Name
Title | ||
|---|---|---|
CONTRAST-ENHANCED BRAIN MRI SYNTHESIS WITH DEEP LEARNING: KEY INPUT MODALITIES AND ASYMPTOTIC PERFORMANCE |
Abstract | ||
|---|---|---|
Contrast-enhanced medical images offer vital insights for the accurate diagnosis, characterization and treatment of tumors, and are routinely used worldwide. Acquiring such images requires to inject the patient intravenously with a gadolinium-based contrast agent (GBCA). Although GBCAs are considered safe, recent concerns about their accumulation in the body tilted the medical consensus towards a more parsimonious usage. Focusing on the case of brain magnetic resonance imaging, this paper proposes a deep learning method that synthesizes virtual contrast-enhanced T1 images as if they had been acquired after the injection of a standard 0.100 mmol/kg dose of GBCA, taking as inputs complementary imaging modalities obtained either after a reduced injection at 0.025 mmol/kg or without any GBCA involved. The method achieves a competitive structural similarity index of 94.2%. Its asymptotic performance is estimated, and the most important input modalities are identified. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISBI48211.2021.9434029 | 2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | DocType | ISSN |
Brain MRI, gadolinium-based contrast agents (GBCA), low-dose imaging, virtual enhancement | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexandre Bône | 1 | 0 | 0.34 |
| Samy Ammari | 2 | 0 | 0.34 |
| Jean-Philippe Lamarque | 3 | 0 | 0.68 |
| Mickael Elhaik | 4 | 0 | 0.34 |
| Emilie Chouzenoux | 5 | 202 | 26.37 |
| François Nicolas | 6 | 0 | 0.34 |
| Philippe Robert | 7 | 0 | 0.34 |
| Corinne Balleyguier | 8 | 0 | 0.34 |
| Nathalie Lassau | 9 | 0 | 0.34 |
| Marc-Michel Rohé | 10 | 0 | 0.34 |