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Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Han F, Gao C, Shih SF, Dai Q, Curiel O, Tsao TC, Wu HH, Deshpande V. Accelerated free-breathing liver fat and R 2 * quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation. Magn Reson Med 2024; 92:1149-1161. [PMID: 38650444 DOI: 10.1002/mrm.30117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 02/25/2024] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE To improve image quality, mitigate quantification biases and variations for free-breathing liver proton density fat fraction (PDFF) andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification accelerated by radial k-space undersampling. METHODS A free-breathing multi-echo stack-of-radial MRI method was developed with compressed sensing with multidimensional regularization. It was validated in motion phantoms with reference acquisitions without motion and in 11 subjects (6 patients with nonalcoholic fatty liver disease) with reference breath-hold Cartesian acquisitions. Images, PDFF, andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ maps were reconstructed using different radial view k-space sampling factors and reconstruction settings. Results were compared with reference-standard results using Bland-Altman analysis. Using linear mixed-effects model fitting (p < 0.05 considered significant), mean and SD were evaluated for biases and variations of PDFF andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ , respectively, and coefficient of variation on the first echo image was evaluated as a surrogate for image quality. RESULTS Using the empirically determined optimal sampling factor of 0.25 in the accelerated in vivo protocols, mean differences and limits of agreement for the proposed method were [-0.5; -33.6, 32.7] s-1 forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-1.0%; -5.8%, 3.8%] for PDFF, close to those of a previous self-gating method using fully sampled radial views: [-0.1; -27.1, 27.0] s-1 forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and [-0.4%; -4.5%, 3.7%] for PDFF. The proposed method had significantly lower coefficient of variation than other methods (p < 0.001). Effective acquisition time of 64 s or 59 s was achieved, compared with 171 s or 153 s for two baseline protocols with different radial views corresponding to sampling factor of 1.0. CONCLUSION This proposed method may allow accelerated free-breathing liver PDFF andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping with reduced biases and variations.
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Affiliation(s)
- Xiaodong Zhong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Marcel D Nickel
- MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany
| | | | - Brian M Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Cary, North Carolina, USA
| | - Fei Han
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Chang Gao
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Qing Dai
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Omar Curiel
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Tsu-Chin Tsao
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
- Department of Bioengineering, Samueli School of Engineering, University of California Los Angeles, Los Angeles, California, USA
| | - Vibhas Deshpande
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Austin, Texas, USA
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Kolokythas O, Yaman Akcicek E, Akcicek H, Briller N, Rajamohan N, Yokoo T, Peeters HM, Revels JW, Moura Cunha G, Sahani DV, Mileto A. T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects. Radiographics 2024; 44:e230173. [PMID: 38990776 DOI: 10.1148/rg.230173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.
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Affiliation(s)
- Orpheus Kolokythas
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Ebru Yaman Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Halit Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Noah Briller
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Naveen Rajamohan
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Takeshi Yokoo
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Hans M Peeters
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Jonathan W Revels
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Dushyant V Sahani
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Achille Mileto
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
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Yoon H, Kim J, Lim HJ, Lee MJ. Quantitative Liver Imaging in Children. Invest Radiol 2024:00004424-990000000-00238. [PMID: 39047265 DOI: 10.1097/rli.0000000000001101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
ABSTRACT In children and adults, quantitative imaging examinations determine the effectiveness of treatment for liver disease. However, pediatric liver disease differs in presentation from liver disease in adults. Children also needed to be followed for a longer period from onset and have less control of their bodies, showing more movement than adults during imaging examinations, which leads to a greater need for sedation. Thus, it is essential to appropriately tailor and accurately perform noninvasive imaging tests in these younger patients. This article is an overview of updated imaging techniques used to assess liver disease quantitatively in children. The common initial imaging study for diffuse liver disease in pediatric patients is ultrasound. In addition to preexisting echo analysis, newly developed attenuation imaging techniques have been introduced to evaluate fatty liver. Ultrasound elastography is also now actively used to evaluate liver conditions, and the broad age spectrum of the pediatric population requires caution to be taken even in the selection of probes. Magnetic resonance imaging (MRI) is another important imaging tool used to evaluate liver disease despite requiring sedation or anesthesia in young children because it allows quantitative analysis with sequences such as fat analysis and MR elastography. In addition to ultrasound and MRI, we review quantitative imaging methods specifically for fatty liver, Wilson disease, biliary atresia, hepatic fibrosis, Fontan-associated liver disease, autoimmune hepatitis, sinusoidal obstruction syndrome, and the transplanted liver. Lastly, concerns such as growth and motion that need to be addressed specifically for children are summarized.
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Affiliation(s)
- Haesung Yoon
- From the Department of Radiology, Gangnam Severance Hospital, Seoul, South Korea (H.Y.); Department of Radiology and Research Institute of Radiological Science, Yonsei University, College of Medicine, Seoul, South Korea (H.Y., J.K., H.J.L., M.-J.L.); and Department of Pediatric Radiology, Severance Children's Hospital, Seoul, South Korea (J.K., H.J.L., M.-J.L.)
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Shih SF, Wu HH. Free-breathing MRI techniques for fat and R 2* quantification in the liver. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01187-2. [PMID: 39039272 DOI: 10.1007/s10334-024-01187-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/18/2024] [Accepted: 07/02/2024] [Indexed: 07/24/2024]
Abstract
OBJECTIVE To review the recent advancements in free-breathing MRI techniques for proton-density fat fraction (PDFF) and R2* quantification in the liver, and discuss the current challenges and future opportunities. MATERIALS AND METHODS This work focused on recent developments of different MRI pulse sequences, motion management strategies, and reconstruction approaches that enable free-breathing liver PDFF and R2* quantification. RESULTS Different free-breathing liver PDFF and R2* quantification techniques have been evaluated in various cohorts, including healthy volunteers and patients with liver diseases, both in adults and children. Initial results demonstrate promising performance with respect to reference measurements. These techniques have a high potential impact on providing a solution to the clinical need of accurate liver fat and iron quantification in populations with limited breath-holding capacity. DISCUSSION As these free-breathing techniques progress toward clinical translation, studies of the linearity, bias, and repeatability of free-breathing PDFF and R2* quantification in a larger cohort are important. Scan acceleration and improved motion management also hold potential for further enhancement.
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Affiliation(s)
- Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA.
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Tsujita Y, Sofue K, Ueshima E, Ueno Y, Hori M, Murakami T. Clinical Application of Quantitative MR Imaging in Nonalcoholic Fatty Liver Disease. Magn Reson Med Sci 2023; 22:435-445. [PMID: 35584952 PMCID: PMC10552668 DOI: 10.2463/mrms.rev.2021-0152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/23/2022] [Indexed: 11/09/2022] Open
Abstract
Viral hepatitis was previously the most common cause of chronic liver disease. However, in recent years, nonalcoholic fatty liver disease (NAFLD) cases have been increasing, especially in developed countries. NAFLD is histologically characterized by fat, fibrosis, and inflammation in the liver, eventually leading to cirrhosis and hepatocellular carcinoma. Although biopsy is the gold standard for the assessment of the liver parenchyma, quantitative evaluation methods, such as ultrasound, CT, and MRI, have been reported to have good diagnostic performances. The quantification of liver fat, fibrosis, and inflammation is expected to be clinically useful in terms of the prognosis, early intervention, and treatment response for the management of NAFLD. The aim of this review was to discuss the basics and prospects of MRI-based tissue quantifications of the liver, mainly focusing on proton density fat fraction for the quantification of fat deposition, MR elastography for the quantification of fibrosis, and multifrequency MR elastography for the evaluation of inflammation.
