Background and PurposeWe aimed to test whether synthetic T1-weighted imaging derived from a post-contrast Quantitative Transient-state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions.MethodsThe analysis included 141 patients who underwent a 3 Tesla-MRI brain exam with intravenous contrast media administration, with the post-contrast acquisition protocol comprising a three-dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1-weighted images were generated from QTI-derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post-contrast T1-weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared.ResultsUsing conventional imaging as a reference, synthetic T1-weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1-weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging.ConclusionsQTI-derived post-contrast synthetic T1-weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials.

Detection of pathological contrast enhancement with synthetic brain imaging from quantitative multiparametric MRI

Donatelli, Graziella;Migaleddu, Gianmichele;Cecchi, Paolo;D'Amelio, Claudio;Cosottini, Mirco
2024-01-01

Abstract

Background and PurposeWe aimed to test whether synthetic T1-weighted imaging derived from a post-contrast Quantitative Transient-state Imaging (QTI) acquisition enabled revealing pathological contrast enhancement in intracranial lesions.MethodsThe analysis included 141 patients who underwent a 3 Tesla-MRI brain exam with intravenous contrast media administration, with the post-contrast acquisition protocol comprising a three-dimensional fast spoiled gradient echo (FSPGR) sequence and a QTI acquisition. Synthetic T1-weighted images were generated from QTI-derived quantitative maps of relaxation times and proton density. Two neuroradiologists assessed synthetic and conventional post-contrast T1-weighted images for the presence and pattern of pathological contrast enhancement in intracranial lesions. Enhancement volumes were quantitatively compared.ResultsUsing conventional imaging as a reference, synthetic T1-weighted imaging was 93% sensitive in revealing the presence of contrast enhancing lesions. The agreement for the presence/absence of contrast enhancement was almost perfect both between readers (k = 1 for both conventional and synthetic imaging) and between sequences (k = 0.98 for both readers). In 91% of lesions, synthetic T1-weighted imaging showed the same pattern of contrast enhancement visible in conventional imaging. Differences in enhancement pattern in the remaining lesions can be due to the lower spatial resolution and the longer acquisition delay from contrast media administration of QTI compared to FSPGR. Overall, enhancement volumes appeared larger in synthetic imaging.ConclusionsQTI-derived post-contrast synthetic T1-weighted imaging captures pathological contrast enhancement in most intracranial enhancing lesions. Further comparative studies employing quantitative imaging with higher spatial resolution is needed to support our data and explore possible future applications in clinical trials.
2024
Donatelli, Graziella; Migaleddu, Gianmichele; Cencini, Matteo; Cecchi, Paolo; D'Amelio, Claudio; Peretti, Luca; Buonincontri, Guido; Tosetti, Michela;...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1242323
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