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Zheng G, Wei F, Lu P, Yang G, Li C, Lin C, Zhou Y, Chen Y, Tian J, Wang X, Wang L, Liu W, Zhang G, Cai Q, Huang H, Yun Y. IDEAL-IQ measurement can distinguish dysplastic nodule from early hepatocellular carcinoma: a case-control study. Quant Imaging Med Surg 2024; 14:3901-3913. [PMID: 38846285 PMCID: PMC11151266 DOI: 10.21037/qims-23-1593] [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: 11/09/2023] [Accepted: 03/25/2024] [Indexed: 06/09/2024]
Abstract
Background Previous studies have confirmed that malignant transformation of dysplastic nodule (DN) into hepatocellular carcinoma (HCC) is accompanied by reduction of iron content in nodules. This pathological abnormality can serve as the basis for magnetic resonance imaging (MRI). This study was designed to identify the feasibility of iterative decomposition of water and fat with echo asymmetry and least squares estimation-iron quantitative (IDEAL-IQ) measurement to distinguish early hepatocellular carcinoma (eHCC) from DN. Methods We reviewed MRI studies of 35 eHCC and 23 DN lesions (46 participants with 58 lesions total, 37 males, 9 females, 31-80 years old). The exams include IDEAL-IQ sequence and 3.0T MR conventional scan [including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and Gadopentic acid (Gd-GDPA)-enhanced]. Then, 3 readers independently diagnosed eHCC, DN, or were unable to distinguish eHCC from DN using conventional MRI (CMRI), and then assessed R2* value of nodules [R2* value represents the nodule iron content (NIC)] and R2* value of liver background [R2* value represents the liver background iron content (LBIC)] with IDEAL-IQ. Statistical analysis was conducted using the t-test for comparison of means, the Mann-Whitney test for comparison of medians, the chi-square test for comparison of frequencies, and diagnostic efficacy was evaluated by using receiver operating characteristic (ROC) curve. Results This study evaluated 35 eHCC participants (17 males, 6 females, 34-81 years old, nodule size: 10.5-27.6 mm, median 18.0 mm) and 23 DN participants (20 males, 3 females, 31-76 years old, nodule size: 16.30±4.095 mm). The NIC and ratio of NIC to LIBC (NIC/LBIC) of the eHCC group (35.926±12.806 sec-1, 0.327±0.107) was lower than that of the DN group (176.635±87.686 sec-1, 1.799±0.629) (P<0.001). Using NIC and NIC/LBIC to distinguish eHCC from DN, the true positive/false positive rates were 91.3%/94.3% and 87.0%/97.1%, respectively. The rates of CMRI, NIC and NIC/LBIC in diagnosis of eHCC were 77.1%, and 94.3%, 97.1%, respectively, and those of DN were 65.2%, 91.3%, and 87.0%, respectively. The diagnosis rate of eHCC and DN by CMRI was lower than that of NIC and NIC/LBIC (eHCC: P=0.03, 0.04, DN: P=0.02, 0.04). Conclusions Using IDEAL-IQ measurement can distinguish DN from eHCC.
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Affiliation(s)
- Guangping Zheng
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Fangjun Wei
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Puxuan Lu
- Department of Radiology, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Gendong Yang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Cuizu Li
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Chunming Lin
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yun Zhou
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yixin Chen
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Jianing Tian
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Xiaolei Wang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Linjing Wang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Wenhao Liu
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Guangfeng Zhang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Qingxian Cai
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Hua Huang
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
| | - Yongxing Yun
- Department of Radiology, Shenzhen Third People’s Hospital, Shenzhen, China
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Neupane P, Shrestha U, Brasher S, Abramson Z, Tipirneni-Sajja A. Simulation of a virtual liver iron overload model and R 2 * estimation using multispectral fat-water models for GRE and UTE acquisitions at 1.5 T and 3 T. NMR IN BIOMEDICINE 2023; 36:e5018. [PMID: 37539770 PMCID: PMC10838367 DOI: 10.1002/nbm.5018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 08/05/2023]
Abstract
R2 *-MRI has emerged as a noninvasive alternative to liver biopsy for assessment of hepatic iron content (HIC). Multispectral fat-water R2 * modeling techniques such as the nonlinear least squares (NLSQ) fitting and autoregressive moving average (ARMA) models have been proposed for the accurate assessment of iron overload by also considering fat, which can otherwise confound R2 *-based HIC measurements in conditions of coexisting iron overload and steatosis. However, the R2 * estimation by these multispectral models has not been systematically investigated for various acquisition methods in iron overload only conditions and across the full clinically relevant range of HICs (0-40 mg Fe/g dry liver weight). The purpose of this study is to evaluate the R2 * accuracy and precision of multispectral models for various multiecho gradient echo (GRE) and ultrashort echo time (UTE) imaging acquisitions by constructing virtual iron overload models based on true histology and synthesizing MRI signals via Monte Carlo simulations at 1.5 T and 3 T, and comparing their results with monoexponential model and published in vivo R2 *-HIC calibrations. The signals were synthesized with TE1 = 1.0 ms for GRE and TE1 = 0.1 ms for UTE acquisition for varying echo spacing, ΔTE (0.1, 0.5, 1, 2 ms), and maximum echo time, TEmax (2, 4, 6, 10 ms). An iron-doped phantom study is also conducted to validate the simulation results in experimental GRE (TE1 = 1.2 ms, ΔTE = 0.72 ms, TEmax = 6.24 ms) and UTE (TE1 = 0.1 ms, ΔTE = 0.5 ms, TEmax = 6.1 ms) acquisitions. For GRE acquisitions, the multispectral ARMA and NLSQ models produced higher slopes (0.032-0.035) compared with the monoexponential model and published in vivo R2 *-HIC calibrations (0.025-0.028). However, for UTE acquisition for shorter echo spacing (≤0.5 ms) and longer maximum echo time, TEmax (≥6 ms), the multispectral and monoexponential signal models produced similar R2 *-HIC slopes (1.5 T, 0.028-0.032; 3 T, 0.014-0.016) and precision values (coefficient of variation < 25%) across the full clinical spectrum of HICs at both 1.5 T and 3 T. The phantom analysis also showed that all signal models demonstrated a significant improvement in R2 * estimation for UTE acquisition compared with GRE, confirming our simulation findings. Future work should investigate the performance of multispectral fat-water models by simulating liver models in coexisting conditions of iron overload and steatosis for accurate R2 * and fat quantification.
