Simon S Martin1, Sophia Pfeifer1, Julian L Wichmann2, Moritz H Albrecht1,3, Doris Leithner1, Lukas Lenga1, Jan-Erik Scholtz1,4, Thomas J Vogl1, Boris Bodelle1. 1. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany. 2. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60590, Frankfurt, Germany. docwichmann@gmail.com. 3. Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA. 4. Cardiac MR PET CT Program, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA.
Abstract
PURPOSE: The aim of this study was to evaluate the impact of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique on quantitative and qualitative image analysis in patients with gastrointestinal stromal tumors (GISTs) at dual-energy computed tomography (DECT) of the abdomen. METHODS: Forty-five DECT datasets of 21 patients (14 men; 63.7 ± 9.2 years) with GISTs were reconstructed with the standard linearly blended (M_0.6) and VMI+ and traditional virtual monoenergetic (VMI) algorithm in 10-keV increments from 40 to 100 keV. Attenuation measurements were performed in GIST lesions and abdominal metastases to calculate objective signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Five-point scales were used to evaluate overall image quality, lesion delineation, image sharpness, and image noise. RESULTS: Quantitative image parameters peaked at 40-keV VMI+ series (SNR 27.8 ± 13.0; CNR 26.3 ± 12.7), significantly superior to linearly blended (SNR 16.8 ± 7.3; CNR 13.6 ± 6.9) and all VMI series (all P < 0.001). Qualitative image parameters were highest for 60-keV VMI+ reconstructions regarding overall image quality and image sharpness (median 5, respectively; P ≤ 0.023). Qualitative assessment of lesion delineation peaked in 40 and 50-keV VMI+ series (median 5, respectively). Image noise was superior in 90 and 100-keV VMI and VMI+ reconstructions (all medians 5). CONCLUSIONS: Low-keV VMI+ reconstructions significantly increase SNR and CNR of GISTs and improve quantitative and qualitative image quality of abdominal DECT datasets compared to traditional VMI and standard linearly blended image series.
PURPOSE: The aim of this study was to evaluate the impact of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique on quantitative and qualitative image analysis in patients with gastrointestinal stromal tumors (GISTs) at dual-energy computed tomography (DECT) of the abdomen. METHODS: Forty-five DECT datasets of 21 patients (14 men; 63.7 ± 9.2 years) with GISTs were reconstructed with the standard linearly blended (M_0.6) and VMI+ and traditional virtual monoenergetic (VMI) algorithm in 10-keV increments from 40 to 100 keV. Attenuation measurements were performed in GIST lesions and abdominal metastases to calculate objective signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Five-point scales were used to evaluate overall image quality, lesion delineation, image sharpness, and image noise. RESULTS: Quantitative image parameters peaked at 40-keV VMI+ series (SNR 27.8 ± 13.0; CNR 26.3 ± 12.7), significantly superior to linearly blended (SNR 16.8 ± 7.3; CNR 13.6 ± 6.9) and all VMI series (all P < 0.001). Qualitative image parameters were highest for 60-keV VMI+ reconstructions regarding overall image quality and image sharpness (median 5, respectively; P ≤ 0.023). Qualitative assessment of lesion delineation peaked in 40 and 50-keV VMI+ series (median 5, respectively). Image noise was superior in 90 and 100-keV VMI and VMI+ reconstructions (all medians 5). CONCLUSIONS: Low-keV VMI+ reconstructions significantly increase SNR and CNR of GISTs and improve quantitative and qualitative image quality of abdominal DECT datasets compared to traditional VMI and standard linearly blended image series.
Authors: Tommaso D'Angelo; Giuseppe Cicero; Silvio Mazziotti; Giorgio Ascenti; Moritz H Albrecht; Simon S Martin; Ahmed E Othman; Thomas J Vogl; Julian L Wichmann Journal: Br J Radiol Date: 2019-04-09 Impact factor: 3.039
Authors: Carlo N De Cecco; Damiano Caruso; U Joseph Schoepf; Domenico De Santis; Giuseppe Muscogiuri; Moritz H Albrecht; Felix G Meinel; Julian L Wichmann; Philip F Burchett; Akos Varga-Szemes; Douglas H Sheafor; Andrew D Hardie Journal: Eur Radiol Date: 2018-02-19 Impact factor: 5.315
Authors: Simon S Martin; Franziska Trapp; Julian L Wichmann; Moritz H Albrecht; Lukas Lenga; James Durden; Christian Booz; Thomas J Vogl; Tommaso D'Angelo Journal: Eur Radiol Date: 2018-11-28 Impact factor: 5.315
Authors: Andra-Iza Iuga; Jonas Doerner; Florian Siedek; Stefan Haneder; Jonathan Byrtus; Julian A Luetkens; David Maintz; Tilman Hickethier Journal: Medicine (Baltimore) Date: 2019-08 Impact factor: 1.817
Authors: Scherwin Mahmoudi; Marvin Lange; Lukas Lenga; Ibrahim Yel; Vitali Koch; Christian Booz; Simon Martin; Simon Bernatz; Thomas Vogl; Moritz Albrecht; Jan-Erik Scholtz Journal: BJR Open Date: 2022-05-10
Authors: Simon S Martin; Jetlir Kolaneci; Rouben Czwikla; Christian Booz; Leon D Gruenewald; Moritz H Albrecht; Zachary M Thompson; Lukas Lenga; Ibrahim Yel; Thomas J Vogl; Julian L Wichmann; Vitali Koch Journal: Diagnostics (Basel) Date: 2022-07-10