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Affiliation(s)
- Yushi Tsujita
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Eisuke Ueshima
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yoshiko Ueno
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Masatoshi Hori
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
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Guglielmo FF, Barr RG, Yokoo T, Ferraioli G, Lee JT, Dillman JR, Horowitz JM, Jhaveri KS, Miller FH, Modi RY, Mojtahed A, Ohliger MA, Pirasteh A, Reeder SB, Shanbhogue K, Silva AC, Smith EN, Surabhi VR, Taouli B, Welle CL, Yeh BM, Venkatesh SK. Liver Fibrosis, Fat, and Iron Evaluation with MRI and Fibrosis and Fat Evaluation with US: A Practical Guide for Radiologists. Radiographics 2023; 43:e220181. [PMID: 37227944 DOI: 10.1148/rg.220181] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Quantitative imaging biomarkers of liver disease measured by using MRI and US are emerging as important clinical tools in the management of patients with chronic liver disease (CLD). Because of their high accuracy and noninvasive nature, in many cases, these techniques have replaced liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. The most commonly evaluated imaging biomarkers are surrogates for liver fibrosis, fat, and iron. MR elastography is now routinely performed to evaluate for liver fibrosis and typically combined with MRI-based liver fat and iron quantification to exclude or grade hepatic steatosis and iron overload, respectively. US elastography is also widely performed to evaluate for liver fibrosis and has the advantage of lower equipment cost and greater availability compared with those of MRI. Emerging US fat quantification methods can be performed along with US elastography. The author group, consisting of members of the Society of Abdominal Radiology (SAR) Liver Fibrosis Disease-Focused Panel (DFP), the SAR Hepatic Iron Overload DFP, and the European Society of Radiology, review the basics of liver fibrosis, fat, and iron quantification with MRI and liver fibrosis and fat quantification with US. The authors cover technical requirements, typical case display, quality control and proper measurement technique and case interpretation guidelines, pitfalls, and confounding factors. The authors aim to provide a practical guide for radiologists interpreting these examinations. © RSNA, 2023 See the invited commentary by Ronot in this issue. Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
- Flavius F Guglielmo
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Richard G Barr
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Takeshi Yokoo
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Giovanna Ferraioli
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - James T Lee
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Jonathan R Dillman
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Jeanne M Horowitz
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Kartik S Jhaveri
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Frank H Miller
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Roshan Y Modi
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Amirkasra Mojtahed
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Michael A Ohliger
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Ali Pirasteh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Scott B Reeder
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Krishna Shanbhogue
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Alvin C Silva
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Elainea N Smith
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Venkateswar R Surabhi
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Bachir Taouli
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Christopher L Welle
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Benjamin M Yeh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
| | - Sudhakar K Venkatesh
- From the Department of Radiology, Thomas Jefferson University, 132 S 10th St, Philadelphia, PA 19107 (F.F.G.); Department of Radiology, Northeastern Ohio Medical University, Rootstown, Ohio (R.G.B.); Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (T.Y.); Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy (G.F.); Department of Radiology, University of Kentucky, Lexington, Ky (J.T.L.); Department of Radiology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio (J.R.D.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (J.M.H., F.H.M.); Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.S.J.); Department of Radiology, ChristianaCare, Newark, Del (R.Y.M.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (A.M.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, Calif (M.A.O., B.M.Y.); Departments of Radiology and Medical Physics (A.P.) and Departments of Radiology, Medical Physics, Biomedical Engineering, Medicine, and Emergency Medicine (S.B.R.), University of Wisconsin, Madison, Wis; Department of Radiology, NYU Langone Health, New York, NY (K.S.); Department of Radiology, Mayo Clinic, Phoenix, Ariz (A.C.S.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (E.N.S.); Department of Radiology, University of Texas MD Anderson Cancer Center, Houston, Tex (V.R.S.); Department of Diagnostic, Molecular and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY (B.T.); and Department of Radiology, Mayo Clinic, Rochester, Minn (C.L.W., S.K.V.)
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7
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Herrmann J, Petit P, Grabhorn E, Lenz A, Jürgens J, Franchi-Albella S. Liver cirrhosis in children - the role of imaging in the diagnostic pathway. Pediatr Radiol 2023; 53:714-726. [PMID: 36040526 PMCID: PMC10027649 DOI: 10.1007/s00247-022-05480-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/23/2022] [Accepted: 07/31/2022] [Indexed: 10/14/2022]
Abstract
Liver cirrhosis in children is a rare disease with multifactorial causes that are distinct from those in adults. Underlying reasons include cholestatic, viral, autoimmune, hereditary, metabolic and cardiac disorders. Early detection of fibrosis is important as clinical stabilization or even reversal of fibrosis can be achieved in some disorders with adequate treatment. This article focuses on the longitudinal evaluation of children with chronic liver disease with noninvasive imaging tools, which play an important role in detecting cirrhosis, defining underlying causes, grading fibrosis and monitoring patients during follow-up. Ultrasound is the primary imaging modality and it is used in a multiparametric fashion. Magnetic resonance imaging and computed tomography are usually applied second line for refined tissue characterization, clarification of nodular lesions and full delineation of abdominal vessels, including portosystemic communications.
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Affiliation(s)
- Jochen Herrmann
- Section of Pediatric Radiology, Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20251, Hamburg, Germany.
| | - Philippe Petit
- Aix Marseille Université, Hopital Timone-Enfants, Marseille, France
| | - Enke Grabhorn
- Department of Pediatric Gastroenterology and Hepatology, University Medical Center Hamburg, Hamburg, Germany
| | - Alexander Lenz
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center, Hamburg, Germany
| | - Julian Jürgens
- Section of Pediatric Radiology, Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20251, Hamburg, Germany
| | - Stéphanie Franchi-Albella
- Department of Pediatric Radiology, Hôpital Bicêtre, National Reference Centre for Rare Pediatric Liver Diseases, Paris, France
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8
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Story JD, Ghahremani S, Kafali SG, Shih SF, Kuwahara KJ, Calkins KL, Wu HH. Using Free-Breathing MRI to Quantify Pancreatic Fat and Investigate Spatial Heterogeneity in Children. J Magn Reson Imaging 2023; 57:508-518. [PMID: 35778376 PMCID: PMC9805469 DOI: 10.1002/jmri.28337] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 06/17/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND MRI acquisition for pediatric pancreatic fat quantification is limited by breath-holds (BH). Full segmentation (FS) or small region of interest (ROI) analysis methods may not account for pancreatic fat spatial heterogeneity, which may limit accuracy. PURPOSE To improve MRI acquisition and analysis for quantifying pancreatic proton-density fat fraction (pPDFF) in children by investigating free-breathing (FB)-MRI, characterizing pPDFF spatial heterogeneity, and relating pPDFF to clinical markers. STUDY TYPE Prospective. POPULATION A total of 34 children, including healthy (N = 16, 8 female) and overweight (N = 18, 5 female) subjects. FIELD STRENGTH AND SEQUENCES 3 T; multiecho gradient-echo three-dimensional (3D) stack-of-stars FB-MRI, multiecho gradient-echo 3D Cartesian BH-MRI. ASSESSMENT A radiologist measured FS- and ROI-based pPDFF on FB-MRI and BH-MRI PDFF maps, with anatomical images as references. Regional pPDFF in the pancreatic head, body, and tail were measured on FB-MRI. FS-pPDFF, ROI-pPDFF, and regional pPDFF were compared, and related to clinical markers, including hemoglobin A1c. STATISTICAL TESTS T-test, Bland-Altman analysis, Lin's concordance correlation coefficient (CCC), one-way analysis of variance, and Spearman's rank correlation coefficient were used. P < 0.05 was considered significant. RESULTS FS-pPDFF and ROI-pPDFF from FB-MRI and BH-MRI had mean difference = 0.4%; CCC was 0.95 for FS-pPDFF and 0.62 for ROI-pPDFF. FS-pPDFF was higher than ROI-pPDFF (10.4% ± 6.4% vs. 4.2% ± 2.8%). Tail-pPDFF (11.6% ± 8.1%) was higher than body-pPDFF (8.9% ± 6.3%) and head-pPDFF (8.7% ± 5.2%). Head-pPDFF and body-pPDFF positively correlated with hemoglobin A1c. DATA CONCLUSION FB-MRI pPDFF is comparable to BH-MRI. Spatial heterogeneity affects pPDFF quantification. Regional measurements of pPDFF in the head and body were correlated with hemoglobin A1c, a marker of insulin sensitivity. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jacob D. Story
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Shahnaz Ghahremani
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Sevgi Gokce Kafali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Shu-Fu Shih
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Kelsey J. Kuwahara
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Kara L. Calkins
- Department of Pediatrics, Division of Neonatology and Developmental Biology, and the UCLA Children’s Discovery and Innovation Institute, University of California Los Angeles, Los Angeles, CA, United States
| | - Holden H. Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
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9
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Gao C, Ghodrati V, Shih SF, Wu HH, Liu Y, Nickel MD, Vahle T, Dale B, Sai V, Felker E, Surawech C, Miao Q, Finn JP, Zhong X, Hu P. Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network. Magn Reson Imaging 2023; 95:70-79. [PMID: 36270417 PMCID: PMC10163826 DOI: 10.1016/j.mri.2022.10.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/14/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE Stack-of-radial MRI allows free-breathing abdominal scans, however, it requires relatively long acquisition time. Undersampling reduces scan time but can cause streaking artifacts and degrade image quality. This study developed deep learning networks with adversarial loss and evaluated the performance of reducing streaking artifacts and preserving perceptual image sharpness. METHODS A 3D generative adversarial network (GAN) was developed for reducing streaking artifacts in stack-of-radial abdominal scans. Training and validation datasets were self-gated to 5 respiratory states to reduce motion artifacts and to effectively augment the data. The network used a combination of three loss functions to constrain the anatomy and preserve image quality: adversarial loss, mean-squared-error loss and structural similarity index loss. The performance of the network was investigated for 3-5 times undersampled data from 2 institutions. The performance of the GAN for 5 times accelerated images was compared with a 3D U-Net and evaluated using quantitative NMSE, SSIM and region of interest (ROI) measurements as well as qualitative scores of radiologists. RESULTS The 3D GAN showed similar NMSE (0.0657 vs. 0.0559, p = 0.5217) and significantly higher SSIM (0.841 vs. 0.798, p < 0.0001) compared to U-Net. ROI analysis showed GAN removed streaks in both the background air and the tissue and was not significantly different from the reference mean and variations. Radiologists' scores showed GAN had a significant improvement of 1.6 point (p = 0.004) on a 4-point scale in streaking score while no significant difference in sharpness score compared to the input. CONCLUSION 3D GAN removes streaking artifacts and preserves perceptual image details.