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Affiliation(s)
- Prasiddhi Neupane
- Biomedical Engineering, The University of Memphis, TN, United States
| | - Utsav Shrestha
- Biomedical Engineering, The University of Memphis, TN, United States
| | - Sarah Brasher
- Biomedical Engineering, The University of Memphis, TN, United States
| | - Zachary Abramson
- St. Jude Children’s Research Hospital, Memphis, TN, United States
| | - Aaryani Tipirneni-Sajja
- Biomedical Engineering, The University of Memphis, TN, United States
- St. Jude Children’s Research Hospital, Memphis, TN, United States
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Tipirneni-Sajja A, Brasher S, Shrestha U, Johnson H, Morin C, Satapathy SK. Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms. MAGMA (NEW YORK, N.Y.) 2023; 36:529-551. [PMID: 36515810 DOI: 10.1007/s10334-022-01053-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 12/15/2022]
Abstract
Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.
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Affiliation(s)
- 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.
| | - Sarah Brasher
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Utsav Shrestha
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Hayden Johnson
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Cara Morin
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sanjaya K Satapathy
- Northwell Health Center for Liver Diseases and Transplantation, Northshore University Hospital/Northwell Health, Manhasset, NY, USA
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Tipirneni-Sajja A, Loeffler RB, Hankins JS, Morin C, Hillenbrand CM. Quantitative Susceptibility Mapping Using a Multispectral Autoregressive Moving Average Model to Assess Hepatic Iron Overload. J Magn Reson Imaging 2021; 54:721-727. [PMID: 33634923 DOI: 10.1002/jmri.27584] [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: 11/25/2020] [Revised: 02/10/2021] [Accepted: 02/11/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND R2*-MRI is clinically used to noninvasively assess hepatic iron content (HIC) to guide potential iron chelation therapy. However, coexisting pathologies, such as fibrosis and steatosis, affect R2* measurements and may thus confound HIC estimations. PURPOSE To evaluate whether a multispectral auto regressive moving average (ARMA) model can be used in conjunction with quantitative susceptibility mapping (QSM) to measure magnetic susceptibility as a confounder-free predictor of HIC. STUDY TYPE Phantom study and in vivo cohort. SUBJECTS Nine iron phantoms covering clinically relevant R2* range (20-1200/second) and 48 patients (22 male, 26 female, median age 18 years). FIELD STRENGTH/SEQUENCE Three-dimensional (3D) and two-dimensional (2D) multi-echo gradient echo (GRE) at 1.5 T. ASSESSMENT ARMA-QSM modeling was performed on the complex 3D GRE signal to estimate R2*, fat fraction (FF), and susceptibility measurements. R2*-based dry clinical HIC values were calculated from the 2D GRE acquisition using a published R2*-HIC calibration curve as reference standard. STATISTICAL TESTS Linear regression analysis was performed to compare ARMA R2* and susceptibility-based estimates to iron concentrations and dry clinical HIC values in phantoms and patients, respectively. RESULTS In phantoms, the ARMA R2* and susceptibility values strongly correlated with iron concentrations (R2 ≥ 0.9). In patients, the ARMA R2* values highly correlated (R2 = 0.97) with clinical HIC values with slope = 0.026, and the susceptibility values showed good correlation (R2 = 0.82) with clinical dry HIC values with slope = 3.3 and produced a dry-to-wet HIC ratio of 4.8. DATA CONCLUSION This study shows the feasibility that ARMA-QSM can simultaneously estimate susceptibility-based wet HIC, R2*-based dry HIC and FFs from a single multi-echo GRE acquisition. Our results demonstrate that both, R2* and susceptibility-based wet HIC values estimated with ARMA-QSM showed good association with clinical dry HIC values with slopes similar to published R2*-biopsy HIC calibration and dry-to-wet tissue weight ratio, respectively. Hence, our study shows that ARMA-QSM can provide potentially confounder-free assessment of hepatic iron overload. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Aaryani Tipirneni-Sajja
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Department of Biomedical Engineering, The University of Memphis, Memphis, Tennessee, USA
| | - Ralf B Loeffler
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Research Imaging NSW, University of New South Wales, Sydney, New South Wales, Australia
| | - Jane S Hankins
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Cara Morin
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Claudia M Hillenbrand
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Research Imaging NSW, University of New South Wales, Sydney, New South Wales, Australia
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Tipirneni-Sajja A, Krafft AJ, Loeffler RB, Song R, Bahrami A, Hankins JS, Hillenbrand CM. Autoregressive moving average modeling for hepatic iron quantification in the presence of fat. J Magn Reson Imaging 2019; 50:1620-1632. [PMID: 30761652 DOI: 10.1002/jmri.26682] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 01/28/2019] [Accepted: 01/29/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Measuring hepatic R2* by fitting a monoexponential model to the signal decay of a multigradient-echo (mGRE) sequence noninvasively determines hepatic iron content (HIC). Concurrent hepatic steatosis introduces signal oscillations and confounds R2* quantification with standard monoexponential models. PURPOSE To evaluate an autoregressive moving average (ARMA) model for accurate quantification of HIC in the presence of fat using biopsy as the reference. STUDY TYPE Phantom study and in vivo cohort. POPULATION Twenty iron-fat phantoms covering clinically relevant R2* (30-800 s-1 ) and fat fraction (FF) ranges (0-40%), and 10 patients (four male, six female, mean age 18.8 years). FIELD STRENGTH/SEQUENCE 2D mGRE acquisitions at 1.5 T and 3 T. ASSESSMENT Phantoms were scanned at both field strengths. In vivo data were analyzed using the ARMA model to determine R2* and FF values, and compared with biopsy results. STATISTICAL TESTS Linear regression analysis was used to compare ARMA R2* and FF results with those obtained using a conventional monoexponential model, complex-domain nonlinear least squares (NLSQ) fat-water model, and biopsy. RESULTS In phantoms and in vivo, all models produced R2* and FF values consistent with expected values in low iron and low/high fat conditions. For high iron and no fat phantoms, monoexponential and ARMA models performed excellently (slopes: 0.89-1.07), but NLSQ overestimated R2* (slopes: 1.14-1.36) and produced false FFs (12-17%) at 1.5 T; in high iron and fat phantoms, NLSQ (slopes: 1.02-1.16) outperformed monoexponential and ARMA models (slopes: 1.23-1.88). The results with NLSQ and ARMA improved in phantoms at 3 T (slopes: 0.96-1.04). In patients, mean R2*-HIC estimates for monoexponential and ARMA models were close to biopsy-HIC values (slopes: 0.90-0.95), whereas NLSQ substantially overestimated HIC (slope 1.4) and produced false FF values (4-28%) with very high SDs (15-222%) in patients with high iron overload and no steatosis. DATA CONCLUSION ARMA is superior in quantifying R2* and FF under high iron and no fat conditions, whereas NLSQ is superior for high iron and concurrent fat at 1.5 T. Both models give improved R2* and FF results at 3 T. LEVEL OF EVIDENCE 2 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1620-1632.
