Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook
Bibliographic record
Abstract
PURPOSE: Partial volume effects (PVE) remain a major challenge in quantitative single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, often compromising both accuracy and reproducibility. While numerous Partial Volume Correction (PVC) methods have been proposed, their clinical translation is still limited. This review provides a clinically oriented evaluation of PVC methods with a particular focus on state-of-the-art applications in neurology, cardiovascular imaging, oncology, and radiopharmaceutical therapy dosimetry, highlighting where these techniques offer the greatest added value. In addition, we outline which PVC techniques have the potential to be used in clinical practice and which remain primarily suited for research purposes, along with their suitability in each of the above-mentioned clinical domains. Finally, this review addresses the central question of whether PVC is essential in clinical practice or whether its impact is context dependent. METHODS: This review categorizes PVC approaches into three partially overlapping classes: reconstruction-based, post-reconstruction-based, and AI-driven or hybrid methods. Each class is further divided into anatomical and non-anatomical subcategories. We systematically compare their clinical applicability across key dimensions: quantitative accuracy, lesion detectability, robustness to noise and artifacts, anatomical dependence, generalizability across scanners and tracers, and clinical readiness. RESULTS: PVC techniques often improve quantitative accuracy in small structures and in regions affected by spill-over from adjacent high-uptake tissues. However, these benefits can come at the cost of increased noise or edge artifacts, which may limit their robustness for routine clinical use. Post-reconstruction methods are sensitive to segmentation errors, while AI-driven models, despite their promise, require further validation using clinical benchmarks, comparison to ground truth, and testing on diverse datasets. Issues, such as generalizability and interpretability remain significant barriers. CONCLUSION: This review emphasizes the importance of application-tailored PVC protocols for reliable quantitative imaging in neurology, cardiology, oncology, and radiopharmaceutical therapy dosimetry. Not all PVC methods are beneficial; some may even impair interpretation in certain contexts. We provide a practical overview of which PVC approaches are most beneficial for each clinical scenario, aiming to guide both researchers and clinicians in selecting appropriate techniques for future studies and routine practice, and also outline key areas requiring further development for broader integration into research and clinical workflows.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".