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Shrestha U, Esparza JP, Satapathy SK, Vanatta JM, Abramson ZR, Tipirneni-Sajja A. Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T. Comput Biol Med 2024; 174:108448. [PMID: 38626508 DOI: 10.1016/j.compbiomed.2024.108448] [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: 12/07/2023] [Revised: 03/06/2024] [Accepted: 04/07/2024] [Indexed: 04/18/2024]
Abstract
BACKGROUND AND OBJECTIVE Magnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. METHODS Realistic hepatic steatosis models encompassing clinical FF range (0-60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5-100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. RESULTS The virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95-1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. CONCLUSION Our study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.
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Affiliation(s)
- Utsav Shrestha
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA; Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Juan P Esparza
- Department of Biomedical Engineering, The University of Memphis, Memphis, TN, USA
| | - Sanjaya K Satapathy
- Department of Medicine, Division of Hepatology, Donald and Barbara Zucker School of Medicine at Hofstra, Hempstead, NY, USA; Northwell Health Center for Liver Diseases & Transplantation, North Shore University Hospital, Manhasset, NY, USA
| | - Jason M Vanatta
- Department of Surgery, University of Tennessee Health and Science Center, Memphis, TN, USA
| | - Zachary R Abramson
- Department of Diagnostic Imaging, 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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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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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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Accuracy versus reliability-based modelling approaches for medical decision making. Comput Biol Med 2021; 141:105138. [PMID: 34929467 DOI: 10.1016/j.compbiomed.2021.105138] [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: 11/02/2021] [Revised: 12/11/2021] [Accepted: 12/11/2021] [Indexed: 11/21/2022]
Abstract
Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have been developed to maximize accuracy in mind. In other words, given the volatility of medical variables, it is also necessary to have stable and reliable forecasts in order to make sound decisions. The quality of medical decisions resulting from accuracy and reliability-based intelligent and statistical modeling approaches is compared and evaluated in this paper in order to determine the relative importance of accuracy and reliability on the quality of made decisions in decision support systems. For this purpose, 33 different case studies from the UCI in three categories of supervised modeling, namely causal forecasting, time series prediction, and classification, were considered. These cases were chosen from various domains, such as disease diagnosis (obesity, Parkinson's disease, diabetes, hepatitis, stenosis of arteries, orthopedic disease, autism) and cancer (lung, breast, cervical), experiments, therapy (immunotherapy, cryotherapy), fertility prediction, and predicting the number of patients in the emergency room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.
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Shrestha U, van der Merwe M, Kumar N, Jacobs E, Satapathy SK, Morin C, Tipirneni-Sajja A. Morphological characterization of hepatic steatosis and Monte Carlo modeling of MRI signal for accurate quantification of fat fraction and relaxivity. NMR IN BIOMEDICINE 2021; 34:e4489. [PMID: 33586261 DOI: 10.1002/nbm.4489] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 12/16/2020] [Accepted: 01/25/2021] [Indexed: 06/12/2023]
Abstract
Chemical-shift-based fat-water MRI signal models with single- or dual-R2 * correction have been proposed for quantification of fat fraction (FF) and assessment of hepatic steatosis. However, there is a void in our understanding of which model truly mimics the underlying biophysical mechanism of steatosis on MRI signal relaxation. The purpose of this study is to morphologically characterize and build realistic steatosis models from histology and synthesize MRI signal using Monte Carlo simulations to investigate the accuracy of single- and dual-R2 * models in quantifying FF and R2 *. Fat morphology was characterized by performing automatic segmentation on 16 mouse liver histology images and extracting the radius, nearest neighbor (NN) distance, and regional anisotropy of fat droplets. A gamma distribution function (GDF) was used to generalize extracted features, and regression analysis was performed to derive relationships between FF and GDF parameters. Virtual steatosis models were created based on derived morphological and statistical descriptors, and the MRI signal was synthesized at 1.5 T and 3 T. R2 * and FF values were calculated using single- and dual-R2 * models and compared with in vivo R2 *-FF calibrations and simulated FFs. The steatosis models generated with regional anisotropy and NN distribution closely mimicked the true in vivo fat morphology. For both R2 * models, predicted R2 * values showed positive correlation with FFs, with slopes similar to those of the in vivo calibrations (P > 0.05), and predicted FFs showed excellent agreement with true FFs (R2 > 0.99), with slopes close to unity. Our study, hence, demonstrates the proof of concept for generating steatosis models from histologic data and synthesizing MRI signal to show the expected signal relaxation under conditions of steatosis. Our results suggest that a single R2 * is sufficient to accurately estimate R2 * and FF values for lower FFs, which agrees with in vivo studies. Future work involves characterizing and building steatosis models at higher FFs and testing single- and dual-R2 * models for accurate assessment of steatosis.
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Affiliation(s)
- Utsav Shrestha
- Department of Biomedical Engineering, The University of Memphis, Memphis, Tennessee, USA
- Department of Computer Science, The University of Memphis, Memphis, Tennessee, USA
| | - Marie van der Merwe
- College of Health Sciences, The University of Memphis, Memphis, Tennessee, USA
| | - Nirman Kumar
- Department of Computer Science, The University of Memphis, Memphis, Tennessee, USA
| | - Eddie Jacobs
- Department of Electrical & Computer Engineering, The University of Memphis, Memphis, Tennessee, USA
| | - Sanjaya K Satapathy
- Department of Medicine, North Shore University Hospital/Northwell Health, Manhasset, New York, USA
| | - Cara Morin
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Aaryani Tipirneni-Sajja
- Department of Biomedical Engineering, The University of Memphis, Memphis, Tennessee, USA
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, 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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Zheng Y, Zhang X, Wang X, Wang K, Cui Y. Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China. BMJ Open 2021; 11:e041040. [PMID: 33478962 PMCID: PMC7825257 DOI: 10.1136/bmjopen-2020-041040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVES Kashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control. DESIGN Time series study. SETTING KASHGAR, CHINA Kashgar, China. METHODS We used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy. RESULTS After careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model. CONCLUSIONS Both the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.
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Affiliation(s)
- Yanling Zheng
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Xueliang Zhang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Xijiang Wang
- Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, China
| | - Kai Wang
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, China
| | - Yan Cui
- Center for Disease Control and Prevention of Xinjiang Uygur Autonomous Region, Urumqi, China
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A hybrid (iron–fat–water) phantom for liver iron overload quantification in the presence of contaminating fat using magnetic resonance imaging. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2019; 33:385-392. [DOI: 10.1007/s10334-019-00795-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 12/19/2022]
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