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Li H, Priest AN, Horvat-Menih I, Huang Y, Li S, Stewart GD, Mendichovszky IA, Francis ST, Gallagher FA. Fast and High-Resolution T 2 Mapping Based on Echo Merging Plus k-t Undersampling with Reduced Refocusing Flip Angles (TEMPURA) as Methods for Human Renal MRI. Magn Reson Med 2024; 92:1138-1148. [PMID: 38730565 DOI: 10.1002/mrm.30115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 03/05/2024] [Accepted: 03/29/2024] [Indexed: 05/13/2024]
Abstract
PURPOSE To develop a highly accelerated multi-echo spin-echo method, TEMPURA, for reducing the acquisition time and/or increasing spatial resolution for kidney T2 mapping. METHODS TEMPURA merges several adjacent echoes into one k-space by either combining independent echoes or sharing one echo between k-spaces. The combined k-space is reconstructed based on compressed sensing theory. Reduced flip angles are used for the refocusing pulses, and the extended phase graph algorithm is used to correct the effects of indirect echoes. Two sequences were developed: a fast breath-hold sequence; and a high-resolution sequence. The performance was evaluated prospectively on a phantom, 16 healthy subjects, and two patients with different types of renal tumors. RESULTS The fast TEMPURA method reduced the acquisition time from 3-5 min to one breath-hold (18 s). Phantom measurements showed that fast TEMPURA had a mean absolute percentage error (MAPE) of 8.2%, which was comparable to a standardized respiratory-triggered sequence (7.4%), but much lower than a sequence accelerated by purely k-t undersampling (21.8%). High-resolution TEMPURA reduced the in-plane voxel size from 3 × 3 to 1 × 1 mm2, resulting in improved visualization of the detailed anatomical structure. In vivo T2 measurements demonstrated good agreement (fast: MAPE = 1.3%-2.5%; high-resolution: MAPE = 2.8%-3.3%) and high correlation coefficients (fast: R = 0.85-0.98; high-resolution: 0.82-0.96) with the standardized method, outperforming k-t undersampling alone (MAPE = 3.3-4.5%, R = 0.57-0.59). CONCLUSION TEMPURA provides fast and high-resolution renal T2 measurements. It has the potential to improve clinical throughput and delineate intratumoral heterogeneity and tissue habitats at unprecedented spatial resolution.
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Affiliation(s)
- Hao Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | | | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
| | - Shaohang Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Grant D Stewart
- Department of Surgery, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | - Ferdia A Gallagher
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, UK
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Li H, Daniel AJ, Buchanan CE, Nery F, Morris DM, Li S, Huang Y, Sousa JA, Sourbron S, Mendichovszky IA, Thomas DL, Priest AN, Francis ST. Improvements in Between-Vendor MRI Harmonization of Renal T 2 Mapping using Stimulated Echo Compensation. J Magn Reson Imaging 2024. [PMID: 38380700 DOI: 10.1002/jmri.29282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND T2 mapping is valuable to evaluate pathophysiology in kidney disease. However, variations in T2 relaxation time measurements across MR scanners and vendors may occur requiring additional correction. PURPOSE To harmonize renal T2 measurements between MR vendor platforms, and use an extended-phase-graph-based fitting method ("StimFit") to correct stimulated echoes and reduce between-vendor variations. STUDY TYPE Prospective. SUBJECTS 8 healthy "travelling" volunteers (37.5% female, 32 ± 6 years) imaged on four MRI systems across three vendors at four sites, 10 healthy volunteers (50% female, 32 ± 8 years) scanned multiple times on a given MR scanner for repeatability evaluation. ISMRM/NIST system phantom scanned for evaluation of T2 accuracy. FIELD STRENGTH/SEQUENCE 3T, multiecho spin-echo sequence. ASSESSMENT T2 images fit using conventional monoexponential fitting and "StimFit." Mean absolute percentage error (MAPE) of phantom measurements with reference T2 values. Average cortex and medulla T2 values compared between MR vendors, with masks obtained from T2 -weighted images and T1 maps. Full-width-at-half-maximum (FWHM) T2 distributions to evaluate local homogeneity of measurements. STATISTICAL TESTS Coefficient of variation (CV), linear mixed-effects model, analysis of variance, student's t-tests, Bland-Altman plots, P-value <0.05 considered statistically significant. RESULTS In the ISMRM/NIST phantom, "StimFit" reduced the MAPE from 4.9%, 9.1%, 24.4%, and 18.1% for the four sites (three vendors) to 3.3%, 3.0%, 6.6%, and 4.1%, respectively. In vivo, there was a significant difference in kidney T2 measurements between vendors using a monoexponential fit, but not with "StimFit" (P = 0.86 and 0.92, cortex and medulla, respectively). The intervendor CVs of T2 measures were reduced from 8.0% to 2.6% (cortex) and 7.1% to 2.8% (medulla) with StimFit, resulting in no significant differences for the CVs of intravendor repeat acquisitions (P = 0.13 and 0.05). "StimFit" significantly reduced the FWHM of T2 distributions in the cortex and whole kidney. DATA CONCLUSION Stimulated-echo correction reduces renal T2 variation across MR vendor platforms. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Hao Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - Alexander J Daniel
