Investigative Radiology

Acute Chelation Therapy–Associated Changes in Urine Gadolinium, Self-reported Flare Severity, and Serum Cytokines in Gadolinium Deposition Disease Objectives The aim of this study was to determine the following in patients who have undergone magnetic resonance imaging with gadolinium-based contrast agents (GBCAs) and meet the proposed diagnostic criteria for gadolinium deposition disease (GDD): (1) the effectiveness of chelation therapy (CT) with intravenous Ca-diethylenetriaminepentaacetic acid in removing retained gadolinium (Gd) and factors affecting the amount removed; (2) the frequency of CT-induced Flare, that is, GDD diagnostic symptom worsening, and factors affecting Flare intensity; (3) whether, as reported in a separate cohort, GDD patients' serum cytokine levels differ significantly from those in healthy normal controls and change significantly in response to CT; and (4) whether urine Gd, Flare reaction, and serum cytokine findings in GDD patients are mimicked in non-ill patients described as having gadolinium storage condition (GSC). Materials and Methods Twenty-one GDD subjects and 3 GSC subjects underwent CT. Patients provided pre-CT and post-CT 24-hour urine samples for Gd content determination along with pre-CT and 24-hour post-CT serum samples for cytokine analysis. Patients rated potential Flare 24 hours after CT. Pre-CT and post-CT 24-hour urine Gd analyses and Luminex serum cytokine assays were performed blind to patients' GDD and GSC status and all other data except age and sex. Serum cytokine levels in a healthy normal control group of age- and sex-matched subjects drawn from Stanford influenza vaccination studies were measured once, contemporaneously with those of GDD and GSC patients, using the same Luminex assay. Results Urine Gd amounts increased post-CT by 4 times or more after 87% of the 30 CT sessions. The most important factors appeared to be the time since the last GBCA dose and the cumulative dose received. Urine Gd amounts for GDD and GSC patients fell in the same ranges. All GDD patients, and no GSC patient, reported a Flare 24 hours post-CT. Linear regression found that Flare intensity was significantly predicted by a model including pre- and post-CT Gd amounts and the number of GBCA-enhanced magnetic resonance imaging. Post-CT, multiple cytokines showed strong positive relationships with GDD patients' Flare intensity in multivariable models. The pre-CT serum levels of 12 cytokines were significantly different in GDD patients compared with healthy flu vaccine controls. The small number of GSC patients precluded analogous statistical testing. Post-CT, GDD patients' serum levels of 20 cytokines were significantly decreased, and 2 cytokines significantly increased. These cytokines did not exhibit the same change pattern in the 3 GSC patients. The small number of GSC patients precluded statistical comparisons of GSC to GDD patients' results. Conclusions In this preliminary study, 24-hour urine Gd content increased markedly and similarly in GDD and GSC patients after Ca-diethylenetriaminepentaacetic acid CT. Post-CT Flare reaction developed only in GDD patients. The current study is the second finding significantly different serum cytokine levels in GDD patients compared with healthy normal controls. These differences and the difference between GDD and GSC patients' Flare and cytokine responses to CT suggest some inflammatory, immunologic, or other physiological differences in patients with GDD. Further research into the treatment and physiological underpinnings of GDD is warranted. Received for publication September 19, 2020; and accepted for publication, after revision, November 11, 2020. Conflicts of interest and sources of funding: Supported by grant 2U19AI057229 from the National Institutes of Health and contributions from The Avy L. and Roberta L. Miller Foundation. Correspondence to: Richard Semelka, MD, Richard Semelka Consulting, PLLC, 3901 Jones Ferry Road, Chapel Hill, NC 27516. E-mail: richardsemelka@gmail.com. 