Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 4 appraisals
Physics in medicine and biology
Hybrid learning: a combination of self-supervised and supervised learning for joint MRI reconstruction and denoising in low-field MRI
Objective.Deep learning has demonstrated strong potential for magnetic resonance imaging (MRI) reconstruction. However, conventional supervised learning requires high-quality, high-signal-to-noise-ratio (SNR) reference data for network training, which are often difficult or impossible to obtain, particularly in low-field MRI. Self-supervised learning (SSL) eliminates the need for reference training data but may suffer from degraded performance under low-SNR conditions. To address these limitations, we propose hybrid learning, a new training framework that integrates self-supervised and supervised learning for joint MRI reconstruction and denoising when only low-SNR training data are available.Approach.Hybrid learning is implemented in two sequential stages. In the first stage, SSL is applied to fully sampled low-SNR data to generate higher-quality pseudo-references. In the second stage, these pseudo-references are then used as targets for supervised learning to reconstruct and denoise undersampled, noisy data. The proposed method was evaluated in four experiments using simulated and real noisy MRI data of the breast, lung, and brain across different field strengths (0.3 T to 3 T), sampling trajectories (Cartesian, spiral, and radial), noise levels, and undersampling ratios.Main Results.Hybrid learning consistently improved reconstruction quality relative to both supervised and self-supervised baselines under different acceleration rates, noise levels, and sampling patterns in all experiments. Compared with standard supervised learning using noisy references, it achieved up to 167.70% higher structural similarity index measure (SSIM), 95.41% lower normalized mean squared error (NMSE), and 90.70% lower high-frequency error norm (HFEN). Compared with standard SSL, it achieved up to 23.88% higher SSIM, 60.85% lower NMSE, and 49.13% lower HFEN.Significance.Hybrid learning enables improved MRI reconstruction under low-SNR imaging conditions by jointly addressing noise and undersampling. It provides a practical solution for robust deep learning-based reconstruction and is particularly well suited for applications such as low-field MRI, where image quality is limited by reduced SNR.
26 June 2026
Read appraisal →Journal of breast imaging
Breast Arterial Calcification as a Predictor for Future Cardiovascular Events and Mortality: A Systematic Review and Meta-analysis
OBJECTIVE: Breast arterial calcification (BAC) is a surrogate marker of cardiovascular disease (CVD); however, its associations with incident mortality and major adverse cardiovascular events (MACEs) have not been systematically evaluated. The association between BAC and incident MACE and whether this is influenced by the method of BAC assessment was examined. METHODS: PubMed, MEDLINE, and EMBASE were reviewed to June 2025 for studies comparing BAC status with incident MACE (composite of death, myocardial infarction [MI], stroke, heart failure). The primary outcome was all-cause mortality. Random-effects meta-analysis was performed using hazard ratios (HRs) with 95% CIs. Subgroup analysis was conducted by method of BAC measurement and restricted to screening mammography cohorts, PROSPERO registration (CRD42020202612). RESULTS: Of 1118 studies screened, 24 were included (236 475 participants; mean age 58.5 years). Breast arterial calcification presence associated with all-cause mortality (HR, 1.32; 95% CI, 1.20-1.45; P <.0001) (follow-up 0.55-26 years). Increased risk was observed for cardiovascular mortality (HR, 1.38; 95% CI, 1.14-1.66; P = .0009), heart failure (HR, 1.29; 95% CI, 1.20-1.37; P <.0001), stroke (HR, 1.2; 95% CI, 1.11-1.30; P <.0001), and MI (HR, 1.2; 95% CI, 1.15-1.26; P <.0001). When restricted to screening mammography cohorts without concurrent indications for cardiovascular investigation (5 studies, n = 85 647), associations remained significant for all-cause mortality (HR, 1.34; 95% CI, 1.14-1.58; P = .0004), cardiovascular mortality (HR, 1.38; 95% CI, 1.14-1.66; P = .0009), heart failure (HR, 1.48; 95% CI, 1.19-1.86; P = .0005), and stroke (HR, 1.34; 95% CI, 1.08-1.67; P = .008). Associations with incident outcomes were observed only in studies using quantitative BAC assessment (HR, 1.31; 95% CI, 1.19-1.44; P = .002). CONCLUSION: Breast arterial calcification is a promising prognostic marker of mortality and CVD risk. Recognition of this incidental finding may enable timely identification of at-risk individuals.
27 May 2026
Read appraisal →Physics in medicine and biology
ReaderAdaptNet: modeling reader variability in breast imaging with reader-specific embeddings
Objective.Inter-reader variability remains a major challenge in breast imaging interpretation, particularly for ordinal classification tasks such as breast density and background parenchymal enhancement (BPE). These visual assessments are prone to inconsistencies due to subjective interpretation, limiting the reliability of AI-based models trained on aggregated or noisy labels. This work aims to account for this variability rather than collapse it.Approach.We propose ReaderAdaptNet, a reader-adaptive network that explicitly models inter-reader variability through reader-specific embeddings. A novel two-stage deep learning framework underpins the training process of the model. The first stage jointly learns discriminative image features and low-dimensional embeddings representing individual annotation styles, enabling personalized classification. The second stage allows for embedding calibration using a small set of labeled examples, enabling rapid adaptation to new readers or consensus definitions without retraining the full model. The method is designed for multi-reader datasets and is evaluated on two breast classification tasks: breast density classification from full-field digital mammography and BPE classification from contrast-enhanced mammography.Main results.Results demonstrate that reader embeddings improve both individual and consensus-level performance, and calibrated embeddings enable flexible, low-cost personalization. Reader-specific embeddings of dimension 32 improved the mean classification accuracy across readers, increasing from 76.4% to 84.4% for breast density, and from 65.1% to 72.1% for BPE, compared to the proposed baseline method.Significance.By disentangling stable image features from reader-specific decision tendencies, ReaderAdaptNet provides a parameter-efficient, interpretable route topersonalization(alignment to a given reader) orunification(alignment to an institutional standard) under real-world variability.
16 May 2026
Read appraisal →Biomedical physics & engineering express
Evaluating the impact of reconstruction algorithms on digital breast tomosynthesis system optimization for microcalcification detection
Early detection of microcalcifications in digital breast tomosynthesis (DBT) depends on optimizing both image reconstruction algorithms and system design parameters. Thisin-silicostudy evaluates the combined effects of x-ray tube motion, angular dose distribution, and detector characteristics on reconstruction algorithms for microcalcification detectability. A synthetic breast phantom containing 127 microcalcification clusters was generated and imaged under four acquisition conditions varying in tube motion (continuous vs. step-and-shot), angular dose distribution (uniform vs. non-uniform), and detector performance (noise level and pixel size). Projection data were reconstructed using filtered back-projection (FBP) and the simultaneous algebraic reconstruction technique (SART). Detection performance was assessed using a 2D Filtered Channel Observer in a multi-reader, multi-case study. Evaluation metrics included signal-to-noise ratio and area under the receiver operating characteristic (ROC) curve. Results show that SART performs best under step-and-shot acquisition by minimizing motion blur and enhancing fine detail, while FBP benefits more from non-uniform angular dose distributions. Detector improvements-lower noise and finer resolution-enhance detectability across all configurations, with FBP showing the largest gains. These findings highlight the interdependence of reconstruction algorithm, tube motion, dose strategy, and detector design in DBT system optimization. This study provides a detailed investigation into classical reconstruction strategies and establishes a methodological baseline for future DBT optimization research, offering actionable guidance for acquisition protocol design and hardware development.
24 Apr 2026
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