Investigative Radiology

Empiric Switching of Gadolinium-Based Contrast Agents in Patients With History of Previous Immediate Hypersensitivity Reaction to GBCA: A Prospective Single-Center, Single-Arm Efficacy Trial Background Breakthrough hypersensitivity reactions (HRs) to gadolinium-based contrast agent (GBCA) occur in 40% of patients despite corticosteroid premedication. Other strategies to reduce HRs are not well studied. Objective The aim of this study was to prospectively evaluate HR rate to GBCA among patients with history of HR to GBCA, empirically given an alternative GBCA prior to repeat administration. Materials and Methods From September 2019 to September 2020, patients with prior HR to GBCA received 13-hour oral corticosteroid and diphenhydramine premedication prescription with switching of GBCA to gadoterate (previously unavailable at our institution before September 2019). Power analysis (α error, 0.05; β error, 0.80) determined 21 patients were required. Patients were evaluated under a quality assurance waiver from the institutional review board. A radiologist documented the nature of initial HR and inciting GBCA, premedication received, incidence, and severity of breakthrough HR. Results After exclusions, we evaluated 26 patients with mild (92.3% [24/26]) or moderate (7.7% [2/26]) HR to gadobutrol (53.8% [14/26]), gadoxetate (3.8% [1/26]), and gadopentetate (3.8% [1/26]). In 38.5% (10/26), inciting GBCA was unknown but was likely gadobutrol or gadopentetate based on availability. There were 22 females. The mean patient age was 52.1 ± 15.8 years. From 27 gadoterate administrations, 59.3% (16/27) patients received corticosteroid and diphenhydramine premedication, 11.1% (3/27) received only diphenhydramine, and 29.6% (8/27) with no premedication. Hypersensitivity reaction rate after empiric switching to gadoterate was 3.7% (1 mild reaction; 95% confidence interval [CI], 0.09%–18.9%) overall with no difference in patients with (6.3% [1/16]; 95% CI, 0.15%–28.7%) or without (0%; [0/11] upper bound 95% CI, 25.0%) corticosteroid premedication. Conclusions In this prospective single-arm study, empirically switching GBCA to gadoterate in patients with prior HR to GBCA substantially reduced the expected rate of subsequent HRs in patients with and without the use of corticosteroid premedication. Implications for PatientCare Empirically switching GBCAs, with or without the use of corticosteroid premedication, can substantially reduce the rate of hypersensitivity breakthrough reactions. Received for publication September 22, 2020; and accepted for publication, after revision, November 5, 2020. Conflicts of interest and sources of funding: none declared. Correspondence to: Nicola Schieda, MD, FRCPC, Department of Medical Imaging, The Ottawa Hospital, 1053 Carling Ave, Room C159, Ottawa, Ontario K1Y 4E9, Canada. E-mail: nschieda@toh.ca. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Steadily Increasing Inversion Time Improves Blood Suppression for Free-Breathing 3D Late Gadolinium Enhancement MRI With Optimized Dark-Blood Contrast Objectives Free-breathing 3-dimensional (3D) late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) techniques with high isotropic resolution and dark-blood contrast may optimize the delineation of myocardial scar patterns. The extended acquisition times required for such scans, however, are paralleled by a declining contrast agent concentration. Consequently, the optimal inversion time (TI) is continuously increasing. We hypothesize that a steadily increasing (dynamic) TI can compensate for this effect and can lead to improved blood nulling to optimize the dark-blood contrast. Materials and Methods Fifty consecutive patients with previous cardiac arrhythmias, scheduled for high-resolution 3D LGE MRI, were prospectively enrolled between October 2017 and February 2020. Free-breathing 3D dark-blood LGE MRI with high isotropic resolution (1.6 × 1.6 × 1.6 mm) was performed using a conventional fixed TI (n = 25) or a dynamic TI (n = 25). The average increase in blood nulling TI per minute was obtained from Look-Locker scans before and after the 3D acquisition in the first fixed TI group. This average increment in TI was used as input to calculate the dynamic increment of the initial blood nulling TI value as set in the second dynamic TI group. Regions of interest were drawn in the left ventricular blood pool to assess mean signal intensity as a measure for blood pool suppression. Overall image quality, observer confidence, and scar demarcation were scored on a 3-point scale. Results Three-dimensional dark-blood LGE data sets were successfully acquired in 46/50 patients (92%). The calculated average TI increase of 2.3 ± 0.5 ms/min obtained in the first fixed TI group was incorporated in the second dynamic TI group and led to a significant decrease of 72% in the mean blood pool signal intensity compared with the fixed TI group (P < 0.001). Overall image