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 © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Extravasation Volume at Computed Tomography Angiography Correlates With Bleeding Rate and Prognosis in Patients With Overt Gastrointestinal Bleeding Objective Despite the identification of active extravasation on computed tomography angiography (CTA) in patients with overt gastrointestinal bleeding (GIB), a large proportion do not have active bleeding or require hemostatic therapy at endoscopy, catheter angiography, or surgery. The objective of our proof-of-concept study was to improve triage of patients with GIB by correlating extravasation volume of first-pass CTA with bleeding rate and clinical outcomes. Materials and Methods All patients who presented with overt GIB and active extravasation on CTA from January 2014 to July 2019 were reviewed in this retrospective, institutional review board–approved and Health Insurance Portability and Accountability Act–compliant study. Extravasation volume was assessed using 3-dimensional software and correlated with hemostatic therapy (primary endpoint) and with intraprocedural bleeding, blood transfusions, and mortality as secondary endpoints using logistic regression models (P < 0.0125 indicating statistical significance). Odds ratios were used to determine the effect size of a threshold extravasation volume. Quantitative data (extravasation volume, aorta attenuation, extravasation attenuation and time) were input into a mathematical model to calculate bleeding rate. Results Fifty consecutive patients including 6 (12%) upper, 18 (36%) small bowel, and 26 (52%) lower GIB met inclusion criteria. Forty-two underwent catheter angiography, endoscopy, or surgery; 16 had intraprocedural active bleeding, and 24 required hemostatic therapy. Higher extravasation volumes correlated with hemostatic therapy (P = 0.007), intraprocedural active bleeding (P = 0.003), and massive transfusion (P = 0.0001), but not mortality (P = 0.936). Using a threshold volume of 0.80 mL or greater, the odds ratio of hemostatic therapy was 8.1 (95% confidence interval, 2.1–26), active bleeding was 11.8 (2.6–45), and massive transfusion was 18 (2.3–65). With mathematical modeling, extravasation volume had a direct and linear relationship with bleeding rate, and the lowest calculated detectable bleeding rate with CTA was less than 0.1 mL/min. Conclusions Larger extravasation volumes correlate with higher bleeding rates and may identify patients who require hemostatic therapy, have intraprocedural bleeding, and require blood transfusions. Current CTAs can detect bleeding rates less than 0.1 mL/min. Received for publication October 13, 2020; and accepted for publication, after revision, November 20, 2020. Conflicts of interest and source of funding: Justin R. Tse was the recipient of the 2019–2020 GE Healthcare/RSNA Resident Research Grant, which provided funding support for this study. Rajesh Shah received grants from Merit Medical and consulting fees from Genentech, Intuitive Surgical, and Kaiser Associates. Dominik Fleischmann received grants from GE Medical Systems and Siemens Medical Solutions and received payment for lectures from the Bracco Group. Aya Kamaya has received book royalties from Elsevier. For the remaining authors, none were declared. The project described here was supported by the RSNA Research & Education Foundation, through grant number RR1973. The content is solely the responsibility of the authors and does not necessarily represent the official views of the RSNA Research & Education Foundation. Correspondence to: Aya Kamaya, MD, Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, Room H-1307, Stanford, CA 94305. E-mail: kamaya@stanford.edu. 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 © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Navigator-Guided Motion and B0 Correction of T2*-Weighted Magnetic Resonance Imaging Improves Multiple Sclerosis Cortical Lesion Detection Background Cortical lesions are common in multiple sclerosis (MS). T2*-weighted (T2*w) imaging at 7 T is relatively sensitive for cortical lesions, but quality is often compromised by motion and main magnetic field (B0) fluctuations. Purpose The aim of this study was to determine whether motion and B0 correction with a navigator-guided gradient-recalled echo sequence can improve cortical lesion detection in T2*w magnetic resonance imaging. Materials and Methods In this prospective study, a gradient-recalled echo sequence incorporating a navigator allowing for motion and B0 field correction was applied to collect T2*w images at 7 T from adults with MS between August 2019 and March 2020. T2*-weighted images were acquired in 1 to 3 partially overlapping scans per individual and were reconstructed using global average B0 correction ("uncorrected") or motion correction and spatially linear B0 correction ("corrected"). Image quality rating and manual segmentation of cortical lesions were performed on uncorrected and corrected images. Lesions seen on a single scan were retrospectively evaluated on the complementary scan. The association of cortical lesions with clinical disability was assessed. Mixed models were used to determine the effect of correction on lesion detection as well as on the relationship between disability and lesion count. Results A total