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Affiliation(s)
- Aaryani Tipirneni-Sajja
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Department of Biomedical Engineering, University of Memphis, Memphis, Tennessee, USA
| | - Axel J Krafft
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Department of Radiology, Medical Physics, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ralf B Loeffler
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ruitian Song
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Armita Bahrami
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jane S Hankins
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Claudia M Hillenbrand
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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Simchick G, Liu Z, Nagy T, Xiong M, Zhao Q. Assessment of MR-based R2* and quantitative susceptibility mapping for the quantification of liver iron concentration in a mouse model at 7T. Magn Reson Med 2018; 80:2081-2093. [PMID: 29575047 PMCID: PMC6107404 DOI: 10.1002/mrm.27173] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Revised: 02/15/2018] [Accepted: 02/15/2018] [Indexed: 01/19/2023]
Abstract
PURPOSE To assess the feasibility of quantifying liver iron concentration (LIC) using R2* and quantitative susceptibility mapping (QSM) at a high field strength of 7 Tesla (T). METHODS Five different concentrations of Fe-dextran were injected into 12 mice to produce various degrees of liver iron overload. After mice were sacrificed, blood and liver samples were harvested. Ferritin enzyme-linked immunosorbent assay (ELISA) and inductively coupled plasma mass spectrometry were performed to quantify serum ferritin concentration and LIC. Multiecho gradient echo MRI was conducted to estimate R2* and the magnetic susceptibility of each liver sample through complex nonlinear least squares fitting and a morphology enabled dipole inversion method, respectively. RESULTS Average estimates of serum ferritin concentration, LIC, R2*, and susceptibility all show good linear correlations with injected Fe-dextran concentration; however, the standard deviations in the estimates of R2* and susceptibility increase with injected Fe-dextran concentration. Both R2* and susceptibility measurements also show good linear correlations with LIC (R2 = 0.78 and R2 = 0.91, respectively), and a susceptibility-to-LIC conversion factor of 0.829 ppm/(mg/g wet) is derived. CONCLUSION The feasibility of quantifying LIC using MR-based R2* and QSM at a high field strength of 7T is demonstrated. Susceptibility quantification, which is an intrinsic property of tissues and benefits from being field-strength independent, is more robust than R2* quantification in this ex vivo study. A susceptibility-to-LIC conversion factor is presented that agrees relatively well with previously published QSM derived results obtained at 1.5T and 3T.
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Affiliation(s)
- Gregory Simchick
- Physics and Astronomy, University of Georgia, Athens, GA, United States
- Bio-Imaging Research Center, University of Georgia, Athens, GA, United States
| | - Zhi Liu
- Pharmaceutical & Biomedical Sciences, University of Georgia, Athens, GA United States
| | - Tamas Nagy
- Pathology, College of Veterinary Medicine, University of Georgia, Athens, GA United States
| | - May Xiong
- Pharmaceutical & Biomedical Sciences, University of Georgia, Athens, GA United States
| | - Qun Zhao
- Physics and Astronomy, University of Georgia, Athens, GA, United States
- Bio-Imaging Research Center, University of Georgia, Athens, GA, United States
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Simchick G, Yin A, Yin H, Zhao Q. Fat spectral modeling on triglyceride composition quantification using chemical shift encoded magnetic resonance imaging. Magn Reson Imaging 2018; 52:84-93. [PMID: 29928937 DOI: 10.1016/j.mri.2018.06.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 06/14/2018] [Accepted: 06/17/2018] [Indexed: 12/12/2022]
Abstract
PURPOSE To explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI). METHODS MR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models. RESULTS Using a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models. CONCLUSION At a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).
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Affiliation(s)
- Gregory Simchick
- Physics and Astronomy, University of Georgia, Athens, GA, United States; Bio-Imaging Research Center, University of Georgia, Athens, GA, United States
| | - Amelia Yin
- Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States; Center for Molecular Medicine, University of Georgia, Athens, GA, United States
| | - Hang Yin
- Biochemistry and Molecular Biology, University of Georgia, Athens, GA, United States; Center for Molecular Medicine, University of Georgia, Athens, GA, United States
| | - Qun Zhao
- Physics and Astronomy, University of Georgia, Athens, GA, United States; Bio-Imaging Research Center, University of Georgia, Athens, GA, United States.