quality (P = 0.02), observer confidence (P = 0.02), and scar demarcation (P = 0.01) significantly improved using a dynamic TI. Conclusions A steadily increasing dynamic TI improves blood pool suppression for optimized dark-blood contrast and increases observer confidence in free-breathing 3D dark-blood LGE MRI with high isotropic resolution. Received for publication September 29, 2020; and accepted for publication, after revision, October 25, 2020. Conflicts of interest and sources of funding: R.J.H., S.G., and J.E.W. acknowledge financial support from Stichting de Weijerhorst. R.J.H. was supported by an HS-BAFTA fellowship from the Cardiovascular Research Institute Maastricht (CARIM). J.S. and D.M.H. are employees of Philips Healthcare. J.E.W. receives institutional grants from Agfa Healthcare, Bard Medical, Bayer Healthcare, General Electric, Optimed, Philips Healthcare, and Siemens Healthineers. The other authors have no conflicts of interest to declare. Correspondence to: Robert J. Holtackers, MSc, Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, PO Box 5800, 6202 AZ Maastricht, the Netherlands. E-mail: rob.holtackers@mumc.nl. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Detection of U-87 Tumor Cells by RGD-Functionalized/Gd-Containing Giant Unilamellar Vesicles in Magnetization Transfer Contrast Magnetic Resonance Images Objectives The targeting of tumor cells and their visualization with magnetic resonance imaging (MRI) is an important task in biomedicine. The low sensitivity of this technique is a significant drawback and one that may hamper the detection of the imaging reporters used. To overcome this sensitivity issue, this work explores the synergy between 2 strategies: (1) arginine, glycine, aspartic acid peptide (RGD)-functionalized giant unilamellar vesicles (GUVs) loaded with Gd complexes to accumulate large amounts of MRI contrast agent at the targeting site; and (2) the use of magnetization transfer contrast (MTC), which is a sensitive MRI technique for the detection of Gd complexes in the tumor region. Materials and Methods Giant unilamellar vesicles were prepared using the gentle swelling method, and the cyclic RGD targeting moiety was introduced onto the external membrane. Paramagnetic Gd-containing complexes and the fluorescent probe rhodamine were both part of the vesicle membranes and Gd-complexes were also the payload within the inner aqueous cavity. Giant unilamellar vesicles that were loaded with the imaging reporters, but devoid of the RGD targeting moiety, were used as controls. U-87 MG human glioblastoma cells, which are known to overexpress the targets for RGD moieties, were used. In the in vivo experiments, U-87 MG cells were subcutaneously injected into nu/nu mice, and the generated tumors were imaged using MRI, 15 days after cell administration. Magnetic resonance imaging was carried out at 7 T, and T2W, T1W, and MTC/Z-spectra were acquired. Confocal microscopy images and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) were used for result validation. Results In vitro results show that RGD GUVs specifically bind to U-87 MG cells. Microscopy demonstrates that (1) RGD GUVs were anchored onto the external surface of the tumor cells without any internalization; (2) a low number of GUVs per cell were clustered at specific regions; and (3) there is no evidence for macrophage uptake or cell toxicity. The MRI of cell pellets after incubation with RGD GUVs and untargeted ctrl-GUVs was performed. No difference in T1 signal was detected, whereas a 15% difference in MT contrast is present between the RGD GUV–treated cells and the ctrl-GUV–treated cells. Magnetic resonance imaging scans of tumor-bearing mice were acquired before and after (t = 0, 4 hours and 24 hours) the administration of RGD GUVs and ctrl-GUVs. A roughly 16% MTC difference between the 2 groups was observed after 4 hours. Immunofluorescence analyses and ICP-MS analyses (for Gd-detection) of the explanted tumors confirmed the specific accumulation of RGD GUVs in the tumor region. Conclusions RGD GUVs seem to be interesting carriers that can facilitate the specific accumulation of MRI contrast agents at the tumor region. However, the concentration achieved is still below the threshold needed for T1w-MRI visualization. Conversely, MTC proved to be sufficiently sensitive for the visualization of detectable contrast between pretargeting and posttargeting images. Received for publication June 29, 2020; and accepted for publication, after revision, October 7, 2020. Conflicts of interest and sources of funding: The authors declare no conflicts of interest. Funding was received from the Italian Ministry of Research through FOE contribution to the Euro-BioImaging MultiModal Molecular Imaging Italian Node (www.mmmi.unito.it). Funding was also received from the University of Turin (G.F.). This research was performed in the framework of COST Action AC15209 (EURELAX). Correspondence to: Giuseppe Ferrauto, PhD, Department of Molecular Biotechnology and Health Sciences, Molecular Imaging Center, University of Turin, Via Nizza 52, 10126 Turin, Italy. E-mail: giuseppe.ferrauto@unito.it. Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.investigativeradiology.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Accelerated Isotropic Multiparametric Imaging by High Spatial Resolution 3D-QALAS With Compressed Sensing: A Phantom, Volunteer, and Patient Study Objectives The aims of this study were to develop an accelerated multiparametric magnetic resonance imaging method based on 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS) combined with compressed sensing (CS) and to evaluate the effect of CS on the quantitative mapping, tissue segmentation, and quality of synthetic images. Materials and Methods A magnetic resonance imaging system phantom, containing multiple compartments with standardized T1, T2, and proton density (PD) values; 10 healthy volunteers; and 12 patients with multiple sclerosis were scanned using the 3D-QALAS sequence with and without CS and conventional contrast-weighted imaging. The scan times of 3D-QALAS with and without CS were 5:56 and 11:11, respectively. For healthy volunteers, brain volumetry and myelin estimation were performed based on the measured T1, T2, and PD. For patients with multiple sclerosis, the mean T1, T2, PD, and the amount of myelin in plaques and contralateral normal-appearing white matter (NAWM) were measured. Simple linear regression analysis and Bland-Altman analysis were performed for each metric obtained from the datasets with and without CS. To compare overall image quality and structural delineations on synthetic and conventional contrast-weighted images, case-control randomized reading sessions were performed by 2 neuroradiologists in a blinded manner. Results The linearity of both phantom and volunteer measurements in T1, T2, and PD values obtained with and without CS was very strong (R2 = 0.9901–1.000). The tissue segmentation obtained with and without CS also had high linearity (R2 = 0.987–0.999). The quantitative tissue values of the plaques and NAWM obtained with CS showed high linearity with those without CS (R2 = 0.967–1.000). There were no significant differences in overall image quality between synthetic contrast-weighted images obtained with and without CS (P = 0.17–0.99). Conclusions Multiparametric imaging of the whole brain based on 3D-QALAS can be accelerated using CS while preserving tissue quantitative values, tissue segmentation, and quality of synthetic images. Received for publication August 10, 2020; and accepted for publication, after revision, October 3, 2020. Conflicts of interest and sources of funding: N.T. is an employee of GE Healthcare Japan. This work was supported by Japan Agency for Medical Research and Development under grant number JP19lk1010025h9902; JSPS KAKENHI grant numbers 19K17150, 19K17177, 18H02772, and 18K07692; Health, Labor and Welfare Policy Research Grants for Research on Region Medical; and a grant-in-aid for special research in subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan; Brain/MINDS beyond program from Japan Agency for Medical Research and Development grant numbers JP19dm0307024 and JP19dm0307101. Correspondence to: Akifumi Hagiwara, MD, PhD, Department of Radiology, Juntendo University School of Medicine, 2-1-1, Hongo, Bunkyo-ku, Tokyo, Japan, 113-8421. E-mail: a-hagiwara@juntendo.ac.jp. Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.investigativeradiology.com). This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study Objectives The aim of this study was to compare a diagnosis support system to detect COVID-19 pneumonia on chest radiographs (CXRs) against radiologists of various levels of expertise in chest imaging. Materials and Methods Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence–based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system. Results The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001). Conclusions The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels. Received for publication August 18, 2020; and accepted for publication, after revision, October 26, 2020. M.F., L.E., S.M., and A.C. contributed equally to this study. Conflicts of interest and sources of funding: none declared. Correspondence to: Lukas Ebner, MD, Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 10, Bern 3010, Switzerland. E-mail: lukas.ebner@insel.ch. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Pre-examinations Improve Automated Metastases Detection on Cranial MRI Objective The aim of this study was to assess the diagnostic value of inclusion of prediagnosis magnetic resonance imaging (MRI) and different MRI sequences when training a convolutional neural network (CNN) in detection of metastases from malignant melanoma (MM) on an annotated real-life cranial MRI dataset. Diagnostic performance was challenged by extracerebral-intracranial MM and by inclusion of MRI with varying sequence parameters. Materials and Methods Our local ethics committee approved this retrospective monocenter study. First, a dual-time approach was assessed, for which the CNN was provided sequences of the MRI that initially depicted new MM (diagnosis MRI) as well as of a prediagnosis MRI: inclusion of only contrast-enhanced T1-weighted images (CNNdual_ce) was compared with inclusion of also the native T1-weighted images, T2-weighted images, and FLAIR sequences of both time points (CNNdual_all). Second, results were compared with the corresponding single time approaches, in which the CNN was provided exclusively the respective sequences of the diagnosis MRI. Casewise diagnostic performance parameters were calculated from 5-fold cross-validation. Results In total, 94 cases with 494 MMs were included. Overall, the highest diagnostic performance was achieved by inclusion of only the contrast-enhanced T1-weighted images of the diagnosis and of a prediagnosis MRI (CNNdual_ce, sensitivity = 73%, PPV = 25%, F1-score = 36%). Using exclusively contrast-enhanced T1-weighted images as input resulted in significantly less false-positives (FPs) compared with inclusion of further sequences beyond contrast-enhanced T1-weighted images (FPs = 5/7 for CNNdual_ce/CNNdual_all, P < 1e-5). Comparison of contrast-enhanced dual and mono time approaches revealed that exclusion of prediagnosis MRI significantly increased FPs (FPs = 5/10 for CNNdual_ce/CNNce, P < 1e-9). Approaches with only native sequences were clearly inferior to CNNs that were provided contrast-enhanced sequences. Conclusions Automated MM detection on contrast-enhanced T1-weighted images performed with high sensitivity. Frequent FPs due to artifacts and vessels were significantly reduced by additional inclusion of prediagnosis MRI, but not by inclusion of further sequences beyond contrast-enhanced T1-weighted images. Future studies might investigate different change detection architectures for computer-aided detection. Received for publication September 1, 2020; and accepted for publication, after revision, October 12, 2020. Financial study support was provided by Guerbet. Conflicts of interest and sources of funding: A.R. received personal fees for consulting and talks (within the last 3 years) from Bayer, Guerbet, and Novartis, and financial study support from Bayer and Guerbet. K.D.H. received personal fees for talks from GE and financial study support from Bayer and Guerbet. The other authors declare no conflicts of interest. Correspondence to: Alexander Radbruch, MD, JD, Clinic for Diagnostic and Interventional Neuroradiology, Venusberg Campus 1, 53127 Bonn, Germany. E-mail: alexander.radbruch@ukbonn.de. Supplemental digital contents are available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.investigativeradiology.com). Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

The Macrocyclic Gadolinium-Based Contrast Agents Gadobutrol and Gadoteridol Show Similar Elimination Kinetics From the Brain After Repeated Intravenous Injections in Rabbits Objective The gadolinium (Gd) concentrations in the cerebellum and cerebrum of rabbits and the elimination kinetics were compared after repeated injection of the macrocyclic Gd-based contrast agents (GBCAs) gadobutrol and gadoteridol. Materials and Methods Male white New Zealand rabbits (2.4–3.1 kg) in 2 study groups (n = 21 each) received 3 injections of either gadobutrol or gadoteridol at 0.9 mmol Gd/kg within 5 days (total dose, 2.7 mmol Gd/kg). Animals in one control group (n = 9) received 3 injections of saline (1.8 mL/kg). After 2, 6, and 12 weeks, 7 animals from each study group and 3 from the control group were killed and the Gd concentrations in the cerebellum, cerebrum, in blood and in urine were determined by inductively coupled plasma mass spectrometry. The chemical species of excreted Gd in urine were determined by high pressure liquid chromatography. Results No significant (P > 0.05) differences in the Gd concentrations in the brain of rabbits were observed between the 2 macrocyclic GBCAs gadoteridol and gadobutrol at all time points. In the gadobutrol group, the mean Gd concentrations in the cerebellum and cerebrum decreased from 0.26 and 0.21 nmol Gd/g after 2 weeks, to 0.040 and 0.027 nmol Gd/g after 12 weeks, respectively, and in the gadoteridol group, from 0.25 and 0.21, to 0.037 and 0.023 nmol Gd/g, respectively. The plasma levels decreased from 0.11 and 0.13 nmol Gd/mL at 2 weeks for gadobutrol and gadoteridol to below the limit of quantification (<0.005 nmol Gd/mL) at 12 weeks. The urine concentration dropped in a biphasic course from 2 to 6 and from 6 to 12 weeks for both agents. The Gd excreted after 12 weeks was still present in the urine in the chemical form of the intact Gd complex for both agents. Conclusions Contrary to what had been reported in rats, no significant differences in the elimination kinetics from brain tissue in rabbits were observed after intravenous injection of multiple doses of the macrocyclic GBCAs gadobutrol and gadoteridol. Received for publication September 4, 2020; and accepted for publication, after revision, October 26, 2020. Conflicts of interest and sources of funding: all authors are employees of Bayer AG. Correspondence to: Thomas Frenzel, PhD, Bayer AG, Müllerstraße 178, 13353 Berlin, Germany. E-mail: thomas.frenzel1@bayer.com. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Application of a Novel Iterative Denoising and Image Enhancement Technique in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of the Abdomen: Improvement of Image Quality and Diagnostic Confidence Objectives The aim of this study was to investigate the impact of a novel iterative denoising and image enhancement technique in T1-weighted precontrast and postcontrast volume-interpolated breath-hold examination (VIBE) of the abdomen on image quality, noise levels, and diagnostic confidence without change of acquisition parameters. Materials and Methods Fifty patients were included in this retrospective, monocentric, institutional review board–approved study after clinically indicated magnetic resonance imaging of the abdomen including T1-weighted precontrast and postcontrast imaging. After acquisition of the standard VIBE (VIBES), images were processed with a novel reconstruction algorithm using the same raw data as for VIBES, resulting in a denoised and enhanced dataset (VIBEDE). Two different radiologists evaluated both datasets in a randomized order regarding sharpness of organs as well as vessels, noise levels, artifacts, overall image quality, and diagnostic confidence using a Likert scale ranging from 1 to 4 with 4 being the best. Furthermore, in the presence of focal liver lesions, the largest lesion was measured in the postcontrast dataset, and lesion detectability was analyzed using a Likert scale (1–4). Results Precontrast and postcontrast sharpness of organs and sharpness of vessels were rated significantly superior by both readers in VIBEDE with a median of 4 (interquartile range, 0) compared with VIBES with a median of 3 (1) (all P's < 0.0001). Precontrast and postcontrast noise levels were also rated superior by both readers in VIBEDE with a median of 4 (0) compared with VIBES with a median of 3 (1) for precontrast and a median of 3 (0) (median of 3 [1] for reader 2) for postcontrast imaging (all P's < 0.0001). Overall image quality was also rated higher with a median of 4 (0) in VIBEDE versus 3 (1) in VIBES (P < 0.0001). Twenty-seven imaging studies contained liver lesions. There was no difference regarding the number and localization between the readers and between VIBES and VIBEDE. Lesion detectability was rated by both readers significantly better in VIBEDE with a median of 4 (0) compared with a median of 4 (1) for reader 1 and a median of 3 (1) for reader 2 (P = 0.001 for reader 1; P < 0.001 for reader 2). Consequently, diagnostic confidence was also significantly superior in VIBEDE versus VIBES with a median of 4 (0) for both (P = 0.001). Interreader agreement resulted in a Cohen κ of 0.76 for precontrast analysis as well as of 0.76 for postcontrast analysis. Conclusions Application of a novel iterative denoising and image enhancement technique in T1-weighted VIBE precontrast and postcontrast imaging of the abdomen is feasible, providing superior image quality, noise levels, and diagnostic confidence. Received for publication September 28, 2020; and accepted for publication, after revision, October 18, 2020. Conflicts of interest and sources of funding: none declared. Correspondence to: Ahmed E. Othman, MD, Department of Diagnostic and Interventional Radiology, Eberhard-Karls-University Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany. E-mail: ahmed.e.othman@googlemail.com. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Diagnostic Confidence and Feasibility of a Deep Learning Accelerated HASTE Sequence of the Abdomen in a Single Breath-Hold Objective The aim of this study was to evaluate the feasibility of a single breath-hold fast half-Fourier single-shot turbo spin echo (HASTE) sequence using a deep learning reconstruction (HASTEDL) for T2-weighted magnetic resonance imaging of the abdomen as compared with 2 standard T2-weighted imaging sequences (HASTE and BLADE). Materials and Methods Sixty-six patients who underwent 1.5-T liver magnetic resonance imaging were included in this monocentric, retrospective study. The following T2-weighted sequences in axial orientation and using spectral fat suppression were compared: a conventional respiratory-triggered BLADE sequence (time of acquisition [TA] = 4:00 minutes), a conventional multiple breath-hold HASTE sequence (HASTES) (TA = 1:30 minutes), as well as a single breath-hold HASTE with deep learning reconstruction (HASTEDL) (TA = 0:16 minutes). Two radiologists assessed the 3 sequences regarding