of 22 T2*w scans were performed on 11 adults with MS (mean [SD] age, 49 [11] years; 8 women). Quality improved for 20 of 22 scans (91%) after correction. A total of 69 cortical lesions were identified on uncorrected images (median per scan, 2; range, 0–11) versus 148 on corrected images (median per scan, 4.5; range, 0–25; rate ratio [RR], 2.1; P < 0.0001). For low-quality uncorrected scans with moderate to severe motion artifact (18/22, 82%), there was an improvement in cortical lesion detection with correction (RR, 2.5; P < 0.0001), whereas there was no significant change in cortical lesion detection for high-quality scans (RR, 1.3; P = 0.43). Conclusions Navigator-guided motion and B0 correction substantially improves the overall image quality of T2*w magnetic resonance imaging at 7 T and increases its sensitivity for cortical lesions. Received for publication October 15, 2020; and accepted for publication, after revision, November 24, 2020. Jiaen Liu and Erin S. Beck contributed equally to this study. Conflicts of interest and sources of funding: This research was supported by the Intramural Research Program of the National Institute of Neurological Disorders and Stroke, NIH. Erin Beck was supported by a Clinician Scientist Development Award and a Career Transition Fellowship Award from the National Multiple Sclerosis Society. Correspondence to: Daniel S. Reich, MD, PhD, 10 Center Drive MSC 1400, Building 10 Room 5C103, Bethesda, MD 20892. E-mail: daniel.reich@nih.gov. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Deep Learning–Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies Purpose The aims of this study were to train and evaluate deep learning models for automated segmentation of abdominal organs in whole-body magnetic resonance (MR) images from the UK Biobank (UKBB) and German National Cohort (GNC) MR imaging studies and to make these models available to the scientific community for analysis of these data sets. Methods A total of 200 T1-weighted MR image data sets of healthy volunteers each from UKBB and GNC (400 data sets in total) were available in this study. Liver, spleen, left and right kidney, and pancreas were segmented manually on all 400 data sets, providing labeled ground truth data for training of a previously described U-Net-based deep learning framework for automated medical image segmentation (nnU-Net). The trained models were tested on all data sets using a 4-fold cross-validation scheme. Qualitative analysis of automated segmentation results was performed visually; performance metrics between automated and manual segmentation results were computed for quantitative analysis. In addition, interobserver segmentation variability between 2 human readers was assessed on a subset of the data. Results Automated abdominal organ segmentation was performed with high qualitative and quantitative accuracy on UKBB and GNC data. In more than 90% of data sets, no or only minor visually detectable qualitative segmentation errors occurred. Mean Dice scores of automated segmentations compared with manual reference segmentations were well higher than 0.9 for the liver, spleen, and kidneys on UKBB and GNC data and around 0.82 and 0.89 for the pancreas on UKBB and GNC data, respectively. Mean average symmetric surface distance was between 0.3 and 1.5 mm for the liver, spleen, and kidneys and between 2 and 2.2 mm for pancreas segmentation. The quantitative accuracy of automated segmentation was comparable with the agreement between 2 human readers for all organs on UKBB and GNC data. Conclusion Automated segmentation of abdominal organs is possible with high qualitative and quantitative accuracy on whole-body MR imaging data acquired as part of UKBB and GNC. The results obtained and deep learning models trained in this study can be used as a foundation for automated analysis of thousands of MR data sets of UKBB and GNC and thus contribute to tackling topical and original scientific questions. Received for publication October 30, 2020; and accepted for publication, after revision, November 22, 2020. Turkay Kart and Marc Fischer contributed equally to this study. This research was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare and in part by the German Research Foundation (project number 428219130). This project was conducted with data from the German National Cohort study (www.nako.de). The German National Cohort study is funded by the Federal Ministry of Education and Research (BMBF) (project funding reference numbers 01ER1301A/B/C and 01ER1511D), federal states, and the Helmholtz Association with additional financial support by the participating universities and the institutes of the Leibniz Association. This work was carried out under UK Biobank Application 40040. Conflicts of interest and sources of funding: none declared. Correspondence to: Sergios Gatidis, MD, University Hospital Tübingen, Hoppe-Seyler-Str. 372076 Tübingen, Germany. E-mail: sergios.gatidis@med.uni-tuebingen.de. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