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
| | | | - Fábio Nery
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, London, UK
| | - David M Morris
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Shaohang Li
- The Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Yuan Huang
- Department of Radiology, University of Cambridge, Cambridge, UK
- EPSRC Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK
| | - João A Sousa
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Steven Sourbron
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Iosif A Mendichovszky
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - David L Thomas
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Andrew N Priest
- Department of Radiology, University of Cambridge, Cambridge, UK
- Department of Radiology, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and School of Medicine, Nottingham, UK
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Stabinska J, Wittsack HJ, Lerman LO, Ljimani A, Sigmund EE. Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions. J Magn Reson Imaging 2023:10.1002/jmri.29127. [PMID: 37991093 PMCID: PMC11117411 DOI: 10.1002/jmri.29127] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension and Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Eric E. Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Health, New York City, New York, USA
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Li X, Li Z, Liu L, Pu Y, Ji Y, Tang W, Chen T, Liang Q, Zhang X. Early assessment of acute kidney injury in severe acute pancreatitis with multimodal DWI: an animal model. Eur Radiol 2023; 33:7744-7755. [PMID: 37368106 DOI: 10.1007/s00330-023-09782-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/16/2023] [Accepted: 03/26/2023] [Indexed: 06/28/2023]
Abstract
OBJECTIVES To evaluate the feasibility of multimodal diffusion-weighted imaging (DWI) for detecting the occurrence and severity of acute kidney injury (AKI) caused by severe acute pancreatitis (SAP) in rats. METHODS SAP was induced in thirty rats by the retrograde injection of 5.0% sodium taurocholate through the biliopancreatic duct. Six rats underwent MRI of the kidneys 24 h before and 2, 4, 6, and 8 h after this AKI model was generated. Conventional and functional MRI sequences were used, including intravoxel incoherent motion imaging (IVIM), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DTI). The main DWI parameters and histological results were analyzed. RESULTS The fast apparent diffusion coefficient (ADC) of the renal cortex was significantly reduced at 2 h, as was the fractional anisotropy (FA) value of the renal cortex on DTI. The mean kurtosis (MK) values for the renal cortex and medulla gradually increased after model generation. The renal histopathological score was negatively correlated with the medullary slow ADC, fast ADC, and perfusion scores for both the renal cortex and medulla, as were the ADC and FA values of the renal medulla in DTI, whereas the MK values of the cortex and medulla were positively correlated (r = 0.733, 0.812). Thus, the cortical fast ADC, medullary MK, FADTI, and slow ADC were optimal parameters for diagnosing AKI. Of these parameters, cortical fast ADC had the highest diagnostic efficacy (AUC = 0.950). CONCLUSIONS The fast ADC of the renal cortex is the core indicator of early AKI, and the medullary MK value might serve as a sensitive biomarker for grading renal injury in SAP rats. CLINICAL RELEVANCE STATEMENT The multimodal parameters of renal IVIM, DTI, and DKI are potential beneficial for the early diagnosis and severity grading of renal injury in SAP patients. KEY POINTS • The multimodal parameters of renal DWI, including IVIM, DTI, and DKI, may be valuable for the noninvasive detection of early AKI and the severity grading of renal injury in SAP rats. • Cortical fast ADC, medullary MK, FA, and slow ADC are optimal parameters for early diagnosis of AKI, and cortical fast ADC has the highest diagnostic efficacy. • Medullary fast ADC, MK, and FA as well as cortical MK are useful for predicting the severity grade of AKI, and the renal medullary MK value exhibits the strongest correlation with pathological scores.
