Sebastian Gassenmaier1, Saif Afat1, Dominik Nickel2, Mahmoud Mostapha3, Judith Herrmann1, Ahmed E Othman4. 1. Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany. 2. MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany. 3. Digital Technology & Innovation, Siemens Medical Solutions USA, Inc., Princeton, NJ, USA. 4. Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Tuebingen, Germany; Department of Neuroradiology, University Medical Centre, Johannes Gutenberg University Mainz, 55131 Mainz, Germany. Electronic address: ahmed.e.othman@googlemail.com.
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 < 0.001). Lesion detectability was also rated higher in T2DL by both readers with a median of 4 versus a median of 3 in T2S (p = 0.005 and <0.001, respectively). There was no difference regarding PI-RADS scoring between T2DL and T2S affecting patient management. 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.
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 < 0.001). Lesion detectability was also rated higher in T2DL by both readers with a median of 4 versus a median of 3 in T2S (p = 0.005 and <0.001, respectively). There was no difference regarding PI-RADS scoring between T2DL and T2S affecting patient management. 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.
Authors: Daniel Wessling; Judith Herrmann; Saif Afat; Dominik Nickel; Ahmed E Othman; Haidara Almansour; Sebastian Gassenmaier Journal: Tomography Date: 2022-07-06
Authors: Francesco Giganti; Veeru Kasivisvanathan; Alex Kirkham; Shonit Punwani; Mark Emberton; Caroline M Moore; Clare Allen Journal: Br J Radiol Date: 2021-07-08 Impact factor: 3.039
Authors: Sebastian Gassenmaier; Thomas Küstner; Dominik Nickel; Judith Herrmann; Rüdiger Hoffmann; Haidara Almansour; Saif Afat; Konstantin Nikolaou; Ahmed E Othman Journal: Diagnostics (Basel) Date: 2021-11-24