Dual-Energy Computed Tomography for Detection and Characterization of Monosodium Urate, Calcium Pyrophosphate, and Hydroxyapatite: A Phantom Study on Diagnostic Performance Objectives The aim of this study was to determine the diagnostic performance of dual-energy computed tomography (DECT) to detect and distinguish crystal deposits in a phantom. The primary objective was to determine the cutoff DECT ratio and the cross-sectional area (CSA) of a crystal deposit necessary to differentiate monosodium urate (MSU), calcium pyrophosphate (CPP), and calcium hydroxyapatite (HA) using DECT. Our secondary objective was to determine the concentration for limit of detection for MSU, CPP, and HA crystal deposits. Exploratory objectives included the comparison between 2 generations of DECT scanners from the same manufacturer as well as different scanner settings. Materials and Methods We used a cylindrical soft tissue phantom with synthetic MSU, CPP, and HA crystals suspended in resin. Crystal suspension concentration increased with similar attenuation between MSU, CPP, and HA in conventional CT. The phantom was scanned on 2 dual-source DECT scanners, at 2 dose levels and all available tube voltage combinations. Both scanners had a tin (Sn) filter at the high-energy spectra. Dual-energy CT ratios were calculated for a given tube voltage combination by dividing linear regression lines of CT numbers against concentration. Dual-energy CT ratios were compared using an analysis of covariance. Receiver operating characteristic curves and corresponding areas under the curve (AUCs) were calculated for individual crystal suspension comparisons (HA vs CPP, MSU vs CPP, and MSU vs HA). Results At standard clinical scan settings with 8 mGy and 80/Sn150 kV, the DECT ratios were as follows: CPP, 2.02 (95% confidence interval [CI], 1.98–2.07); HA, 2.00 (95% CI, 1.96–2.05); and MSU, 1.09 (95% CI, 1.06–1.11). Ratios varied numerically depending on the scanner and tube voltage combination. Monosodium urate crystal DECT ratios were significantly different from HA and CPP (P < 0.001), whereas DECT ratios for HA and CPP crystals did not differ significantly (P = 0.99). The differentiation of MSU crystals from both calcium crystals (HA and CPP) was excellent with an AUC of 1.00 (95% CI, 1.00–1.00) and an optimal cutoff DECT ratio of 1.43:1.40 depending on the scanner. In addition, differentiation of MSU and calcium-containing crystals (HA and CPP) required a CSA of minimum 4 pixels of crystal at standard clinical scan conditions. In contrast, differentiation between CPP and HA crystals was moderate with AUCs ranging from 0.66 (95% CI, 0.52–0.80) to 0.80 (95% CI, 0.69–0.91) and an optimal cutoff DECT ratio of 2.02:2.06 depending on the scanner. Furthermore, differentiation between CPP and HA crystals required a CSA of minimum 87 pixels of crystal at standard clinical scan conditions, corresponding to a region of interest of 3.7 mm diameter. When scanning at highest possible spectral separation and maximum dose of 50 mGy, the limit of detection for crystals within a region of interest of 50 pixels was 14 mg/cm3 for MSU and 2 mg/cm3 for both CPP and HA. Conclusions This phantom study shows that DECT can be used to detect MSU, CPP, and HA crystal deposits. Differentiation of CPP and HA was not possible in crystals deposits less than 3.7 mm in diameter, but MSU could accurately be differentiated from CPP and HA crystal deposits at standard clinical scan conditions. Received for publication September 28, 2020; and accepted for publication, after revision, December 2, 2020. Conflicts of interest and sources of funding: A.D., L.S., H.B., and M.B. have no conflicts of interest to declare. F.C.M. is an employee of Siemens Healthineers. F.B. has a research agreement for DECT with Siemens Healthineers. A.D. is supported by the IMK Foundation, the A.P. Møller Foundation, the Aase and Ejnar Danielsen's Foundation, and the Danish Medical Association. The Parker Institute is also supported by a core grant from the Oak Foundation (OCAY-18-774-OFIL). Correspondence to: Anna Døssing, MD, The Parker Institute, Bispebjerg and Frederiksberg Hospital, Nordre Fasanvej 57, 2000 Frederiksberg, Denmark. E-mail: anna.doessing.01@regionh.dk. 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 © 2021 Wolters Kluwer Health, Inc. All rights reserved.