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Affiliation(s)
- Xinghui Li
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Zenghui Li
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Lu Liu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Yu Pu
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Yifan Ji
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Wei Tang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Tianwu Chen
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China
| | - Qi Liang
- Department of Clinical Laboratory, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, Sichuan Province, China.
| | - Xiaoming Zhang
- Medical Imaging Key Laboratory of Sichuan Province, Department of Radiology, Affiliated Hospital of North Sichuan Medical College, 1 South Maoyuan Street, Nanchong, 637001, China.
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Zhang Z, Li S, Wang W, Zhang Y, Wang K, Cheng J, Wen B. Synthetic MRI for the quantitative and morphologic assessment of head and neck tumors: a preliminary study. Dentomaxillofac Radiol 2023; 52:20230103. [PMID: 37427697 PMCID: PMC10461255 DOI: 10.1259/dmfr.20230103] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/05/2023] [Accepted: 06/07/2023] [Indexed: 07/11/2023] Open
Abstract
OBJECTIVES To evaluate the feasibility of synthetic MRI for quantitative and morphologic assessment of head and neck tumors and compare the results with the conventional MRI approach. METHODS AND MATERIALS A total of 92 patients with different head and neck tumor histology who underwent conventional and synthetic MRI were retrospectively recruited. The quantitative T1, T2, proton density (PD), and apparent diffusion coefficient (ADC) values of 38 benign and 54 malignant tumors were measured and compared. Diagnostic efficacy for differentiating malignant and benign tumors was evaluated with receiver operating characteristic (ROC) analysis and integrated discrimination index. The image quality of conventional and synthetic T1W/T2W images on a 5-level Likert scale was also compared with Wilcoxon signed rank test. RESULTS T1, T2 and ADC values of malignant head and neck tumors were smaller than those of benign tumors (all p < 0.05). T2 and ADC values showed better diagnostic efficacy than T1 for distinguishing malignant tumors from benign tumors (both p < 0.05). Adding the T2 value to ADC increased the area under the curve from 0.839 to 0.886, with an integrated discrimination index of 4.28% (p < 0.05). In terms of overall image quality, synthetic T2W images were comparable to conventional T2W images, while synthetic T1W images were inferior to conventional T1W images. CONCLUSIONS Synthetic MRI can facilitate the characterization of head and neck tumors by providing quantitative relaxation parameters and synthetic T2W images. T2 values added to ADC values may further improve the differentiation of tumors.
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Affiliation(s)
- Zanxia Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shujian Li
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- MR Research China, GE Healthcare, Beijing, China
| | - Jingliang Cheng
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Hua C, Qiu L, Zhou L, Zhuang Y, Cai T, Xu B, Hao S, Fang X, Wang L, Jiang H. Value of multiparametric magnetic resonance imaging for evaluating chronic kidney disease and renal fibrosis. Eur Radiol 2023; 33:5211-5221. [PMID: 37148348 DOI: 10.1007/s00330-023-09674-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES To identify optimized MRI markers for evaluating chronic kidney disease (CKD) and renal interstitial fibrosis (IF). MATERIALS AND METHODS This prospective study included 43 patients with CKD and 20 controls. The CKD group was divided into mild and moderate-to-severe subgroups based on pathological results. Scanned sequences included T1 mapping, R2* mapping, intravoxel incoherent motion imaging, and diffusion-weighted imaging. One-way analyses of variance were used to compare MRI parameters among groups. Correlations of MRI parameters with estimated glomerular filtration rate (eGFR) and renal IF were analyzed using age as covariates. The support vector machine (SVM) model was used to evaluate the diagnostic efficacy of multiparametric MRI. RESULTS Compared to control values, renal cortical apparent diffusion coefficient (cADC), medullary ADC (mADC), cortical pure diffusion coefficient (cDt), medullary Dt (mDt), cortical shifted apparent diffusion coefficient (csADC), and medullary sADC (msADC) values gradually decreased in the mild and moderate-to-severe groups, while cortical T1 (cT1) and medullary T1 (mT1) values gradually increased. Values of cADC, mADC, cDt, mDt, cT1, mT1, csADC, and msADC were significantly associated with eGFR and IF (p < 0.001). The SVM model indicated that multiparametric MRI combining cT1 and csADC can distinguish patients with CKD from