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Affiliation(s)
- Chang Gao
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Vahid Ghodrati
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Yongkai Liu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | | | - Thomas Vahle
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Brian Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States
| | - Victor Sai
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Ely Felker
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States
| | - Chuthaporn Surawech
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, Division of Diagnostic Radiology, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand
| | - Qi Miao
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Department of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
| | - J Paul Finn
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States
| | - Peng Hu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, United States; Inter-Departmental Graduate Program of Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, United States.
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10
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Strobel KM, Kafali SG, Shih SF, Artura AM, Masamed R, Elashoff D, Wu HH, Calkins KL. Pregnancies complicated by gestational diabetes and fetal growth restriction: an analysis of maternal and fetal body composition using magnetic resonance imaging. J Perinatol 2023; 43:44-51. [PMID: 36319757 PMCID: PMC9840659 DOI: 10.1038/s41372-022-01549-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/19/2022] [Accepted: 10/20/2022] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Maternal body composition may influence fetal body composition. OBJECTIVE The objective of this pilot study was to investigate the relationship between maternal and fetal body composition. METHODS Three pregnant women cohorts were studied: healthy, gestational diabetes (GDM), and fetal growth restriction (FGR). Maternal body composition (visceral adipose tissue volume (VAT), subcutaneous adipose tissue volume (SAT), pancreatic and hepatic proton-density fat fraction (PDFF) and fetal body composition (abdominal SAT and hepatic PDFF) were measured using MRI between 30 to 36 weeks gestation. RESULTS Compared to healthy and FGR fetuses, GDM fetuses had greater hepatic PDFF (5.2 [4.2, 5.5]% vs. 3.2 [3, 3.3]% vs. 1.9 [1.4, 3.7]%, p = 0.004). Fetal hepatic PDFF was associated with maternal SAT (r = 0.47, p = 0.02), VAT (r = 0.62, p = 0.002), and pancreatic PDFF (r = 0.54, p = 0.008). When controlling for maternal SAT, GDM increased fetal hepatic PDFF by 0.9 ([0.51, 1.3], p = 0.001). CONCLUSION In this study, maternal SAT, VAT, and GDM status were positively associated with fetal hepatic PDFF.
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Affiliation(s)
- Katie M. Strobel
- Department of Pediatrics, Division of Neonatology & Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Sevgi Gokce Kafali
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | | | - Rinat Masamed
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - David Elashoff
- University of California Los Angeles, Los Angeles, CA, USA
| | - Holden H. Wu
- Department of Medicine, Biostatistics and Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kara L. Calkins
- Department of Pediatrics, Division of Neonatology & Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
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11
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Dillman JR, Tkach JA, Pedneker A, Trout AT. Quantitative abdominal magnetic resonance imaging in children-special considerations. Abdom Radiol (NY) 2022; 47:3069-3077. [PMID: 34196762 DOI: 10.1007/s00261-021-03191-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/20/2021] [Accepted: 06/21/2021] [Indexed: 01/18/2023]
Abstract
The use of quantitative MRI methods for assessment of the abdomen in children has become commonplace over the past decade. Increasingly employed methods include MR elastography, chemical shift encoded (CSE) MR imaging for determination of proton density fat fraction, diffusion-weighted imaging, and a variety of relaxometry techniques, such as T1 and T2* mapping. These techniques can be used in a variety of settings to distinguish normal from abnormal tissue as well as determine the severity of disease. The performance of quantitative MRI methods in the pediatric population presents unique challenges as compared to adult populations. These challenges relate to multiple factors, including patient size, pediatric physiology, inability to breath hold, and greater physical motion during the examination. The purpose of this review article is to review quantitative MRI methods that may be used in clinical practice to assess the pediatric abdomen and to discuss special considerations when performing these techniques in children.
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Affiliation(s)
- Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Amol Pedneker
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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12
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Li S, Shen C, Ding Z, She H, Du YP. Accelerating multi-echo chemical shift encoded water-fat MRI using model-guided deep learning. Magn Reson Med 2022; 88:1851-1866. [PMID: 35649172 DOI: 10.1002/mrm.29307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/30/2022] [Accepted: 05/02/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE To accelerate chemical shift encoded (CSE) water-fat imaging by applying a model-guided deep learning water-fat separation (MGDL-WF) framework to the undersampled k-space data. METHODS A model-guided deep learning water-fat separation framework is proposed for the acceleration using Cartesian/radial undersampling data. The proposed MGDL-WF combines the power of CSE water-fat imaging model and data-driven deep learning by jointly using a multi-peak fat model and a modified residual U-net network. The model is used to guide the image reconstruction, and the network is used to capture the artifacts induced by the undersampling. A data consistency layer is used in MGDL-WF to ensure the output images to be consistent with the k-space measurements. A Gauss-Newton iteration algorithm is adapted for the gradient updating of the networks. RESULTS Compared with the compressed sensing water-fat separation (CS-WF) algorithm/2-step procedure algorithm, the MGDL-WF increased peak signal-to-noise ratio (PSNR) by 5.31/5.23, 6.11/4.54, and 4.75 dB/1.88 dB with Cartesian sampling, and by 4.13/6.53, 2.90/4.68, and 1.68 dB/3.48 dB with radial sampling, at acceleration rates (R) of 4, 6, and 8, respectively. By using MGDL-WF, radial sampling increased the PSNR by 2.07 dB at R = 8, compared with Cartesian sampling. CONCLUSIONS The proposed MGDL-WF enables exploiting features of the water images and fat images from the undersampled multi-echo data, leading to improved performance in the accelerated CSE water-fat imaging. By using MGDL-WF, radial sampling can further improve the image quality with comparable scan time in comparison with Cartesian sampling.