A Deep Learning System for Synthetic Knee Magnetic Resonance Imaging: Is Artificial Intelligence–Based Fat-Suppressed Imaging Feasible? Objectives The aim of this study was to determine the feasibility and performance of a deep learning system used to create synthetic artificial intelligence–based fat-suppressed magnetic resonance imaging (AFSMRI) scans of the knee. Materials and Methods This single-center study was approved by the institutional review board. Artificial intelligence–based FS MRI scans were created from non-FS images using a deep learning system with a modified convolutional neural network–based U-Net that used a training set of 25,920 images and validation set of 16,416 images. Three musculoskeletal radiologists reviewed 88 knee MR studies in 2 sessions, the original (proton density [PD] + FSPD) and the synthetic (PD + AFSMRI). Readers recorded AFSMRI quality (diagnostic/nondiagnostic) and the presence or absence of meniscal, ligament, and tendon tears; cartilage defects; and bone marrow abnormalities. Contrast-to-noise rate measurements were made among subcutaneous fat, fluid, bone marrow, cartilage, and muscle. The original MRI sequences were used as the reference standard to determine the diagnostic performance of AFSMRI (combined with the original PD sequence). This is a fully balanced study design, where all readers read all images the same number of times, which allowed the determination of the interchangeability of the original and synthetic protocols. Descriptive statistics, intermethod agreement, interobserver concordance, and interchangeability tests were applied. A P value less than 0.01 was considered statistically significant for the likelihood ratio testing, and P value less than 0.05 for all other statistical analyses. Results Artificial intelligence–based FS MRI quality was rated as diagnostic (98.9% [87/88] to 100% [88/88], all readers). Diagnostic performance (sensitivity/specificity) of the synthetic protocol was high, for tears of the menisci (91% [71/78], 86% [84/98]), cruciate ligaments (92% [12/13], 98% [160/163]), collateral ligaments (80% [16/20], 100% [156/156]), and tendons (90% [9/10], 100% [166/166]). For cartilage defects and bone marrow abnormalities, the synthetic protocol offered an overall sensitivity/specificity of 77% (170/221)/93% (287/307) and 76% (95/125)/90% (443/491), respectively. Intermethod agreement ranged from moderate to substantial for almost all evaluated structures (menisci, cruciate ligaments, collateral ligaments, and bone marrow abnormalities). No significant difference was observed between methods for all structural abnormalities by all readers (P > 0.05), except for cartilage assessment. Interobserver agreement ranged from moderate to substantial for almost all evaluated structures. Original and synthetic protocols were interchangeable for the diagnosis of all evaluated structures. There was no significant difference for the common exact match proportions for all combinations (P > 0.01). The conspicuity of all tissues assessed through contrast-to-noise rate was higher on AFSMRI than on original FSPD images (P < 0.05). Conclusions Artificial intelligence–based FS MRI (3D AFSMRI) is feasible and offers a method for fast imaging, with similar detection rates for structural abnormalities of the knee, compared with original 3D MR sequences. Received for publication September 15, 2020; and accepted for publication, after revision, October 27, 2020. Conflicts of interest and sources of funding: L.F. was affiliated with GERRAF, Siemens Medical Systems prior to 2014. J.F. received institutional research support from Siemens Healthcare USA, DePuy Synthes, Zimmer Biomet, Microsoft, Synthetic MRI, ImageBiopsy Lab, QED, and BTG International; scientifically advised Siemens Healthcare USA, GE Healthcare Technologies, BTG International, ImageBiopsy Lab, Mirata Pharmaceuticals, Synthetic MRI, and Boston Scientific; and has shared patents with Siemens Healthcare USA and Johns Hopkins University. S.A. is a research consultant for Pfizer Inc. M.A.J. received National Institutes of Health grant numbers 5P30CA006973 (Imaging Response Assessment Team-IRAT), U01CA140204, and 1R01CA190299. The Tesla K40s used for this research was donated by the NVIDIA Corporation. V.S.P., R.d.C.L., C.C.K., and D.T. have nothing to disclose. Correspondence to: Laura M. Fayad, MD, The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins Medical Institutions, 600 North Wolfe St, Baltimore, MD 21287. E-mail: lfayad1@jhmi.edu. Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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 © 2021 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 © 2021 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 © 2021 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 © 2021 Wolters Kluwer Health, Inc. All rights reserved.

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