controls with high accuracy (0.84), sensitivity (0.70), and specificity (0.92) (AUC: 0.96). Multiparametric MRI combining cT1 and cADC exhibited high accuracy (0.91), sensitivity (0.95), and specificity (0.81) for evaluating IF severity (AUC: 0.96). CONCLUSION Multiparametric MRI combining T1 mapping and diffusion imaging may be of clinical utility in non-invasive assessment of CKD and IF. CLINICAL RELEVANCE STATEMENT This study shows that multiparametric MRI combining T1 mapping and diffusion imaging may be clinically useful in the non-invasive assessment of chronic kidney disease (CKD) and interstitial fibrosis; this could provide information for risk stratification, diagnosis, treatment, and prognosis. KEY POINTS • Optimized MRI markers for evaluating chronic kidney disease and renal interstitial fibrosis were investigated. • Renal cortex/medullary T1 values increased as interstitial fibrosis increased; cortical shifted apparent diffusion coefficient (csADC) correlated significantly with eGFR and interstitial fibrosis. • Support vector machine (SVM) combining cortical T1 (cT1) and csADC/cADC effectively identifies chronic kidney disease and accurately predicts renal interstitial fibrosis.
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Affiliation(s)
- Chenchen Hua
- Diagnostic Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
- Department of Diagnostic Radiology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Lu Qiu
- Department of Diagnostic Radiology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Leting Zhou
- Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Yi Zhuang
- Department of Diagnostic Radiology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Ting Cai
- Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Bin Xu
- Diagnostic Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Shaowei Hao
- Siemens Healthineers Digital Technology (Shanghai) CO., Ltd, Shanghai, China
| | - Xiangming Fang
- Diagnostic Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China
| | - Liang Wang
- Department of Nephrology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China.
| | - Haoxiang Jiang
- Department of Diagnostic Radiology, The Affiliated Wuxi Children's Hospital of Nanjing Medical University, No. 299 Qingyang Road, Wuxi, China.
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Aslam I, Aamir F, Kassai M, Crowe LA, Poletti PA, de Seigneux S, Moll S, Berchtold L, Vallée JP. Validation of automatically measured T1 map cortico-medullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys. PLoS One 2023; 18:e0277277. [PMID: 36791140 PMCID: PMC9931131 DOI: 10.1371/journal.pone.0277277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 10/24/2022] [Indexed: 02/16/2023] Open
Abstract
MRI T1-mapping is an important non-invasive tool for renal diagnosis. Previous work shows that ΔT1 (cortex-medullary difference in T1) has significant correlation with interstitial fibrosis in chronic kidney disease (CKD) allograft patients. However, measuring cortico-medullary values by manually drawing ROIs over cortex and medulla (a gold standard method) is challenging, time-consuming, subjective and requires human training. Moreover, such subjective ROI placement may also affect the work reproducibility. This work proposes a deep learning-based 2D U-Net (RCM U-Net) to auto-segment the renal cortex and medulla of CKD allograft kidney T1 maps. Furthermore, this study presents a correlation of automatically measured ΔT1 values with eGFR and percentage fibrosis in allograft kidneys. Also, the RCM U-Net correlation results are compared with the manual ROI correlation analysis. The RCM U-Net has been trained and validated on T1 maps from 40 patients (n = 2400 augmented images) and tested on 10 patients (n = 600 augmented images). The RCM U-Net segmentation results are compared with the standard VGG16, VGG19, ResNet34 and ResNet50 networks with U-Net as backbone. For clinical validation of the RCM U-Net segmentation, another set of 114 allograft kidneys patient's cortex and medulla were automatically segmented to measure the ΔT1 values and correlated with eGFR and fibrosis. Overall, the RCM U-Net showed 50% less Mean Absolute Error (MAE), 16% better Dice Coefficient (DC) score and 12% improved results in terms of Sensitivity (SE) over conventional CNNs (i.e. VGG16, VGG19, ResNet34 and ResNet50) while the Specificity (SP) and Accuracy (ACC) did not show significant improvement (i.e. 0.5% improvement) for both cortex and medulla segmentation. For eGFR and fibrosis assessment, the proposed RCM U-Net correlation coefficient (r) and R-square (R2) was better correlated (r = -0.2, R2 = 0.041 with p = 0.039) to eGFR than manual ROI values (r = -0.19, R2 = 0.037 with p = 0.051). Similarly, the proposed RCM U-Net had noticeably better r and R2 values (r = 0.25, R2 = 0.065 with p = 0.007) for the correlation with the renal percentage fibrosis than the Manual