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Yan F, He N, Lin H, Li R. Iron deposition quantification: Applications in the brain and liver. J Magn Reson Imaging 2018; 48:301-317. [PMID: 29897645 DOI: 10.1002/jmri.26161] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 04/02/2018] [Indexed: 01/01/2023] Open
Abstract
Iron has long been implicated in many neurological and other organ diseases. It is known that over and above the normal increases in iron with age, in certain diseases there is an excessive iron accumulation in the brain and liver. MRI is a noninvasive means by which to image the various structures in the brain in three dimensions and quantify iron over the volume of the object of interest. The quantification of iron can provide information about the severity of iron-related diseases as well as quantify changes in iron for patient follow-up and treatment monitoring. This article provides an overview of current MRI-based methods for iron quantification, specifically for the brain and liver, including: signal intensity ratio, R2 , R2*, R2', phase, susceptibility weighted imaging and quantitative susceptibility mapping (QSM). Although there are numerous approaches to measuring iron, R2 and R2* are currently preferred methods in imaging the liver and QSM has become the preferred approach for imaging iron in the brain. LEVEL OF EVIDENCE 5 Technical Efficacy: Stage 5 J. Magn. Reson. Imaging 2018. J. MAGN. RESON. IMAGING 2018;48:301-317.
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Affiliation(s)
- Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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9
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Quantifying iron content in magnetic resonance imaging. Neuroimage 2018; 187:77-92. [PMID: 29702183 DOI: 10.1016/j.neuroimage.2018.04.047] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2017] [Revised: 04/13/2018] [Accepted: 04/20/2018] [Indexed: 01/19/2023] Open
Abstract
Measuring iron content has practical clinical indications in the study of diseases such as Parkinson's disease, Huntington's disease, ferritinopathies and multiple sclerosis as well as in the quantification of iron content in microbleeds and oxygen saturation in veins. In this work, we review the basic concepts behind imaging iron using T2, T2*, T2', phase and quantitative susceptibility mapping in the human brain, liver and heart, followed by the applications of in vivo iron quantification in neurodegenerative diseases, iron tagged cells and ultra-small superparamagnetic iron oxide (USPIO) nanoparticles.
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Liu S, Wang C, Zhang X, Zuo P, Hu J, Haacke EM, Ni H. Quantification of liver iron concentration using the apparent susceptibility of hepatic vessels. Quant Imaging Med Surg 2018; 8:123-134. [PMID: 29675354 DOI: 10.21037/qims.2018.03.02] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Background The quantification of liver iron concentration (LIC) is important for the monitoring of the body iron level in patients with iron overload. Conventionally, LIC is quantified through R2 or R2* mapping using MRI. In this paper, we demonstrate an alternative approach for LIC quantification through measuring the apparent susceptibility of hepatic vessels using quantitative susceptibility mapping (QSM). Methods QSM was performed in the liver region with the iterative susceptibility weighted imaging and mapping (iSWIM) algorithm, using the geometry of the vessels extracted from magnitude images as constraints. The susceptibilities of liver tissue were estimated from the apparent susceptibility of the hepatic veins and then converted to LIC. The accuracy of the proposed method was first validated using simulations, and then confirmed using in vivo data collected on 8 healthy controls and 11 patients at 3T. The effects of data acquisition parameters were studied using simulations, and the LICs estimated using QSM were compared with those estimated using R2* mapping. Results Simulation results showed that the use of a 3D data acquisition protocol with higher image resolution led to improved accuracy in LIC quantification using QSM. Both simulations and in vivo data results demonstrated that the LICs estimated using the proposed QSM method agreed well with those estimated using R2* mapping. With the shortest echo time being 2.5ms in the multi-echo gradient echo sequence, simulations results showed that LIC up to 12.45 mg iron/g dry tissue can be quantified using the proposed QSM method. For the in vivo data, the highest LIC measured was 11.32 mg iron/g dry tissue. Conclusions The proposed method offers a reliable and flexible way to quantify LIC and has the potential to extend the range of LIC that can be accurately measured using R2* and QSM.
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Affiliation(s)
- Saifeng Liu
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA
| | - Chaoyue Wang
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
| | - Xiaoqi Zhang
- Department of Radiology, Tianjin First Central Hospital, Tianjin 300192, China
| | - Panli Zuo
- Siemens Healthcare, MR Collaborations NE Asia, Beijing 100010, China
| | - Jiani Hu
- Department of Radiology, Wayne State University, Detroit, MI, USA
| | - E Mark Haacke
- The MRI Institute for Biomedical Research, Bingham Farms, MI, USA.,School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.,Department of Radiology, Wayne State University, Detroit, MI, USA
| | - Hongyan Ni
- Department of Radiology, Tianjin First Central Hospital, Tianjin 300192, China
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Lin H, Wei H, He N, Fu C, Cheng S, Shen J, Wang B, Yan X, Liu C, Yan F. Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification. Eur Radiol 2018; 28:3494-3504. [PMID: 29470640 DOI: 10.1007/s00330-017-5263-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 12/08/2017] [Accepted: 12/20/2017] [Indexed: 12/21/2022]
Abstract
PURPOSES To evaluate the feasibility of simultaneous quantification of liver iron concentration (LIC) and fat fraction (FF) using water-fat separation and quantitative susceptibility mapping (QSM). METHODS Forty-five patients suspected of liver iron overload (LIO) were included. A volumetric interpolated breath-hold examination sequence for QSM and FF, a fat-saturated gradient echo sequence for R2*, a spin echo sequence for LIC measurements and MRS analyses for FF (FF-MRS) were performed. Magnetic susceptibility and FF were calculated using a water-fat separation method (FF-MRI). Correlation and receiver operating characteristic analyses were performed. RESULTS Magnetic susceptibility showed strong correlation with LIC (rs=0.918). The optimal susceptibility cut-off values were 0.34, 0.63, 1.29 and 2.23 ppm corresponding to LIC thresholds of 1.8, 3.2, 7.0 and 15.0 mg/g dry weight. The area under the curve (AUC) were 0.948, 0.970, 1 and 1, respectively. No difference in AUC was found between susceptibility and R2* at all LIC thresholds. Correlation was found between FF-MRI and FF-MRS (R2=0.910). CONCLUSIONS QSM has a high diagnostic performance for LIC quantification, similar to that of R2*. FF-MRI provides simultaneous fat quantification. Findings suggest QSM in combination with water-fat separation has potential value for evaluating LIO, especially in cases with coexisting steatosis. KEY POINTS • Magnetic susceptibility showed strong correlation with LIC (r s =0.918). • QSM showed high diagnostic performance for LIC, similar to that of R 2* . • Simultaneously estimated FF-MRI showed strong correlation with MR-Spectroscopy-based FF (R 2 =0.910). • QSM combining water-fat separation has quantitative value for LIO with coexisted steatosis.
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Affiliation(s)
- Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Caixia Fu
- Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Shu Cheng
- Department of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Shen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai, 200025, China
| | - Baisong Wang
- Department of Biological Statistics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xu Yan
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Chunlei Liu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA.,Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai, 200025, China.
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12
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Zimmermann M, Abbas Z, Dzieciol K, Shah NJ. Accelerated Parameter Mapping of Multiple-Echo Gradient-Echo Data Using Model-Based Iterative Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:626-637. [PMID: 29408790 DOI: 10.1109/tmi.2017.2771504] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
A new reconstruction method, coined MIRAGE, is presented for accurate, fast, and robust parameter mapping of multiple-echo gradient-echo (MEGE) imaging, the basis sequence of novel quantitative magnetic resonance imaging techniques such as water content and susceptibility mapping. Assuming that the temporal signal can be modeled as a sum of damped complex exponentials, MIRAGE performs model-based reconstruction of undersampled data by minimizing the rank of local Hankel matrices. It further incorporates multi-channel information and spatial prior knowledge. Finally, the parameter maps are estimated using nonlinear regression. Simulations and retrospective undersampling of phantom and in vivo data affirm robustness, e.g., to strong inhomogeneity of the static magnetic field and partial volume effects. MIRAGE is compared with a state-of-the-art compressed sensing method, -ESPIRiT. Parameter maps estimated from reconstructed data using MIRAGE are shown to be accurate, with the mean absolute error reduced by up to 50% for in vivo results. The proposed method has the potential to improve the diagnostic utility of quantitative imaging techniques that rely on MEGE data.
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13
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Song R, Loeffler RB, Holtrop JL, McCarville MB, Hankins JS, Hillenbrand CM. Fast quantitative parameter maps without fitting: Integration yields accurate mono-exponential signal decay rates. Magn Reson Med 2017; 79:2978-2985. [PMID: 29086437 DOI: 10.1002/mrm.26964] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 08/30/2017] [Accepted: 09/18/2017] [Indexed: 01/18/2023]
Abstract
PURPOSE To develop a computationally fast and accurate algorithm for mono-exponential signal modelling and validate the new technique in the context of R2* mapping for iron overload assessment. METHODS An algorithm is introduced that directly calculates R2* values from a series of images based on integration of the mono-exponential signal decay curve. The algorithm is fast, because fitting is avoided and only arithmetic computations without iterations are applied. Precision and accuracy of the method is determined in comparison to the conventional log-linear (LL), nonlinear least-squares-based Levenberg-Marquardt (NLM), and squared nonlinear Levenberg-Marquardt (SQNLM) methods, which rely on iterative curve fitting. RESULTS In simulations, the signal integration based method consistently had the same or better accuracy than the LL, NLM, and SQNLM algorithms for R2* values ranging from 50 s-1 to 1200 s-1 . In phantoms and in vivo (12 participants), this method was robust over a wide range of R2* values and signal-to-noise ratios. Computation times were approximately 100, 1460, and 930 times faster than those of the LL, NLM, and SQNLM methods, respectively. CONCLUSIONS The fast signal integration method accurately calculates R2* maps. It has the potential to replace conventional, mono-exponential fitting methods for quantitative MRI such as R2* parameter mapping. Magn Reson Med 79:2978-2985, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Ruitian Song
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ralf B Loeffler
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Joseph L Holtrop
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - M Beth McCarville
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jane S Hankins
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Claudia M Hillenbrand
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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14
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Loeffler RB, McCarville MB, Wagstaff AW, Smeltzer MP, Krafft AJ, Song R, Hankins JS, Hillenbrand CM. Can multi-slice or navigator-gated R2* MRI replace single-slice breath-hold acquisition for hepatic iron quantification? Pediatr Radiol 2017; 47:46-54. [PMID: 27752732 PMCID: PMC5203961 DOI: 10.1007/s00247-016-3700-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2016] [Revised: 07/21/2016] [Accepted: 08/26/2016] [Indexed: 11/25/2022]
Abstract
BACKGROUND Liver R2* values calculated from multi-gradient echo (mGRE) magnetic resonance images (MRI) are strongly correlated with hepatic iron concentration (HIC) as shown in several independently derived biopsy calibration studies. These calibrations were established for axial single-slice breath-hold imaging at the location of the portal vein. Scanning in multi-slice mode makes the exam more efficient, since whole-liver coverage can be achieved with two breath-holds and the optimal slice can be selected afterward. Navigator echoes remove the need for breath-holds and allow use in sedated patients. OBJECTIVE To evaluate if the existing biopsy calibrations can be applied to multi-slice and navigator-controlled mGRE imaging in children with hepatic iron overload, by testing if there is a bias-free correlation between single-slice R2* and multi-slice or multi-slice navigator controlled R2*. MATERIALS AND METHODS This study included MRI data from 71 patients with transfusional iron overload, who received an MRI exam to estimate HIC using gradient echo sequences. Patient scans contained 2 or 3 of the following imaging methods used for analysis: single-slice images (n = 71), multi-slice images (n = 69) and navigator-controlled images (n = 17). Small and large blood corrected region of interests were selected on axial images of the liver to obtain R2* values for all data sets. Bland-Altman and linear regression analysis were used to compare R2* values from single-slice images to those of multi-slice images and navigator-controlled images. RESULTS Bland-Altman analysis showed that all imaging method comparisons were strongly associated with each other and had high correlation coefficients (0.98 ≤ r ≤ 1.00) with P-values ≤0.0001. Linear regression yielded slopes that were close to 1. CONCLUSION We found that navigator-gated or breath-held multi-slice R2* MRI for HIC determination measures R2* values comparable to the biopsy-validated single-slice, single breath-hold scan. We conclude that these three R2* methods can be interchangeably used in existing R2*-HIC calibrations.