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Affiliation(s)
- Shuo Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chenfei Shen
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zekang Ding
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huajun She
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Yiping P Du
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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13
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Rohani SC, Morin CE, Zhong X, Kannengiesser S, Shrestha U, Goode C, Holtrop J, Khan A, Loeffler RB, Hankins JS, Hillenbrand CM, Tipirneni-Sajja A. Hepatic Iron Quantification Using a Free-Breathing 3D Radial Gradient Echo Technique and Validation With a 2D Biopsy-Calibrated R 2* Relaxometry Method. J Magn Reson Imaging 2022; 55:1407-1416. [PMID: 34545639 PMCID: PMC10424632 DOI: 10.1002/jmri.27921] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Hepatic iron content (HIC) is an important parameter for the management of iron overload. Non-invasive HIC assessment is often performed using biopsy-calibrated two-dimensional breath-hold Cartesian gradient echo (2D BH GRE) R2* -MRI. However, breath-holding is not possible in most pediatric patients or those with respiratory problems, and three-dimensional free-breathing radial GRE (3D FB rGRE) has emerged as a viable alternative. PURPOSE To evaluate the performance of a 3D FB rGRE and validate its R2* and fat fraction (FF) quantification with 3D breath-hold Cartesian GRE (3D BH cGRE) and biopsy-calibrated 2D BH GRE across a wide range of HICs. STUDY TYPE Retrospective. SUBJECTS Twenty-nine patients with hepatic iron overload (22 females, median age: 15 [5-25] years). FIELD STRENGTH/SEQUENCE Three-dimensional radial and 2D and 3D Cartesian multi-echo GRE at 1.5 T. ASSESSMENT R2* and FF maps were computed for 3D GREs using a multi-spectral fat model and 2D GRE R2* maps were calculated using a mono-exponential model. Mean R2* and FF values were calculated via whole-liver contouring and T2* -thresholding by three operators. STATISTICAL TESTS Inter- and intra-observer reproducibility was assessed using Bland-Altman and intraclass correlation coefficient (ICC). Linear regression and Bland-Altman analysis were performed to compare R2* and FF values among the three acquisitions. One-way repeated-measures ANOVA and Wilcoxon signed-rank tests, respectively, were used to test for significant differences between R2* and FF values obtained with different acquisitions. Statistical significance was assumed at P < 0.05. RESULTS The mean biases and ICC for inter- and intra-observer reproducibility were close to 0% and >0.99, respectively for both R2* and FF. The 3D FB rGRE R2* and FF values were not significantly different (P > 0.44) and highly correlated (R2 ≥ 0.98) with breath-hold Cartesian GREs, with mean biases ≤ ±2.5% and slopes 0.90-1.12. In non-breath-holding patients, Cartesian GREs showed motion artifacts, whereas 3D FB rGRE exhibited only minimal streaking artifacts. DATA CONCLUSION Free-breathing 3D radial GRE is a viable alternative in non-breath-hold patients for accurate HIC estimation. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Shawyon Chase Rohani
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Cara E. Morin
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, USA
| | | | - Utsav Shrestha
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Chris Goode
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Joseph Holtrop
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Ayaz Khan
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | - Ralf B. Loeffler
- Research Imaging NSW, University of New South Wales, Sydney, Australia
| | - Jane S. Hankins
- Department of Hematology, St. Jude Children’s Research Hospital, Memphis, TN, USA
| | | | - Aaryani Tipirneni-Sajja
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN, USA
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14
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Liao Y, Li X, Jia F, Ye Z, Ning G, Liu S, Li P, Fu C, Li Q, Wang S, Zhang H, Qu H. Optimization of the image contrast for the developing fetal brain using 3D radial VIBE sequence in 3 T magnetic resonance imaging. BMC Med Imaging 2022; 22:11. [PMID: 35057733 PMCID: PMC8780316 DOI: 10.1186/s12880-022-00737-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/13/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Faster and motion robust magnetic resonance imaging (MRI) sequences are desirable in fetal brain MRI. T1-weighted images are essential for evaluating fetal brain development. We optimized the radial volumetric interpolated breath-hold examination (VIBE) sequence for qualitative T1-weighted images of the fetal brain with improved image contrast and reduced motion sensitivity. MATERIALS AND METHODS This was an institutional review board-approved prospective study. Thirty-five pregnant subjects underwent fetal brain scan at 3 Tesla MRI. T1-weighted images were acquired using a 3D radial VIBE sequence with flip angles of 6º, 9º, 12º, and 15º. T1-weighted images of Cartesian VIBE sequence were acquired in three of the subjects. Qualitative assessments including image quality and motion artifact severity were evaluated. The image contrast ratio between gray and white matter were measured. Interobserver reliability and intraobserver repeatability were assessed using intraclass correlation coefficient (ICC). RESULTS Interobserver reliability and intraobserver repeatability universally revealed almost perfect agreement (ICC > 0.800). Significant differences in image quality were detected in basal ganglia (P = 0.023), central sulcus (P = 0.028), myelination (P = 0.007) and gray matter (P = 0.023) among radial VIBE with flip angles 6º, 9º, 12º, 15º. Image quality at the 9º flip angle in radial VIBE was generally better than flip angle of 15º. Radial VIBE sequence with 9º flip angle of gray matter was significantly different by gestational age (GA) before and after 28 weeks (P = 0.036). Quantified image contrast was significantly different among different flip angles, consistent with qualitative analysis of image quality. CONCLUSIONS Three-dimensional radial VIBE with 9º flip angle provides optimal, stable T1-weighted images of the fetal brain. Fetal brain structure and development can be evaluated using high-quality images obtained using this angle. However, different scanners will achieve different TRs and so the FA should be re-optimized each time a new protocol is employed.
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Affiliation(s)
- Yi Liao
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Xuesheng Li
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Fenglin Jia
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Zhijun Ye
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Gang Ning
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Sai Liu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Pei Li
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Chuan Fu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China
| | - Qing Li
- MR Collaborations, Siemens Healthineers, Shanghai, People's Republic of China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, People's Republic of China
| | - Huapeng Zhang
- MR Application, Xi'an Branch of Siemens Healthineers, Shanxi, People's Republic of China
| | - Haibo Qu
- Department of Radiology, West China Second University Hospital of Sichuan University, Chengdu, Sichuan, People's Republic of China.
- Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
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15
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Kafali SG, Armstrong T, Shih SF, Kim GJ, Holtrop JL, Venick RS, Ghahremani S, Bolster BD, Hillenbrand CM, Calkins KL, Wu HH. Free-breathing radial magnetic resonance elastography of the liver in children at 3 T: a pilot study. Pediatr Radiol 2022; 52:1314-1325. [PMID: 35366073 PMCID: PMC9192470 DOI: 10.1007/s00247-022-05297-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 12/02/2021] [Accepted: 01/20/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Magnetic resonance (MR) elastography of the liver measures hepatic stiffness, which correlates with the histopathological staging of liver fibrosis. Conventional Cartesian gradient-echo (GRE) MR elastography requires breath-holding, which is challenging for children. Non-Cartesian radial free-breathing MR elastography is a potential solution to this problem. OBJECTIVE To investigate radial free-breathing MR elastography for measuring hepatic stiffness in children. MATERIALS AND METHODS In this prospective pilot study, 14 healthy children and 9 children with liver disease were scanned at 3 T using 2-D Cartesian GRE breath-hold MR elastography (22 s/slice) and 2-D radial GRE free-breathing MR elastography (163 s/slice). Each sequence was acquired twice. Agreement in the stiffness measurements was evaluated using Lin's concordance correlation coefficient (CCC) and within-subject mean difference. The repeatability was assessed using the within-subject coefficient of variation and intraclass correlation coefficient (ICC). RESULTS Fourteen healthy children and seven children with liver disease completed the study. Median (±interquartile range) normalized measurable liver areas were 62.6% (±26.4%) and 44.1% (±39.6%) for scan 1, and 60.3% (±21.8%) and 43.9% (±44.2%) for scan 2, for Cartesian and radial techniques, respectively. Hepatic stiffness from the Cartesian and radial techniques had close agreement with CCC of 0.89 and 0.94, and mean difference of 0.03 kPa and -0.01 kPa, for scans 1 and 2. Cartesian and radial techniques achieved similar repeatability with within-subject coefficient of variation=1.9% and 3.4%, and ICC=0.93 and 0.92, respectively. CONCLUSION In this pilot study, radial free-breathing MR elastography was repeatable and in agreement with Cartesian breath-hold MR elastography in children.
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Affiliation(s)
- Sevgi Gokce Kafali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA ,Department of Bioengineering, University of California Los Angeles, Los Angeles, CA USA
| | - Tess Armstrong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA
| | - Shu-Fu Shih
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA ,Department of Bioengineering, University of California Los Angeles, Los Angeles, CA USA
| | - Grace J. Kim
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA
| | - Joseph L. Holtrop
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN USA
| | - Robert S. Venick
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Shahnaz Ghahremani
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA
| | | | - Claudia M. Hillenbrand
- Department of Diagnostic Imaging, St. Jude Children’s Research Hospital, Memphis, TN USA ,Research Imaging NSW, University of New South Wales, Sydney, Australia
| | - Kara L. Calkins
- Department of Pediatrics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA USA
| | - Holden H. Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, 300 UCLA Medical Plaza, Suite B119, Los Angeles, CA 90095 USA ,Department of Bioengineering, University of California Los Angeles, Los Angeles, CA USA
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16
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Starekova J, Hernando D, Pickhardt PJ, Reeder SB. Quantification of Liver Fat Content with CT and MRI: State of the Art. Radiology 2021; 301:250-262. [PMID: 34546125 PMCID: PMC8574059 DOI: 10.1148/radiol.2021204288] [Citation(s) in RCA: 93] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Hepatic steatosis is defined as pathologically elevated liver fat content and has many underlying causes. Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an increasing prevalence among adults and children. Abnormal liver fat accumulation has serious consequences, including cirrhosis, liver failure, and hepatocellular carcinoma. In addition, hepatic steatosis is increasingly recognized as an independent risk factor for the metabolic syndrome, type 2 diabetes, and, most important, cardiovascular mortality. During the past 2 decades, noninvasive imaging-based methods for the evaluation of hepatic steatosis have been developed and disseminated. Chemical shift-encoded MRI is now established as the most accurate and precise method for liver fat quantification. CT is important for the detection and quantification of incidental steatosis and may play an increasingly prominent role in risk stratification, particularly with the emergence of CT-based screening and artificial intelligence. Quantitative imaging methods are increasingly used for diagnostic work-up and management of steatosis, including treatment monitoring. The purpose of this state-of-the-art review is to provide an overview of recent progress and current state of the art for liver fat quantification using CT and MRI, as well as important practical considerations related to clinical implementation.