ROI results (r = 0.3, R2 = 0.091 and p = 0.0013). Using a linear mixed model, T1 was significantly higher in the medulla than in the cortex (p<0.0001) and significantly lower in patients with cellular rejection when compared to both patients without rejection and those with humoral rejection (p<0.001). There was no significant difference in T1 between patients with and without humoral rejection (p = 0.43), nor between the types of T1 measurements (Gold standard manual versus automated RCM U-Net) (p = 0.7). The cortico-medullary area ratio measured by the RCM U-Net was significantly increased in case of cellular rejection by comparison to humoral rejection (1.6 +/- 0.39 versus 0.99 +/- 0.32, p = 0.019). In conclusion, the proposed RCM U-Net provides more robust auto-segmented cortex and medulla than the other standard CNNs allowing a good correlation of ΔT1 with eGFR and fibrosis as reported in literature as well as the differentiation of cellular and humoral transplant rejection. Therefore, the proposed approach is a promising alternative to the gold standard manual ROI method to measure T1 values without user interaction, which helps to reduce analysis time and improves reproducibility.
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Affiliation(s)
- Ibtisam Aslam
- Service of Radiology, University Hospital of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Fariha Aamir
- Medical Image Processing Research Group (MIPRG), Department of Electrical & Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan
| | - Miklós Kassai
- Service of Radiology, University Hospital of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lindsey A. Crowe
- Service of Radiology, University Hospital of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pierre-Alexandre Poletti
- Service of Radiology, University Hospital of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Sophie de Seigneux
- Service of Nephrology, Department of Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Solange Moll
- Department of Pathology, Institute of Clinical Pathology, University Hospital of Geneva, Geneva, Switzerland
| | - Lena Berchtold
- Service of Nephrology, Department of Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Jean-Paul Vallée
- Service of Radiology, University Hospital of Geneva and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- * E-mail:
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Echeverria‐Chasco R, Martin‐Moreno PL, Garcia‐Fernandez N, Vidorreta M, Aramendia‐Vidaurreta V, Cano D, Villanueva A, Bastarrika G, Fernández‐Seara MA. Multiparametric renal magnetic resonance imaging: A reproducibility study in renal allografts with stable function. NMR IN BIOMEDICINE 2023; 36:e4832. [PMID: 36115029 PMCID: PMC10078573 DOI: 10.1002/nbm.4832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 06/15/2023]
Abstract
Monitoring renal allograft function after transplantation is key for the early detection of allograft impairment, which in turn can contribute to preventing the loss of the allograft. Multiparametric renal MRI (mpMRI) is a promising noninvasive technique to assess and characterize renal physiopathology; however, few studies have employed mpMRI in renal allografts with stable function (maintained function over a long time period). The purposes of the current study were to evaluate the reproducibility of mpMRI in transplant patients and to characterize normal values of the measured parameters, and to estimate the labeling efficiency of Pseudo-Continuous Arterial Spin Labeling (PCASL) in the infrarenal aorta using numerical simulations considering experimental measurements of aortic blood flow profiles. The subjects were 20 transplant patients with stable kidney function, maintained over 1 year. The MRI protocol consisted of PCASL, intravoxel incoherent motion, and T1 inversion recovery. Phase contrast was used to measure aortic blood flow. Renal blood flow (RBF), diffusion coefficient (D), pseudo-diffusion coefficient (D*), flowing fraction ( f ), and T1 maps were calculated and mean values were measured in the cortex and medulla. The labeling efficiency of PCASL was estimated from simulation of Bloch equations. Reproducibility was assessed with the within-subject coefficient of variation, intraclass correlation coefficient, and Bland-Altman analysis. Correlations were evaluated using the Pearson correlation coefficient. The significance level was p less than 0.05. Cortical reproducibility was very good for T1, D, and RBF, moderate for f , and low for D*, while medullary reproducibility was good for T1 and D. Significant correlations in the cortex between RBF and f (r = 0.66), RBF and eGFR (r = 0.64), and D* and eGFR (r = -0.57) were found. Normal values of the measured parameters employing the mpMRI protocol in kidney transplant patients with stable function were characterized and the results showed good reproducibility of the techniques.
