Meeting Banner
Abstract #2113

Deep learning enabled fast free-breathing stack-of-stars multiparameter mapping for fully quantitative analysis of prostate carcinoma.

Ha Young Kim1,2, Carolin M. Pirkl2, Rolf F. Schulte2, Pablo Garcia-Polo3, Xinzeng Wang4, Matteo Cencini5, Veronika Spieker1,6, Sonia Gines7, Arnaud Guidon8, Michela Tosetti9, Luis Marti-Bonmati7, Julia A. Schnabel1,6,10, and Marion I. Menzel2,11
1Technical University of Munich, Munich, Germany, 2GE HealthCare, Munich, Germany, 3GE HealthCare, Madrid, Spain, 4GE HealthCare, Houston, TX, United States, 5INFN, Sezione di Pisa, Pisa, Italy, 6Helmholtz Munich, Munich, Germany, 7Hospital Universitario y Politécnico La Fe, Valencia, Spain, 8GE HealthCare, Boston, MA, United States, 9IRCCS Stella Maris, Pisa, Italy, 10King's College London, London, United Kingdom, 11Technische Hochschule Ingolstadt, Ingolstadt, Germany

Synopsis

Keywords: Quantitative Imaging, Prostate, cancer

Motivation: The diagnosis of prostate cancer continues to be highly qualitative, resulting in reduced diagnostic precision.

Goal(s): We present fast, quantitative transient-state T1-T2 mapping of the prostate with enhanced image quality on a larger prostate cancer patient cohort, along with an evaluation of quantitative parameters on tissue and lesion heterogeneities of the prostate.

Approach: (i) Incorporation of deep learning reconstruction with quantitative transient-state imaging combined with stack-of-stars based acquisition. (ii) Quantitative parameter analysis using unsupervised K-means clustering.

Results: (i) Significant improvement of SNR and image quality using deep learning reconstruction. (ii) Identification of different tissues and lesions using quantitative T1-T2 relaxation times.

Impact: Quantitative transient-state imaging combined with deep learning-based reconstruction provides high image quality T1 and T2 maps, enabling a fully quantitative evaluation of prostate cancer diagnosis with the potential of improving the prostate diagnosis pipeline.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords