Quantitative Imaging


Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal. Quantitative imaging includes the development, standardization, and optimization of anatomical, functional, and molecular imaging acquisition protocols, data analyses, display methods, and reporting structures. These features permit the validation of accurately and precisely obtained image-derived metrics with anatomically and physiologically relevant parameters, including treatment response and outcome, and the use of such metrics in research and patient care.

Quantitative imaging (QI) is becoming an increasingly common tool in modern radiology practice, advancing from research trials to clinical reading rooms. Today, methods that quantify imaging features assist in the clinical assessment of many patients, serving as biomarkers for disease states as diverse as brain ischemia, interstitial lung disease, and colorectal cancer. Because the potential impact of QI on patient care and on clinical outcomes is so great, the Radiological Society of North America has committed considerable resources to standardizing QI, most recently with the Quantitative Imaging Biomarkers Alliance (QIBA). The Association of University Radiologists' leadership, QIBA participants, and many others in the radiology community view QI as important to the future of radiology. Because it is anticipated that most practicing radiologists will eventually implement some QI tools to meet the specific patient care needs of their referring clinicians, it is important for radiologists of all subspecialties and practice types to become familiar with the various strengths and limitations of QI.

Quantitative imaging is increasingly applied in modern radiology practice, assisting in the clinical assessment of many patients and providing a source of biomarkers for a spectrum of diseases. QI is commonly used to inform patient diagnosis or prognosis, determine the choice of therapy, or monitor therapy response. Because most radiologists will likely implement some QI tools to meet the patient care needs of their referring clinicians, it is important for all radiologists to become familiar with the strengths and limitations of QI. The Association of University Radiologists Radiology Research Alliance Quantitative Imaging Task Force has explored the clinical application of QI and summarizes its work in this review. We provide an overview of the clinical use of QI by discussing QI tools that are currently employed in clinical practice, clinical applications of these tools, approaches to reporting of QI, and challenges to implementing QI. It is hoped that these insights will help radiologists recognize the tangible benefits of QI to their patients, their referring clinicians, and their own radiology practice.

QI currently has important clinical applications in ultrasound, computerized tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine, including position emission tomography (PET), although theoretically can be applied to any digital imaging modality. QI is enhanced by volumetric data sets, which facilitate assessments of morphological, parametric, functional, and other quantitative features.


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