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Affiliation(s)
- Ralf B Loeffler
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA
| | - M Beth McCarville
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA
| | - Anne W Wagstaff
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA
- Rhodes College, Memphis, TN, USA
- University of Alabama at Birmingham School of Medicine, Birmingham, AL, USA
| | - Matthew P Smeltzer
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN, USA
- Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, USA
| | - Axel J Krafft
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA
- Department of Radiology, University Hospital Center Freiburg, Freiburg, Germany
| | - Ruitian Song
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA
| | - Jane S Hankins
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Claudia M Hillenbrand
- Diagnostic Imaging, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, 38105-3678, USA.
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15
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Umesh Rudrapatna S, Bakker CJG, Viergever MA, van der Toorn A, Dijkhuizen RM. Improved estimation of MR relaxation parameters using complex-valued data. Magn Reson Med 2016; 77:385-397. [PMID: 26762754 DOI: 10.1002/mrm.26088] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 11/02/2015] [Accepted: 11/23/2015] [Indexed: 12/12/2022]
Abstract
PURPOSE In MR image analysis, T1 , T2 , and T2* maps are generally calculated using magnitude MR data. Without knowledge of the underlying noise variance, parameter estimates at low signal to noise ratio (SNR) are usually biased. This leads to confounds in studies that compare parameters across SNRs and or across scanners. This article compares several estimation techniques which use real or complex-valued MR data to achieve unbiased estimation of MR relaxation parameters without the need for additional preprocessing. THEORY AND METHODS Several existing and new techniques to estimate relaxation parameters using complex-valued data were compared with widely used magnitude-based techniques. Their bias, variance and processing times were studied using simulations covering various aspects of parameter variations. Validation on noise-degraded experimental measurements was also performed. RESULTS Simulations and experiments demonstrated the superior performance of techniques based on complex-valued data, even in comparison with magnitude-based techniques that account for Rician noise characteristics. This was achieved with minor modifications to data modeling and at computational costs either comparable to or higher ( ≈two fold) than magnitude-based estimators. Theoretical analysis shows that estimators based on complex-valued data are statistically efficient. CONCLUSION The estimation techniques that use complex-valued data provide minimum variance unbiased estimates of parametric maps and markedly outperform commonly used magnitude-based estimators under most conditions. They additionally provide phase maps and field maps, which are unavailable with magnitude-based methods. Magn Reson Med 77:385-397, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- S Umesh Rudrapatna
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C J G Bakker
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.,Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M A Viergever
- Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - A van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
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16
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Krafft AJ, Loeffler RB, Song R, Bian X, McCarville MB, Hankins JS, Hillenbrand CM. Does fat suppression via chemically selective saturation affect R2*-MRI for transfusional iron overload assessment? A clinical evaluation at 1.5T and 3T. Magn Reson Med 2015; 76:591-601. [PMID: 26308155 DOI: 10.1002/mrm.25868] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 07/10/2015] [Accepted: 07/13/2015] [Indexed: 01/01/2023]
Abstract
PURPOSE Fat suppression (FS) via chemically selective saturation (CHESS) eliminates fat-water oscillations in multiecho gradient echo (mGRE) R2*-MRI. However, for increasing R2* values as seen with increasing liver iron content (LIC), the water signal spectrally overlaps with the CHESS band, which may alter R2*. We investigated the effect of CHESS on R2* and developed a heuristic correction for the observed CHESS-induced R2* changes. METHODS Eighty patients [female, n = 49; male, n = 31; mean age (± standard deviation), 18.3 ± 11.7 y] with iron overload were scanned with a non-FS and a CHESS-FS mGRE sequence at 1.5T and 3T. Mean liver R2* values were evaluated using three published fitting approaches. Measured and model-corrected R2* values were compared and statistically analyzed. RESULTS At 1.5T, CHESS led to a systematic R2* reduction (P < 0.001 for all fitting algorithms) especially toward higher R2*. Our model described the observed changes well and reduced the CHESS-induced R2* bias after correction (linear regression slopes: 1.032/0.927/0.981). No CHESS-induced R2* reductions were found at 3T. CONCLUSION The CHESS-induced R2* bias at 1.5T needs to be considered when applying R2*-LIC biopsy calibrations for clinical LIC assessment, which were established without FS at 1.5T. The proposed model corrects the R2* bias and could therefore improve clinical iron overload assessment based on linear R2*-LIC calibrations. Magn Reson Med 76:591-601, 2016. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Axel J Krafft
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ralf B Loeffler
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Ruitian Song
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Xiao Bian
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.,Rhodes College, Memphis, Tennessee, USA
| | - M Beth McCarville
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Jane S Hankins
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Claudia M Hillenbrand
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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17
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Yokoo T, Yuan Q, Sénégas J, Wiethoff AJ, Pedrosa I. Quantitative R2* MRI of the liver with rician noise models for evaluation of hepatic iron overload: Simulation, phantom, and early clinical experience. J Magn Reson Imaging 2015; 42:1544-59. [PMID: 25996989 DOI: 10.1002/jmri.24948] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 04/28/2015] [Indexed: 12/11/2022] Open
Abstract
PURPOSE To compare Rician and non-Rician noise models for quantitative R2 * magnetic resonance imaging (MRI), in a simulation, phantom, and human study. MATERIALS AND METHODS Synthetic 12-echo spoiled GRE (SGRE) datasets were generated with various R2 * rates (0-2000 sec(-1) ) at a signal-to-noise ratio (SNR) of 50, 20, 10, and 5. Phantoms of different MnCl2 concentrations (0-25 mM) were constructed and imaged using a 12-echo 3D SGRE sequence at 1.5T. Increasing levels of synthetic noise was added to the original data to simulate sequentially lower SNR conditions. Sixteen patients with suspected or known iron overload were imaged using 12-echo 3D SGRE at 1.5T. Various R2 * quantification methods, based on Rician and non-Rician noise models, were compared in the simulation, phantom, and human datasets. RESULTS Non-Rician R2 * estimates were variably inaccurate in the high R2 * range (>500 sec(-1) ), with SNR-dependent linear goodness-of-fit statistic (R(2) ) of 0.373-0.999. Rician R2 * estimates were accurate even in the high R2 * range, with high R(2) of 0.940-0.999 regardless of SNR. Non-Rician R2 * estimates were variably nonlinear at high MnCl2 concentrations, with SNR-dependent R(2) of 0.345-0.994. Rician R2 * estimates were linear even at high MnCl2 concentrations, with high R(2) of 0.923-0.994 regardless of SNR. Between-method agreement of the R2 * estimates was excellent in patients with low ferritin but poor in patients with high ferritin. Rician R2 * estimates had excellent correlation with ferritin (r = 0.966 P < 0.001). CONCLUSION Rician R2 * estimates were most consistent in the high R2 * conditions and under varying SNR, and may be more reliable when high iron load is suspected.
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Affiliation(s)
- Takeshi Yokoo
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Qing Yuan
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Andrea J Wiethoff
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Briarcliff Manor, New York, USA
| | - Ivan Pedrosa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas, USA.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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18
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Abstract
Liver fat, iron, and combined overload are common manifestations of diffuse liver disease and may cause lipotoxicity and iron toxicity via oxidative hepatocellular injury, leading to progressive fibrosis, cirrhosis, and eventually, liver failure. Intracellular fat and iron cause characteristic changes in the tissue magnetic properties in predictable dose-dependent manners. Using dedicated magnetic resonance pulse sequences and postprocessing algorithms, fat and iron can be objectively quantified on a continuous scale. In this article, we will describe the basic physical principles of magnetic resonance fat and iron quantification and review the imaging techniques of the "past, present, and future." Standardized radiological metrics of fat and iron are introduced for numerical reporting of overload severity, which can be used toward objective diagnosis, grading, and longitudinal disease monitoring. These noninvasive imaging techniques serve an alternative or complimentary role to invasive liver biopsy. Commercial solutions are increasingly available, and liver fat and iron quantitative imaging is now within reach for routine clinical use and may soon become standard of care.
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Affiliation(s)
- Takeshi Yokoo
- From the *Department of Radiology, †Advanced Imaging Research Center, and ‡Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX
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19
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Pei M, Nguyen TD, Thimmappa ND, Salustri C, Dong F, Cooper MA, Li J, Prince MR, Wang Y. Algorithm for fast monoexponential fitting based on Auto-Regression on Linear Operations (ARLO) of data. Magn Reson Med 2014; 73:843-50. [PMID: 24664497 DOI: 10.1002/mrm.25137] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2013] [Revised: 12/30/2013] [Accepted: 12/30/2013] [Indexed: 11/08/2022]
Abstract
PURPOSE To develop a fast and accurate monoexponential fitting algorithm based on Auto-Regression on Linear Operations (ARLO) of data, and to validate its accuracy and computational speed by comparing it with the conventional Levenberg-Marquardt (LM) and Log-Linear (LL) algorithms. METHODS ARLO, LM, and LL performances for T2* mapping were evaluated in simulation and in vivo imaging of liver (n=15) and myocardial (n=1) iron overload patients and the brain (two healthy volunteers). RESULTS In simulations, ARLO consistently delivered accuracy similar to LM and significantly superior to LL. In in vivo mapping of T2 * values, ARLO showed excellent agreement with LM, while LL showed only limited agreements with ARLO and LM. Compared with LM and LL in the liver, ARLO was 125 and 8 times faster using our Matlab implementations, and 156 and 13 times faster using our C++ implementations. In C++ implementations, ARLO reduced the online whole-brain processing time from 9 min 15 s of LM and 35 s of LL to 2.7 s, providing T2 * maps approximately in real time. CONCLUSION Due to comparable accuracy and significantly higher speed, ARLO can be considered as a valid alternative to the conventional LM algorithm for online T2 * mapping.
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Affiliation(s)
- Mengchao Pei
- Shanghai Key Laboratory of Magnetic Resonance and Department of Physics, East China Normal University, Shanghai, China; Yifu Inc, Jiaxing, Zhejiang Province, China
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Hernando D, Levin YS, Sirlin CB, Reeder SB. Quantification of liver iron with MRI: state of the art and remaining challenges. J Magn Reson Imaging 2014; 40:1003-21. [PMID: 24585403 DOI: 10.1002/jmri.24584] [Citation(s) in RCA: 188] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Accepted: 01/14/2014] [Indexed: 12/11/2022] Open
Abstract
Liver iron overload is the histological hallmark of hereditary hemochromatosis and transfusional hemosiderosis, and can also occur in chronic hepatopathies. Iron overload can result in liver damage, with the eventual development of cirrhosis, liver failure, and hepatocellular carcinoma. Assessment of liver iron levels is necessary for detection and quantitative staging of iron overload and monitoring of iron-reducing treatments. This article discusses the need for noninvasive assessment of liver iron and reviews qualitative and quantitative methods with a particular emphasis on magnetic resonance imaging (MRI). Specific MRI methods for liver iron quantification include signal intensity ratio as well as R2 and R2* relaxometry techniques. Methods that are in clinical use, as well as their limitations, are described. Remaining challenges, unsolved problems, and emerging techniques to provide improved characterization of liver iron deposition are discussed.
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Affiliation(s)
- Diego Hernando
- Department of Radiology, University of Wisconsin - Madison, Madison, Wisconsin, USA
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21
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Harry H, Kan HE. Quantitative proton MR techniques for measuring fat. NMR IN BIOMEDICINE 2013; 26:1609-29. [PMID: 24123229 PMCID: PMC4001818 DOI: 10.1002/nbm.3025] [Citation(s) in RCA: 91] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2013] [Revised: 07/13/2013] [Accepted: 08/19/2013] [Indexed: 05/09/2023]
Abstract
Accurate, precise and reliable techniques for the quantification of body and organ fat distributions are important tools in physiology research. They are critically needed in studies of obesity and diseases involving excess fat accumulation. Proton MR methods address this need by providing an array of relaxometry-based (T1, T2) and chemical shift-based approaches. These techniques can generate informative visualizations of regional and whole-body fat distributions, yield measurements of fat volumes within specific body depots and quantify fat accumulation in abdominal organs and muscles. MR methods are commonly used to investigate the role of fat in nutrition and metabolism, to measure the efficacy of short- and long-term dietary and exercise interventions, to study the implications of fat in organ steatosis and muscular dystrophies and to elucidate pathophysiological mechanisms in the context of obesity and its comorbidities. The purpose of this review is to provide a summary of mainstream MR strategies for fat quantification. The article succinctly describes the principles that differentiate water and fat proton signals, summarizes the advantages and limitations of various techniques and offers a few illustrative examples. The article also highlights recent efforts in the MR of brown adipose tissue and concludes by briefly discussing some future research directions.
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Affiliation(s)
- Houchun Harry
- Corresponding Author Houchun Harry Hu, PhD Children's Hospital Los Angeles University of Southern California 4650 Sunset Boulevard Department of Radiology, MS #81 Los Angeles, California, USA. 90027 , Office: +1 (323) 361-2688 Fax: +1 (323) 361-1510
| | - Hermien E. Kan
- C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
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Hernando D, Cook RJ, Diamond C, Reeder SB. Magnetic susceptibility as a B0 field strength independent MRI biomarker of liver iron overload. Magn Reson Med 2013; 70:648-56. [PMID: 23801540 PMCID: PMC3883906 DOI: 10.1002/mrm.24848] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2012] [Revised: 05/20/2013] [Accepted: 05/28/2013] [Indexed: 01/19/2023]
Abstract
PURPOSE MR-based quantification of liver magnetic susceptibility may enable field strength-independent measurement of liver iron concentration (LIC). However, susceptibility quantification is challenging, due to nonlocal effects of susceptibility on the B0 field. The purpose of this work is to demonstrate feasibility of susceptibility-based LIC quantification using a fat-referenced approach. METHODS Phantoms consisting of vials with increasing iron concentrations immersed between oil/water layers, and 27 subjects (9 controls/18 subjects with liver iron overload) were scanned. Ferriscan (1.5 T) provided R2-based reference LIC. Multiecho three-dimensional-SPGR (1.5 T/3 T) enabled fat-water, B0- and R2*-mapping. Phantom iron concentration (mg Fe L(-1)) was estimated from B0 differences (ΔB0) between vials and neighboring oil. Liver susceptibility and LIC (mg Fe g(-1) dry tissue) was estimated from ΔB0 between the lateral right lobe of the liver and adjacent subcutaneous adipose tissue. RESULTS Estimated phantom iron concentrations had good correlation with true iron concentrations (1.5 T:slope = 0.86, intercept = 0.72, r(2) = 0.98; 3 T:slope = 0.85, intercept = 1.73, r(2) = 0.98). In liver, ΔB0 correlated strongly with R2* (1.5 T:r(2) = 0.86; 3 T:r(2) = 0.93) and B0-LIC had good agreement with Ferriscan-LIC (slopes/intercepts nearly 1.0/0.0, 1.5 T:r(2) = 0.67, slope = 0.93 ± 0.13, P ≈ 0.50, intercept = 1.93 ± 0.78, P ≈ 0.02; 3 T:r(2) = 0.84, slope = 1.01 ± 0.09, P ≈ 0.90, intercept = 0.23 ± 0.52, P ≈ 0.68). DISCUSSION Fat-referenced, susceptibility-based LIC estimation is feasible at both field strengths. This approach may enable improved susceptibility mapping in the abdomen.
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Affiliation(s)
- Diego Hernando
- Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA
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Taylor BA, Loeffler RB, Song R, McCarville ME, Hankins JS, Hillenbrand CM. Automated T(2) * measurements using supplementary field mapping to assess cardiac iron content. J Magn Reson Imaging 2013; 38:441-7. [PMID: 23292658 DOI: 10.1002/jmri.23990] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2012] [Accepted: 11/14/2012] [Indexed: 01/14/2023] Open
Abstract
PURPOSE To develop and evaluate an algorithm that automatically identifies high-susceptibility areas and excludes them from T(2) * measurements in the left ventricle (LV) for myocardial iron measurements. MATERIALS AND METHODS An autoregressive moving average (ARMA) model was implemented on multigradient echo scans of 24 patients (age range 3-45 years, 10 male/14 female). Voxels with relatively high susceptibility (>3 Hz/mm) were flagged and deselected from the T(2) * calculations for iron quantification. The mean, standard deviation, and coefficient of variation (CoV) of the ARMA-defined region were compared to the CoV of four distinct regions of the LV and the entire LV using a Student's t-test (α = 0.05). RESULTS The CoV of T(2) * values obtained by the ARMA method are comparable with that in the interventricular septum (IS), where susceptibility was the lowest (CoV = 0.31). The ARMA method provides a greater area (51.9 ± 13.7% of the LV) to measure T(2) * than that using the IS alone (21.1 ± 3.4%, P < 0.0001). Areas where low susceptibility are measured corroborate with areas reported in previous studies that investigated T(2) * variations throughout the LV. CONCLUSION An automated method to measure T(2) * relaxation in the LV with minimal effects from susceptibility has been developed. Variability is reduced while covering more regions for cardiac T2 * calculation.
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Affiliation(s)
- Brian A Taylor
- Department of Radiological Sciences, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
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