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Affiliation(s)
- Jitka Starekova
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Diego Hernando
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Perry J Pickhardt
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
| | - Scott B Reeder
- From the Departments of Radiology (J.S., D.H., P.J.P., S.B.R.), Medical Physics (D.H., S.B.R.), Biomedical Engineering (S.B.R.), Medicine (S.B.R.), and Emergency Medicine (S.B.R.), University of Wisconsin, 1111 Highland Ave, Madison, WI 53705
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17
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Armstrong T, Zhong X, Shih SF, Felker E, Lu DS, Dale BM, Wu HH. Free-breathing 3D stack-of-radial MRI quantification of liver fat and R 2* in adults with fatty liver disease. Magn Reson Imaging 2021; 85:141-152. [PMID: 34662702 DOI: 10.1016/j.mri.2021.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 10/07/2021] [Accepted: 10/12/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the agreement, intra-session repeatability, and inter-reader agreement of liver proton-density fat fraction (PDFF) and R2* quantification using free-breathing 3D stack-of-radial MRI, with and without self-gated motion compensation, compared to reference breath-hold techniques in subjects with fatty liver disease (FLD). METHODS In this institutional review board-approved prospective study, thirty-eight adults with FLD and/or iron overload (24 male, 58 ± 12 years) were imaged at 3T using free-breathing stack-of-radial MRI, breath-hold 3D Cartesian MRI, and breath-hold single-voxel MR spectroscopy (SVS). Each sequence was acquired twice in random order. To assess agreement compared to reference breath-hold techniques, the dependency of liver PDFF and/or R2* quantification on the sequence, radial sampling factor, and radial self-gating temporal resolution was assessed by calculating the Bayesian mean difference (MDB) of the posteriors. Intra-session repeatability and inter-reader agreement (two independent readers) were assessed by the coefficient of repeatability (CR) and intraclass correlation coefficient (ICC), respectively. RESULTS Thirty-five participants (21 male, 57 ± 12 years) were included for analysis. Both free-breathing radial MRI techniques (with and without self-gating) achieved ICC ≥ 0.92 for quantifying PDFF and R2*, and quantified PDFF with MDB < 1.2% compared to breath-hold techniques. Free-breathing radial MRI required self-gating to accurately quantify R2* (MDB < 10s-1 with self-gating; MDB < 50s-1 without self-gating). The radial sampling factor affected PDFF and R2* quantification while the radial self-gating temporal resolution only affected R2* quantification. Repeated self-gated free-breathing radial MRI scans achieved CR < 3% and CR < 27 s-1 for PDFF and R2*, respectively. CONCLUSION A free-breathing stack-of-radial MRI technique with self-gating demonstrated agreement, repeatability, and inter-reader agreement compared to reference breath-hold techniques for quantification of liver PDFF and R2* in adults with FLD.
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Affiliation(s)
- Tess Armstrong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Xiaodong Zhong
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Los Angeles, CA, United States
| | - Shu-Fu Shih
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States
| | - Ely Felker
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - David S Lu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States
| | - Brian M Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc., Cary, NC, United States
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States; Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, United States.
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18
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Zhong X, Armstrong T, Gao C, Nickel MD, Han F, Dale BM, Li X, Kafali SG, Hu P, Wu HH, Deshpande V. Accelerated k-space shift calibration for free-breathing stack-of-radial MRI quantification of liver fat and R 2 ∗. Magn Reson Med 2021; 87:281-291. [PMID: 34412158 DOI: 10.1002/mrm.28981] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 07/29/2021] [Accepted: 08/02/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To develop an accelerated k-space shift calibration method for free-breathing 3D stack-of-radial MRI quantification of liver proton-density fat fraction (PDFF) and R 2 ∗ . METHODS Accelerated k-space shift calibration was developed to partially skip acquisition of k-space shift data in the through-plane direction then interpolate in processing, as well as to reduce the in-plane averages. A multi-echo stack-of-radial sequence with the baseline calibration was evaluated on a phantom versus vendor-provided reference-standard PDFF and R 2 ∗ values at 1.5T, and in 13 healthy subjects and 5 clinical subjects at 3T with respect to reference-standard breath-hold Cartesian acquisitions. PDFF and R 2 ∗ maps were calculated with different calibration acceleration factors offline and compared to reference-standard values using Bland-Altman analysis. Bias and uncertainty were evaluated using normal distribution and Bayesian probability of difference (P < .05 considered significant). RESULTS Bland-Altman plots of phantom and in vivo data showed that substantial acceleration was highly feasible in both through-plane and in-plane directions. Compared to the baseline calibration without acceleration, Bayesian analysis revealed no significant differences on biases and uncertainties of PDFF and R 2 ∗ measurements with all acceleration methods in this study, except the method with through-plane acceleration equaling slices and averages equaling 20 for PDFF and R 2 ∗ (both P < .001) for the phantom. A six-fold reduction in equivalent calibration acquisition time (time saving ≥25 s and ≥80.7%) was achieved using recommended acceleration factors for the in vivo protocols in this study. CONCLUSION This proposed method may allow accelerated calibration for free-breathing stack-of-radial MRI PDFF and R 2 ∗ mapping.
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Affiliation(s)
- Xiaodong Zhong
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Tess Armstrong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Chang Gao
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Marcel D Nickel
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Fei Han
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Los Angeles, California, USA
| | - Brian M Dale
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Cary, North Carolina, USA
| | - Xinzhou Li
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Sevgi G Kafali
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Peng Hu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA.,Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA
| | - Vibhas Deshpande
- MR R&D Collaborations, Siemens Medical Solutions USA, Inc, Austin, Texas, USA
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19
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Duffy PB, Stemmer A, Callahan MJ, Cravero JP, Johnston PR, Warfield SK, Bixby SD. Free-breathing radial stack-of-stars three-dimensional Dixon gradient echo sequence in abdominal magnetic resonance imaging in sedated pediatric patients. Pediatr Radiol 2021; 51:1645-1653. [PMID: 33830291 DOI: 10.1007/s00247-021-05054-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/30/2021] [Accepted: 03/16/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND There is a strong need for improvements in motion robust T1-weighted abdominal imaging sequences in children to enable high-quality, free-breathing imaging. OBJECTIVE To compare imaging time and quality of a radial stack-of-stars, free-breathing T1-weighted gradient echo acquisition (volumetric interpolated breath-hold examination [VIBE]) three-dimensional (3-D) Dixon sequence in sedated pediatric patients undergoing abdominal magnetic resonance imaging (MRI) against conventional Cartesian T1-weighed sequences. MATERIALS AND METHODS This study was approved by the institutional review board with informed consent obtained from all subjects. Study subjects included 31 pediatric patients (19 male, 12 female; median age: 5 years; interquartile range: 5 years) undergoing abdominal MRI at 3 tesla with a free-breathing T1-weighted radial stack-of-stars 3-D VIBE Dixon prototype sequence, StarVIBE Dixon (radial technique), between October 2018 and June 2019 with previous abdominal MR imaging using conventional Cartesian T1-weighed imaging (traditional technique). MRI component times were recorded as well as the total number of non-contrast T1-weighted sequences. Two radiologists independently rated images for quality using a scale from 1 to 5 according to the following metrics: overall image quality, hepatic edge sharpness, hepatic vessel clarity and respiratory motion robustness. Scores were compared between the groups. RESULTS Mean T1-weighted imaging times for all subjects were 3.63 min for radial exams and 8.01 min for traditional exams (P<0.001), and total non-contrast imaging time was 32.7 min vs. 43.9 min (P=0.002). Adjusted mean total MRI time for all subjects was 60.2 min for radial exams and 65.7 min for traditional exams (P=0.387). The mean number of non-contrast T1-weighted sequences performed in radial MRI exams was 1.0 compared to 1.9 (range: 0-6) in traditional exams (P<0.001). StarVIBE Dixon outperformed Cartesian methods in all quality metrics. The mean overall image quality (scale 1-5) was 3.95 for radial exams and 3.31 for traditional exams (P<0.001). CONCLUSION Radial stack-of-stars 3-D VIBE Dixon during free-breathing abdominal MRI in pediatric patients offers improved image quality compared to Cartesian T1-weighted imaging techniques with decreased T1-weighted and total non-contrast imaging time. This has important implications for children undergoing sedation for imaging.
