors (e.g., long-axial-FOV PET and full-ring SPECT), wider adoption of standardised reconstruction and quantification workflows, incorporation of targeted alpha therapy and Auger models, improved uncertainty propagation, and routine implementation of accelerated clinical dosimetry pipelines. This manuscript aims to help support researchers, medical physicists, and clinicians in effectively adopting and applying these tools to improve outcomes in nuclear medicine practices.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.