Stay Informed: Transforming Radiology with Structured Reporting and Data-Driven Approaches

Dive into our activities, projects, and product updates. Catch on the latest industry news and learn who we are as a company and as a team.

New mint Lesion 3.4 Software presented at RSNA 2017

With mint Lesion 3.4, reproducible assessment and structured reporting of significant image observations are easier than ever.

Numerous improvements and new functionalities related to the image data handling and display streamline the image assessment. As such, for instance, a refined image series browser provides instantaneous access to image combined with a single-click customizability of an image hanging. Next to the existing cross reference lines, a new cross-hair tool allows for easy spatial navigation, which includes the picking of lesions in a rotating 3D MIP view of PET image data.

An area of constant refinements and enhancements is also the support for specific reading profiles targeting specific read tasks in Radiology. Usually, the referrals require certain critical aspects to be addressed in a clear and reproducible manner as they correlate with tipping points for further decisions with regards to diagnosis and treatment. Mint Lesion implements the read tasks and related international guidelines and criteria in detail and assures the adherence and consistency of radiological reports. In mint Lesion 3.4, head and neck tumor entities have been added and all reading profiles with TNM criteria involvement have been updated to the latest version of TNM. Furthermore, new reading profiles have been added. As one example for this, a dedicated reading profile to assess the indication of a surgical treatment of pancreatic cancer facilitates the reproducible reporting of complex vascular involvements with the help of automatically generated, illustrative 3D drawings in a structured report.

Related Resources

Related Resources

Three important sequences (FLAIR, T2, T1 with contrast agent) in the assessment of glioblastoma

University Hospital Tübingen: Advancing MRI Efficiency in Glioblastoma Care with Deep Learning

This study explores the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients. Glioblastomas, known for being the most…

Picture of Dr. Maurice Heimer, radiology resident

Insights into the BORN Project: Development and Successes with Dr. Maurice Heimer

The BORN-project of the Bavarian Center for Cancer Research (BZKF) is making swift progress in its second funding phase. A key aspect of the project…

A Closer Look at the BZKF BORN-Project: Interview with Dr. Maurice Heimer from the Clinic and Polyclinic for Radiology at the LMU Klinikum

The Bavaria-wide Oncological Radiology Network (BORN) is now in its second funding phase and is making rapid progress. The project was initiated in…