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Affiliation(s)
- Patrick B Duffy
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | | | - Michael J Callahan
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Joseph P Cravero
- Department of Anesthesiology, Boston Children's Hospital, Boston, MA, USA
| | - Patrick R Johnston
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Sarah D Bixby
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA.
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20
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Sandhu S, Orsi C, Francis GL, Wang Z, Fernandez R, Alkhouri N. Shear wave elastography reveals a high prevalence of liver fibrosis in overweight or obese Hispanic youth. J Ultrason 2020; 20:e162-e168. [PMID: 33365151 PMCID: PMC7705483 DOI: 10.15557/jou.2020.0027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 07/07/2020] [Indexed: 12/05/2022] Open
Abstract
Background: Obesity, prediabetes, and type 2 diabetes are risk factors for nonalcoholic fatty liver disease. Inflammation and hepatocellular damage associated with nonalcoholic fatty liver disease lead to progressive non-alcoholic steatohepatitis, fibrosis and cirrhosis. Current tests to identify fibrosis (liver biopsy) are invasive and not conducive to serial examination. For that reason, we used the newer technique of shear wave elastogrophy (SWE) to detect fibrosis in overweight or obese Hispanic youth and sought to determine if carbohydrate tolerance or insulin resistance were associated with fibrosis in this high risk population. Methods: A total of 67 Hispanic youth (8-18 years of age) with overweight or obesity who were referred for multidisciplinary evaluation were included. SWE was used to identify those with suspected fibrosis. Results of SWE were then compared with glycohemoglobin (A1c), insulin resistance (homeostatic model of insulin resistance), and biochemical parameters. Results: The prevalence of suspected fibrosis (SWE >5.10 kPa) in overweight or obese Hispanic youth was 62.7% (42/67). Patients with suspected fibrosis (SWE ≥5.10 kPa) had significantly higher levels of serum aspartate aminotransferase, alanine aminotransferase and the aminotransferase to platelet ratio index when compared to patients without significant fibrosis (SWE <5.01 kPa). However, there were no significant differences between the groups in body mass index, A1c, or homeostatic model of insulin resistance. Conclusions: SWE detected a high prevalence (62.7%) of suspected hepatic fibrosis in a group of high risk, overweight or obese Hispanic youth suggesting that SWE is a useful tool for surveillance and longitudinal studies.
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Affiliation(s)
- Sanjeet Sandhu
- University of Texas Health and Science Center, Department of Pediatric Endocrinology, San Antonio, TX, United States
| | - Carisse Orsi
- University of Texas Health and Science Center, Department of Pediatric Endocrinology, San Antonio, TX, United States
| | - Gary L Francis
- University of Texas Health and Science Center, Department of Pediatric Endocrinology, San Antonio, TX, United States
| | - Zhu Wang
- University of Texas Health and Science Center Department of Population Health Sciences, San San Antonio TX, United States
| | - Roman Fernandez
- University of Texas Health and Science Center Department of Population Health Sciences, San San Antonio TX, United States
| | - Naim Alkhouri
- Texas Liver Institute and University of Texas Health, Department of Pediatric Gastroenterology, San Antonio, TX, United States
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21
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Yoon H, Shin HJ, Kim MJ, Lee MJ. Quantitative Imaging in Pediatric Hepatobiliary Disease. Korean J Radiol 2020; 20:1342-1357. [PMID: 31464113 PMCID: PMC6715564 DOI: 10.3348/kjr.2019.0002] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 06/11/2019] [Indexed: 02/07/2023] Open
Abstract
Pediatric hepatobiliary imaging is important for evaluation of not only congenital or structural disease but also metabolic or diffuse parenchymal disease and tumors. A variety of ultrasonography and magnetic resonance imaging (MRI) techniques can be used for these assessments. In ultrasonography, conventional ultrasound imaging as well as vascular imaging, elastography, and contrast-enhanced ultrasonography can be used, while in MRI, fat quantification, T2/T2* mapping, diffusion-weighted imaging, magnetic resonance elastography, and dynamic contrast-enhanced MRI can be performed. These techniques may be helpful for evaluation of biliary atresia, hepatic fibrosis, nonalcoholic fatty liver disease, sinusoidal obstruction syndrome, and hepatic masses in children. In this review, we discuss each tool in the context of management of hepatobiliary disease in children, and cover various imaging techniques in the context of the relevant physics and their clinical applications for patient care.
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Affiliation(s)
- Haesung Yoon
- Department of Radiology, Severance Hospital, Severance Pediatric Liver Disease Research Group, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Hyun Joo Shin
- Department of Radiology, Severance Hospital, Severance Pediatric Liver Disease Research Group, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Myung Joon Kim
- Department of Radiology, Severance Hospital, Severance Pediatric Liver Disease Research Group, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea
| | - Mi Jung Lee
- Department of Radiology, Severance Hospital, Severance Pediatric Liver Disease Research Group, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Korea.
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22
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Chan WY, Hartono S, Thng CH, Koh DM. New Advances in Magnetic Resonance Techniques in Abdomen and Pelvis. Magn Reson Imaging Clin N Am 2020; 28:433-445. [PMID: 32624160 DOI: 10.1016/j.mric.2020.04.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
This article explores new acquisition methods in magnetic resonance (MR) imaging to provide high spatial and temporal resolution imaging for a wide spectrum of clinical applications in the abdomen and pelvis. We present an overview of some of these advanced MR techniques, such as non-cartesian image acquisition, fast sampling and compressed sensing, diffusion quantification and quantitative MR that can improve data sampling, enhance image quality, yield quantitative measurements, and/or optimize diagnostic performance in the body.
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Affiliation(s)
- Wan Ying Chan
- Division of Oncologic Imaging, National Cancer Centre, 11 Hospital Crescent, Singapore 169610, Singapore
| | - Septian Hartono
- Department of Neurology, National Neuroscience Institute, Singapore, 11 Jln Tan Tock Seng, Singapore 308433, Singapore
| | - Choon Hua Thng
- Division of Oncologic Imaging, National Cancer Centre, 11 Hospital Crescent, Singapore 169610, Singapore
| | - Dow-Mu Koh
- Department of Radiology, Royal Marsden Hospital, Downs Road, Sutton SM2 5PT, UK.
