The Knowledge Hub for Medical Imaging Professionals: Transforming Radiology with Structured Reporting, Data-Driven Approaches and Multicentric Research

Access breakthrough research, innovative case studies and collaborative projects advancing radiology worldwide. Dive into our activities and product updates, and learn who we are as a company and as a team.

Doctors looking at MRI scans to evaluate a glioblastoma.

Optimizing Glioblastoma Imaging: Enhancing MRI Efficiency and Quality with Deep Learning

This study investigates the use of deep learning (DL) to optimize MRI protocols for glioblastoma patients, aiming to reduce scan time and improve image quality.

Glioblastomas are aggressive brain tumors requiring frequent MRI monitoring, which can be challenging due to lengthy scan times and motion artifacts. Traditional methods to shorten scan times, like parallel acquisition techniques (PAT) and compressed sensing (CS), have limitations such as reduced signal-to-noise ratio and overly smooth images.

The study, involving 33 patients, found that DL-optimized MRI sequences reduced scan time by 30% while enhancing image quality and maintaining diagnostic accuracy. These improvements are particularly beneficial for patients who struggle with lengthy MRI procedures, offering a promising advancement in glioblastoma care.

Read more about the study here.

Related Resources

Related Resources

RECIST measurements of a patient with uveal melanoma and hepatic metastases

University Hospital Tuebingen: Study compares the Efficacy of SIRT and CS-PHP in Uveal Melanoma with Hepatic Metastasis

A study[1] conducted by researchers at University Hospital Tuebingen retrospectively compared the efficacy of two liver-targeted therapies,…

A picture of workshop participants  next to a positive review for the ESOI-EORTC RECIST Workshop in Munich

ESOI-EORTC Workshop on Imaging in Assessing Response to Cancer Therapy - A Participant's Perspective

Following the conclusion of the ESOI-EORTC Workshop on Imaging in Assessing Response to Cancer Therapy in Munich, we were keen to gather feedback…

A diagram and a CT scan visualizing tumor growth rate in patients with refractory or relapsed lymphoma

Study with mint Lesion™ Uncovers the Impact of Tumor Growth Rate in Predicting the Efficacy of CAR-T-cell Therapy in Lymphoma Patients

A recent study conducted by Dr. med. Michael Winkelmann and his colleagues at the LMU Klinikum München explored the association between TGR dynamics…