Non-invasive MRI tools for liver fat evaluation are essential for reducing biopsies and enabling early, repeatable assessment of hepatic steatosis. Yet, despite their clinical value, these tools are rarely integrated into radiologists’ workflows and are often not optimized for real-world imaging conditions. As a result, their potential remains largely untapped — not due to lack of capability, but because they are not designed around how liver imaging is performed.

Embedded Workflow Integration

Rather than creating a detached, exclusive solution, we designed μetaRadiology to be embedded directly into PACS, where radiologists already work. By embedding liver fat evaluation into the reading flow, it becomes part of routine interpretation instead of an extra step, reducing friction and supporting consistent clinical use.

Validation is equally critical. The algorithm has been optimized and tested on in-house phantoms, ensuring consistency and robustness before deployment — turning potential into reliable, real-world performance. This added value of phantom validation is also why μetaRadiology is used in research-driven centers such as ISGlobal The emphasis is not only on flexible functionality but also on delivering liver fat evaluation that is reproducible, technically accurate, and aligned with clinical reality, enabling meaningful analysis without unnecessary complexity.

The Clinical Context: NAFLD and the Limitations of Biopsy

Beyond workflow and validation, liver fat evaluation must be viewed in the broader context of liver disease management. Non-alcoholic fatty liver disease (NAFLD) is highly prevalent in both adults and children and represents a growing global health burden. While liver biopsy has historically beenconsidered the reference standard for evaluating hepatic steatosis, it is accompanied by significant limitations. It is invasive, expensive, and subject to sampling variability, making it unsuitable for routine monitoring and longitudinal assessment [1–3].

MRI-PDFF: A Reliable Non-Invasive Alternative

MRI-derived proton density fat fraction (PDFF) has therefore emerged as a reliable non-invasive alternative for the detection and quantification of liver fat. Multiple studies have demonstrated strong correlation between MRI-PDFF and histological grading of steatosis, supporting its role in both clinical and research settings [4–5]. Importantly, non-invasive assessment enables repeatable measurements over time, which is critical for monitoring disease progression and evaluating treatment response in NAFLD [6–10, 11–13].

Early Evaluation and Clinical Decision-Making

Early and reliable evaluation of liver fat plays a key role in clinical decision-making. Monitoring changes in steatosis can inform treatment strategies, support preventive care, and help avoid adverse outcomes associated with disease progression [6–10, 14–16]. This is particularly relevant in populations where repeated invasive procedures are neither practical nor ethically desirable.

The Challenge of Consistency and Trust

However, the clinical value of MRI-based liver fat evaluation depends heavily on consistency and trust. Variability in acquisition techniques, reconstruction methods, and post-processing approaches can affect measurement reliability, especially when tools are not optimized or standardized [17–22]. From a product perspective, this variability undermines confidence and limits adoption — regardless of the underlying scientific validity [23–26].

This is why optimization and validation must be foundational rather than optional. Phantom-based optimization allows algorithms to be calibrated against known reference values under controlled conditions before being exposed to real- world variability. In practice, this approach supports reproducibility and consistency, which are essential for longitudinal assessment and clinical confidence.

Clinical Application: Color-Coded Fat Fraction Maps

Figure 1: Color-coded liver fat fraction (%FF) maps generated using μetaRadiology for three adult patients (a–c) and one pediatric patient (d), with patient sex as follows: (a) female, (b) male, (c) male, and (d) female. Pseudo-coloring is based on the NIH color palette, with dark colors representing %FF <5 (Grade 0, Normal), and spectral colors from violet to vivid red representing %FF 5.1–50. Mean quantitative %FF values were 6.2%, 3.3%, 17.2%, and 22.4% for patients (a), (b), (c), and (d), corresponding respectively to Grade 1 (Mild), Grade 0 (Normal), Grade 2 (Moderate), and Grade 3 (Heavy) steatosis.

Embedded Delivery: Removing Barriers to Adoption

Equally important is how evaluation is delivered. When liver fat assessment requires external software or fragmented workflows, its use becomes limited. Embedding evaluation directly into PACS removes these barriers, allowing non- invasive assessment to become part of routine imaging rather than a specialized add-on. In this setting, consistency improves naturally, because evaluation is aligned with how radiologists already work.

Looking ahead, the future of imaging analytics is unlikely to be dominated by monolithic platforms. Instead, it will be shaped by focused, optimized tools that integrate seamlessly into existing systems and support better decisions without adding friction. Liver fat evaluation is a clear example of why this approach matters.

Beyond Measurement: Supporting Better Patient Care

When non-invasive assessment is embedded, validated, and designed around real clinical workflows, it moves beyond measurement alone. It becomes a meaningful signal, one that supports monitoring, decision-making, and ultimately better patient care.

1. Embedded Integration: Seamlessly integrated into existing PACS workflows
2. Validated Performance: Optimized and tested with phantom-based validation
3. Clinical Workflow: Designed around how radiologists actually work
4. Better Care: Supports monitoring, decisions, and patient outcomes

μetaRadiology: Beyond Images to Meaningful Information

μetaRadiology reflects the essence of its name. “μeta” — meaning “beyond” — captures the philosophy of going beyond images to deliver meaningful information. Like metadata complements data, μetaRadiology complements radiology by mapping insights, improving conspicuity, enhancing understanding of disease, and supporting clinical decisions. It turns measurement into understanding, bringing imaging closer to patient care.

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