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23
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Zhong X, Hu HH, Armstrong T, Li X, Lee Y, Tsao T, Nickel MD, Kannengiesser SA, Dale BM, Deshpande V, Kiefer B, Wu HH. Free‐Breathing Volumetric Liver and Proton Density Fat Fraction Quantification in Pediatric Patients Using Stack‐of‐Radial
MRI
With Self‐Gating Motion Compensation. J Magn Reson Imaging 2020; 53:118-129. [DOI: 10.1002/jmri.27205] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/06/2020] [Accepted: 05/07/2020] [Indexed: 01/28/2023] Open
Affiliation(s)
- Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthcare Los Angeles California USA
| | - Houchun H. Hu
- Department of Radiology Nationwide Children's Hospital Columbus Ohio USA
- Clinical Science, Hyperfine Guilford Connecticut USA
| | - Tess Armstrong
- Department of Radiological Sciences, David Geffen School of Medicine University of California Los Angeles Los Angeles California USA
| | - Xinzhou Li
- Department of Radiological Sciences, David Geffen School of Medicine University of California Los Angeles Los Angeles California USA
- Department of Bioengineering University of California Los Angeles Los Angeles California USA
| | - Yu‐Hsiu Lee
- Department of Mechanical and Aerospace Engineering University of California Los Angeles Los Angeles California USA
| | - Tsu‐Chin Tsao
- Department of Mechanical and Aerospace Engineering University of California Los Angeles Los Angeles California USA
| | - Marcel D. Nickel
- MR Application Development, Siemens Healthcare GmbH Erlangen Germany
| | | | - Brian M. Dale
- MR R&D Collaborations, Siemens Healthcare Cary North Carolina USA
| | | | - Berthold Kiefer
- MR Application Development, Siemens Healthcare GmbH Erlangen Germany
| | - Holden H. Wu
- Department of Radiological Sciences, David Geffen School of Medicine University of California Los Angeles Los Angeles California USA
- Department of Bioengineering University of California Los Angeles Los Angeles California USA
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24
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Schneider M, Benkert T, Solomon E, Nickel D, Fenchel M, Kiefer B, Maier A, Chandarana H, Block KT. Free-breathing fat and R 2 * quantification in the liver using a stack-of-stars multi-echo acquisition with respiratory-resolved model-based reconstruction. Magn Reson Med 2020; 84:2592-2605. [PMID: 32301168 PMCID: PMC7396291 DOI: 10.1002/mrm.28280] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 03/22/2020] [Accepted: 03/23/2020] [Indexed: 01/04/2023]
Abstract
Purpose To develop a free‐breathing hepatic fat and
R2∗ quantification method by extending a previously described stack‐of‐stars model‐based fat‐water separation technique with additional modeling of the transverse relaxation rate
R2∗. Methods The proposed technique combines motion‐robust radial sampling using a stack‐of‐stars bipolar multi‐echo 3D GRE acquisition with iterative model‐based fat‐water separation. Parallel‐Imaging and Compressed‐Sensing principles are incorporated through modeling of the coil‐sensitivity profiles and enforcement of total‐variation (TV) sparsity on estimated water, fat, and
R2∗ parameter maps. Water and fat signals are used to estimate the confounder‐corrected proton‐density fat fraction (PDFF). Two strategies for handling respiratory motion are described: motion‐averaged and motion‐resolved reconstruction. Both techniques were evaluated in patients (n = 14) undergoing a hepatobiliary research protocol at 3T. PDFF and
R2∗ parameter maps were compared to a breath‐holding Cartesian reference approach. Results Linear regression analyses demonstrated strong (r > 0.96) and significant (P ≪ .01) correlations between radial and Cartesian PDFF measurements for both the motion‐averaged reconstruction (slope: 0.90; intercept: 0.07%) and the motion‐resolved reconstruction (slope: 0.90; intercept: 0.11%). The motion‐averaged technique overestimated hepatic
R2∗ values (slope: 0.35; intercept: 30.2 1/s) compared to the Cartesian reference. However, performing a respiratory‐resolved reconstruction led to better
R2∗ value consistency (slope: 0.77; intercept: 7.5 1/s). Conclusions The proposed techniques are promising alternatives to conventional Cartesian imaging for fat and
R2∗ quantification in patients with limited breath‐holding capabilities. For accurate
R2∗ estimation, respiratory‐resolved reconstruction should be used.
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Affiliation(s)
- Manuel Schneider
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen Nürnberg, Erlangen, Germany.,MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Thomas Benkert
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Eddy Solomon
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dominik Nickel
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Matthias Fenchel
- MR R&D Collaborations, Siemens Medical Solutions, New York, NY, USA
| | - Berthold Kiefer
- MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen Nürnberg, Erlangen, Germany
| | - Hersh Chandarana
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Kai Tobias Block
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
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25
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Kozak BM, Jaimes C, Kirsch J, Gee MS. MRI Techniques to Decrease Imaging Times in Children. Radiographics 2020; 40:485-502. [PMID: 32031912 DOI: 10.1148/rg.2020190112] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Long acquisition times can limit the use of MRI in pediatric patients, and the use of sedation or general anesthesia is frequently necessary to facilitate diagnostic examinations. The use of sedation or anesthesia has disadvantages including increased cost and imaging time and potential risks to the patient. Reductions in imaging time may decrease or eliminate the need for sedation or general anesthesia. Over the past decade, a number of imaging techniques that can decrease imaging time have become commercially available. These products have been used increasingly in clinical practice and include parallel imaging, simultaneous multisection imaging, radial k-space acquisition, compressed sensing MRI reconstruction, and automated protocol selection software. The underlying concepts, supporting data, current clinical applications, and available products for each of these strategies are reviewed in this article. In addition, emerging techniques that are still under investigation may provide further reductions in imaging time, including artificial intelligence-based reconstruction, gradient-controlled aliasing sampling and reconstruction, three-dimensional MR spectroscopy, and prospective motion correction. The preliminary results for these techniques are also discussed. ©RSNA, 2020 See discussion on this article by Greer and Vasanawala.
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Affiliation(s)
- Benjamin M Kozak
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - John Kirsch
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
| | - Michael S Gee
- From the Department of Radiology, Massachusetts General Hospital, 55 Fruit St, Founders 210, Boston, MA 02114 (B.M.K., J.K., M.S.G.); Department of Radiology, Harvard Medical School, Boston, Mass (B.M.K., C.J., J.K., M.S.G.); and Department of Radiology, Boston Children's Hospital, Boston, Mass (C.J.)
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26
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Hu HH, Branca RT, Hernando D, Karampinos DC, Machann J, McKenzie CA, Wu HH, Yokoo T, Velan SS. Magnetic resonance imaging of obesity and metabolic disorders: Summary from the 2019 ISMRM Workshop. Magn Reson Med 2019; 83:1565-1576. [PMID: 31782551 DOI: 10.1002/mrm.28103] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 11/04/2019] [Accepted: 11/11/2019] [Indexed: 02/06/2023]
Abstract
More than 100 attendees from Australia, Austria, Belgium, Canada, China, Germany, Hong Kong, Indonesia, Japan, Malaysia, the Netherlands, the Philippines, Republic of Korea, Singapore, Sweden, Switzerland, the United Kingdom, and the United States convened in Singapore for the 2019 ISMRM-sponsored workshop on MRI of Obesity and Metabolic Disorders. The scientific program brought together a multidisciplinary group of researchers, trainees, and clinicians and included sessions in diabetes and insulin resistance; an update on recent advances in water-fat MRI acquisition and reconstruction methods; with applications in skeletal muscle, bone marrow, and adipose tissue quantification; a summary of recent findings in brown adipose tissue; new developments in imaging fat in the fetus, placenta, and neonates; the utility of liver elastography in obesity studies; and the emerging role of radiomics in population-based "big data" studies. The workshop featured keynote presentations on nutrition, epidemiology, genetics, and exercise physiology. Forty-four proffered scientific abstracts were also presented, covering the topics of brown adipose tissue, quantitative liver analysis from multiparametric data, disease prevalence and population health, technical and methodological developments in data acquisition and reconstruction, newfound applications of machine learning and neural networks, standardization of proton density fat fraction measurements, and X-nuclei applications. The purpose of this article is to summarize the scientific highlights from the workshop and identify future directions of work.
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Affiliation(s)
- Houchun H Hu
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio
| | - Rosa Tamara Branca
- Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Diego Hernando
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin.,Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany
| | - Jürgen Machann
- Institute for Diabetes Research and Metabolic Diseases, Helmholtz Center Munich at the University of Tübingen, Tübingen, Germany.,German Center for Diabetes Research, Tübingen, Germany.,Section on Experimental Radiology, Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Tübingen, Germany
| | - Charles A McKenzie
- Department of Medical Biophysics, University of Western Ontario, London, Ontario, Canada
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California
| | - Takeshi Yokoo
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - S Sendhil Velan
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore.,Singapore BioImaging Consortium, Agency for Science Technology and Research, Singapore
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27
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Zhong X, Armstrong T, Nickel MD, Kannengiesser SAR, Pan L, Dale BM, Deshpande V, Kiefer B, Wu HH. Effect of respiratory motion on free-breathing 3D stack-of-radial liver R 2 ∗ relaxometry and improved quantification accuracy using self-gating. Magn Reson Med 2019; 83:1964-1978. [PMID: 31682016 DOI: 10.1002/mrm.28052] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2019] [Revised: 09/12/2019] [Accepted: 10/05/2019] [Indexed: 12/20/2022]
Abstract
PURPOSE To develop an accurate free-breathing 3D liver R 2 ∗ mapping approach and to evaluate it in vivo. METHODS A free-breathing multi-echo stack-of-radial sequence was applied in 5 normal subjects and 6 patients at 3 Tesla. Respiratory motion compensation was implemented using the inherent self-gating signal. A breath-hold Cartesian acquisition was the reference standard. Proton density fat fraction and R 2 ∗ were measured and compared between radial and Cartesian methods using Bland-Altman plots. The normal subject results were fitted to a linear mixed model (P < .05 considered significant). RESULTS Free-breathing stack-of-radial without self-gating exhibited signal attenuation in echo images and artifactually elevated apparent R 2 ∗ values. In the Bland-Altman plots of normal subjects, compared to breath-hold Cartesian, free-breathing stack-of-radial acquisitions of 22, 30, 36, and 44 slices, had mean R 2 ∗ differences of 27.4, 19.4, 10.9, and 14.7 s-1 with 800 radial views, and they had 18.4, 11.9, 9.7, and 27.7 s-1 with 404 views, which were reduced to 0.4, 0.9, -0.2, and -0.7 s-1 and to -1.7, -1.9, -2.1, and 0.5 s-1 with self-gating, respectively. No substantial proton density fat fraction differences were found. The linear mixed model showed free-breathing radial R 2 ∗ results without self-gating were significantly biased by 17.2 s-1 averagely (P = .002), which was eliminated with self-gating (P = .930). Proton density fat fraction results were not different (P > .234). For patients, Bland-Altman plots exhibited mean R 2 ∗ differences of 14.4 and 0.1 s-1 for free-breathing stack-of-radial without self-gating and with self-gating, respectively, but no substantial proton density fat fraction differences. CONCLUSION The proposed self-gating method corrects the respiratory motion bias and enables accurate free-breathing stack-of-radial quantification of liver R 2 ∗ .
