Skip to content Provide accessibility feedback
By using the Volpara website you accept our use of cookies. Here’s our privacy statement.
Breast Health
Lung Health
Magnifying glass Search

38 m+

Mammography and tomosynthesis images have been anonymized and analyzed by Volpara Health.


Mammography technologists rely on Volpara Health to improve mammogram quality.

Automated Image Evaluation

The primary focus of every TruPGMI image evaluation (of both standard views) is to assess whether all breast tissue is imaged. As a rule, irrespective of the view, all breast tissue must be imaged, inferring that fat tissue should be visualized posterior to glandular tissue. Obviously, the same rules cannot apply precisely to partial views, magnified views, and views with special view modifiers. 

The TruPGMI method first identifies positioning deficiencies and then categorizes each image as Perfect ( P ), Good ( G ), Moderate ( M ), or Inadequate ( I ), resulting in an overall assessment of its quality from a positioning perspective. 

This automated approach is based on best practices from around the world (including the UK PGMI standard) and enables breast imaging centers to achieve a high standard of mammographic image quality;  to provide an objective training program to advance technologist performance; and more easily prepare for external quality audits such as the Food and Drug Administration (FDA) Enhancing Quality Using the Inspection Program (EQUIP) initiative. 

The techs love, love, love, Volpara. I catch them looking at it all the time and their scores are proof.

– Denise Foster RT (R) (M), Lead Mammography Technologist at Southern IL Healthcare / The Breast Center of Carbondale

Products featuring TruPGMI™

Volpara Analytics


Monitor your team’s performance with automated image quality metrics.

Volpara Live


Get fast imaging feedback for on-the job training.


You might be interested in...

14 Jul 2020

Monitoring Team Performance in Positioning and Compression Metrics

8 Mar 2021

Study using Volpara Health’s AI-powered image quality scoring wins Magna cum Laude award at ECR 2021