overall image quality, noise, sharpness, diagnostic confidence, and lesion detectability as well as lesion characterization using a Likert scale ranging from 1 to 4 with 4 being the best. Comparative analyses were conducted to assess the differences between the 3 sequences. Results HASTEDL was successfully acquired in all patients. Overall image quality for HASTEDL was rated as good (median, 3; interquartile range, 3–4) and was significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.001). Noise, sharpness, and artifacts for HASTEDL reached similar levels to BLADE (P ≤ 0.176) and were significantly superior to HASTEs (P < 0.001). Diagnostic confidence for HASTEDL was rated excellent by both readers and significantly superior to HASTEs (P < 0.001) and inferior to BLADE (P = 0.044). Lesion detectability and lesion characterization for HASTEDL reached similar levels to those of BLADE (P ≤ 0.523) and were significantly superior to HASTEs (P < 0.001). Concerning the number of detected lesions and the measured diameter of the largest lesion, no significant differences were found comparing BLADE, HASTES, and HASTEDL (P ≤ 0.912). Conclusions The single breath-hold HASTEDL is feasible and yields comparable image quality and diagnostic confidence to standard T2-weighted TSE BLADE and may therefore allow for a remarkable time saving in abdominal imaging. Received for publication September 8, 2020; and accepted for publication, after revision, October 12, 2020. Conflicts of interest and sources of funding: none declared. Disclaimer: The concepts and information presented in this article are based on research results that are not commercially available. Correspondence to: Ahmed Othman, MD, Department of Diagnostic and Interventional Radiology, University of Tuebingen, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany. E-mail: ahmed.e.othman@googlemail. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

Computed Tomography Angiography of the Aorta—Optimization of Automatic Tube Voltage Selection Settings to Reduce Radiation Dose or Contrast Medium in a Prospective Randomized Trial Objectives The aim of this study was to compare the image quality of low-kV protocols with optimized automatic tube voltage selection (ATVS) settings to reduce either radiation dose or contrast medium (CM) with that of a reference protocol for computed tomography angiography (CTA) of the thoracoabdominal aorta. Materials and Methods In this institutional review board–approved, single-center, prospective randomized controlled trial, 126 patients receiving CTA of the aorta were allocated to one of three computed tomography protocols: (A) reference protocol at 120 kVp and standard weight-adapted CM dose; (B) protocol at 90 kVp, reduced radiation and standard CM dose; and (C) protocol at 90 kVp, standard radiation and reduced CM dose. All three protocols were performed on a third-generation dual-source computed tomography scanner using the semimode of the ATVS system. The image-task-dependent optimization settings of the ATVS (slider level) were adjusted to level 11 (high-contrast task) for protocols A and B and level 3 (low-contrast task) for protocol C. Radiation dose parameters were assessed. The contrast-to-noise ratios (CNRs) of protocols B and C were tested for noninferiority compared with A. Subjective image quality was assessed using a 5-point Likert scale. Results Size-specific dose estimate was 34.3% lower for protocol B compared with A (P < 0.0001). Contrast medium was 20.2% lower for protocol C compared with A (P < 0.0001). Mean CNR in B and C was noninferior to protocol A (CNR of 30.2 ± 7, 33.4 ± 6.7, and 30.5 ± 8.9 for protocols A, B, and C, respectively). There was no significant difference in overall subjective image quality among protocols (4.09 ± 0.21, 4.03 ± 0.19, and 4.08 ± 0.17 for protocols A, B, and C, respectively; P = 0.4). Conclusions The slider settings of an ATVS system can be adjusted to optimize either radiation dose or CM at noninferior image quality in low-kV CTA of the aorta. This optimization could be used to extend future ATVS algorithms to take clinical risk factors like kidney function of individual patients into account. Received for publication August 3, 2020; and accepted for publication, after revision, September 28, 2020. Conflicts of interest and sources of funding: A.K., R.G., B.S. are employees of Siemens Healthcare GmbH. No funding was received. Correspondence to: Hatem Alkadhi, MD, MPH, EBCR, FESER, Institute for Diagnostic and Interventional Radiology, University Hospital of Zurich, University of Zurich, Rämistrasse 100, 8091 Zurich, Switzerland. E-mail: hatem.alkadhi@usz.ch. Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.

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Medicine by Alexandros G. Sfakianakis,Anapafseos 5 Agios Nikolaos 72100 Crete Greece,00302841026182,00306932607174,alsfakia@gmail.com,

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