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Affiliation(s)
- Xiaodong Zhong
- MR R&D Collaborations, Siemens Healthcare, Los Angeles, California
| | - Tess Armstrong
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Marcel D Nickel
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Li Pan
- MR R&D Collaborations, Siemens Healthcare, Baltimore, Maryland
| | - Brian M Dale
- MR R&D Collaborations, Siemens Healthcare, Cary, North Carolina
| | | | - Berthold Kiefer
- MR Application Development, Siemens Healthcare GmbH, Erlangen, Germany
| | - Holden H Wu
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.,Physics and Biology in Medicine Interdepartmental Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
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Free-breathing Magnetic Resonance Imaging Assessment of Body Composition in Healthy and Overweight Children: An Observational Study. J Pediatr Gastroenterol Nutr 2019; 68:782-787. [PMID: 30789865 PMCID: PMC6752952 DOI: 10.1097/mpg.0000000000002309] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
OBJECTIVE Conventional, breath-holding magnetic resonance imaging (MRI) assesses body composition by measuring fat volumes and proton density fat fraction (PDFF). However, breath-holding MRI is not always feasible in children. This study's objective was to use free-breathing MRI to quantify visceral and subcutaneous fat volumes and PDFFs and correlate these measurements with hepatic PDFF. METHODS This was an observational, hypothesis-forming study that enrolled 2 groups of children (ages 6-17 years), healthy children and overweight children with presumed nonalcoholic fatty liver disease. Free-breathing MRI was used to measure visceral and subcutaneous fat volumes and PDFFs, and hepatic PDFF. Imaging biomarkers were compared between groups, and correlations coefficients (r) and coefficients of determination (R) were calculated. RESULTS When compared with the control group (n = 10), the overweight group (n = 9) had greater mean visceral (1843 vs 329 cm, P < 0.001) and subcutaneous fat volumes (7663 vs 893 cm, P < 0.001), as well as greater visceral (80% vs 45%, p < 0.001) and subcutaneous fat PDFFs (89% vs 75%, P = 0.003). Visceral fat volume (r = 0.79, P < 0.001) and PDFF (r = 0.92, P < 0.001) correlated with hepatic PDFF. In overweight subjects, for each unit increase in visceral fat PDFF, hepatic PDFF increased by 2.64%; visceral fat PDFF explained 54% of hepatic PDFF variation (R = 0.54, P = 0.02). CONCLUSIONS In this study, we used free-breathing MRI to measure body composition in children. Future studies are needed to investigate the possible value of subcutaneous and visceral fat PDFFs, and validate free-breathing MRI body composition biomarkers.
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29
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Armstrong T, Ly KV, Ghahremani S, Calkins KL, Wu HH. Free-breathing 3-D quantification of infant body composition and hepatic fat using a stack-of-radial magnetic resonance imaging technique. Pediatr Radiol 2019; 49:876-888. [PMID: 31001664 DOI: 10.1007/s00247-019-04384-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Revised: 02/12/2019] [Accepted: 03/08/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Body composition and hepatic fat correlate with future risk for metabolic syndrome. In children, many conventional techniques for quantifying body composition and hepatic fat have limitations. MRI is a noninvasive research tool to study body composition and hepatic fat in infants; however, conventional Cartesian MRI is sensitive to motion, particularly in the abdomen because of respiration. Therefore we developed a free-breathing MRI technique to quantify body composition and hepatic fat in infants. OBJECTIVE In infants, we aimed to (1) compare the image quality between free-breathing 3-D stack-of-radial MRI (free-breathing radial) and 3-D Cartesian MRI in the liver and (2) determine the feasibility of using free-breathing radial MRI to quantify body composition and hepatic proton-density fat fraction (PDFF). MATERIALS AND METHODS Ten infants ages 2-7 months were scanned with free-breathing radial (two abdominal; one head and chest) and Cartesian (one abdominal) MRI sequences. The median preparation and scan times were reported. To assess feasibility for hepatic PDFF quantification, a radiologist masked to the MRI technique scored abdominal scans for motion artifacts in the liver using a 3-point scale (1, or non-diagnostic, to 3, or no artifacts). Median visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and brown adipose tissue (BAT) volume and PDFF, and hepatic PDFF were measured using free-breathing radial MRI. We assessed repeatability of free-breathing radial hepatic PDFF (coefficient of repeatability) between back-to-back scans. We determined differences in the distribution of image-quality scores using McNemar-Bowker tests. P<0.05 was considered significant. RESULTS Nine infants completed the entire study (90% completion). For ten infants, the median preparation time was 32 min and scan time was 24 min. Free-breathing radial MRI demonstrated significantly higher image-quality scores compared to Cartesian MRI in the liver (radial scan 1 median = 2 and radial scan 2 median = 3 vs. Cartesian median = 1; P=0.01). Median measurements using free-breathing radial were VAT=52.0 cm3, VAT-PDFF=42.2%, SAT=267.7 cm3, SAT-PDFF=87.1%, BAT=1.4 cm3, BAT-PDFF=26.1% and hepatic PDFF=3.4% (coefficient of repeatability <2.0%). CONCLUSION In this study, free-breathing radial MRI in infants achieved significantly improved liver image quality compared to Cartesian MRI. It is feasible to use free-breathing radial MRI to quantify body composition and hepatic fat in infants.
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Affiliation(s)
- Tess Armstrong
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Ste. B119, Los Angeles, CA, 90095, USA.,Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Karrie V Ly
- Department of Pediatrics, Division of Neonatology, David Geffen School of Medicine, University of California Los Angeles, Mattel Children's Hospital, Los Angeles, CA, USA.,Physician Assistant Program, Midwestern University, Glendale, AZ, USA
| | - Shahnaz Ghahremani
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Ste. B119, Los Angeles, CA, 90095, USA
| | - Kara L Calkins
- Department of Pediatrics, Division of Neonatology, David Geffen School of Medicine, University of California Los Angeles, Mattel Children's Hospital, Los Angeles, CA, USA
| | - Holden H Wu
- Department of Radiological Sciences, University of California Los Angeles, 300 UCLA Medical Plaza, Ste. B119, Los Angeles, CA, 90095, USA. .,Physics and Biology in Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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30
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Hu HH, Benkert T, Jones JY, McAllister AS, Rusin JA, Krishnamurthy R, Block KT. 3D T1-weighted contrast-enhanced brain MRI in children using a fat-suppressed golden angle radial acquisition: an alternative to Cartesian inversion-recovery imaging. Clin Imaging 2019; 55:112-118. [DOI: 10.1016/j.clinimag.2019.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 09/18/2018] [Accepted: 02/08/2019] [Indexed: 02/07/2023]
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31
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Post-contrast T1-weighted spine 3T MRI in children using a golden-angle radial acquisition. Neuroradiology 2019; 61:341-349. [DOI: 10.1007/s00234-019-02165-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Accepted: 01/09/2019] [Indexed: 11/26/2022]
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32
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Janos S, Schooler GR, Ngo JS, Davis JT. Free-breathing unsedated MRI in children: Justification and techniques. J Magn Reson Imaging 2019; 50:365-376. [DOI: 10.1002/jmri.26644] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 12/20/2018] [Accepted: 12/20/2018] [Indexed: 12/16/2022] Open
Affiliation(s)
- Sara Janos
- Department of Radiology; Duke University Medical Center; Durham North Carolina USA
| | - Gary R. Schooler
- Department of Radiology; Duke University Medical Center; Durham North Carolina USA
| | - Jennifer S. Ngo
- Department of Radiology; Duke University Medical Center; Durham North Carolina USA
| | - Joseph T. Davis
- Department of Radiology; Duke University Medical Center; Durham North Carolina USA
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