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RESEARCH PRODUCT
Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality.
Sebastian GassenmaierDominik NickelAhmed E. OthmanAhmed E. OthmanSaif AfatJudith HerrmannMahmoud Mostaphasubject
Malemedicine.medical_specialtyImage qualityLesionDeep LearningProstateMedicineHumansRadiology Nuclear Medicine and imagingAgedRetrospective Studiesmedicine.diagnostic_testbusiness.industryDeep learningProstatic NeoplasmsMagnetic resonance imagingRetrospective cohort studyGeneral MedicineMiddle AgedMagnetic Resonance Imagingmedicine.anatomical_structureAcquisition timeArtificial intelligenceRadiologymedicine.symptombusinessT2 weighteddescription
Abstract Purpose To introduce a novel deep learning (DL) T2-weighted TSE imaging (T2DL) sequence in prostate MRI and investigate its impact on examination time, image quality, diagnostic confidence, and PI-RADS classification compared to standard T2-weighted TSE imaging (T2S). Method Thirty patients who underwent multiparametric MRI (mpMRI) of the prostate due to suspicion of prostatic cancer were included in this retrospective study. Standard sequences were acquired consisting of T1- and T2-weighted imaging and diffusion-weighted imaging as well as the novel T2DL. Axial acquisition time of T2S was 4:37 min compared to 1:38 min of T2DL. Two radiologists independently evaluated all imaging datasets in a blinded reading regarding image quality, lesion detectability, and diagnostic confidence using a Likert-scale ranging from 1 to 4 with 4 being the best. T2 score as well as PI-RADS score were obtained for the most malignant lesion. Results Mean patient age was 65 ± 11 years. Noise levels and overall image quality were rated significantly superior by both readers with a median of 4 in T2DL compared to a median of 3 in T2S (all p Conclusions Deep learning axial T2w TSE imaging of the prostate is feasible with reduction of examination time of 65 % compared to standard imaging and improvement of image quality and lesion detectability.
year | journal | country | edition | language |
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2021-04-01 | European journal of radiology |