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Affiliation(s)
- Rebeca Echeverria‐Chasco
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Paloma L. Martin‐Moreno
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Department of NephrologyClínica Universidad de NavarraPamplonaSpain
| | - Nuria Garcia‐Fernandez
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Department of NephrologyClínica Universidad de NavarraPamplonaSpain
| | | | - Verónica Aramendia‐Vidaurreta
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - David Cano
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
| | - Arantxa Villanueva
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
- Electrical Electronics and Communications Engineering Department and Smart Cities InstitutePublic University of NavarrePamplonaSpain
| | - Gorka Bastarrika
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
| | - Maria A. Fernández‐Seara
- Department of RadiologyClínica Universidad de NavarraPamplonaSpain
- IdiSNA, Instituto de Investigación Sanitaria de NavarraPamplonaSpain
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9
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Raunig DL, Pennello GA, Delfino JG, Buckler AJ, Hall TJ, Guimaraes AR, Wang X, Huang EP, Barnhart HX, deSouza N, Obuchowski N. Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap. Acad Radiol 2023; 30:159-182. [PMID: 36464548 PMCID: PMC9825667 DOI: 10.1016/j.acra.2022.10.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 12/02/2022]
Abstract
Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.
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Affiliation(s)
- David L Raunig
- Department of Statistical and Quantitative Sciences, Data Science Institute, Takeda Pharmaceuticals, Cambridge, Massachusetts.
| | - Gene A Pennello
- Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Jana G Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland, Ohio
| | - Erich P Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Huiman X Barnhart
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Nandita deSouza
- Division of Radiotherapy and Imaging, the Insitute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nancy Obuchowski
- Department of Quantitative Health Sciences, Lerner Research Institute Cleveland Clinic Foundation, Cleveland, Ohio
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10
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Chhabra J, Karwarker GV, Rajamanuri M, Maligireddy AR, Dai E, Chahal M, Mannava SM, Alfonso M. The Role of Arterial Spin Labeling Functional MRI in Assessing Perfusion Impairment of Renal Allografts: A Systematic Review. Cureus 2022; 14:e25428. [PMID: 35769679 PMCID: PMC9236280 DOI: 10.7759/cureus.25428] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/28/2022] [Indexed: 11/05/2022] Open
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11
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Dillman JR, Benoit SW, Gandhi DB, Trout AT, Tkach JA, VandenHeuvel K, Devarajan P. Multiparametric quantitative renal MRI in children and young adults: comparison between healthy individuals and patients with chronic kidney disease. Abdom Radiol (NY) 2022; 47:1840-1852. [PMID: 35237897 DOI: 10.1007/s00261-022-03456-x] [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: 12/21/2021] [Revised: 02/11/2022] [Accepted: 02/14/2022] [Indexed: 11/26/2022]
Abstract
PURPOSE Multiparametric quantitative renal MRI may provide noninvasive radiologic biomarkers of chronic kidney disease (CKD) based on investigations in animal models and adults. We aimed to (1) obtain normative multiparametric quantitative MRI data from the kidneys of healthy children and young adults, (2) compare MRI measurements between healthy control participants and patients with CKD, and (3) determine if MRI measurements correlate with clinical and laboratory data as well as histology. METHODS This was a prospective, case-control study of 20 healthy controls and 12 CKD patients who underwent percutaneous renal biopsy ranging from 12 to 23 years of age between October 2018 and March 2020. Kidney function was documented and pathology assessed for fibrosis/inflammation. Utilizing a field strength of 1.5T, we examined renal T1, T2, and T2* relaxation mapping, MR elastography (MRE), and diffusion-weighted imaging (DWI). A single analyst made all manual measurements for quantitative MRI pulse sequences. Independent measurements from cortex, medulla, and whole kidney were obtained by drawing regions of interest on single slices from the upper, mid, and lower kidney. A weighted average was calculated for each kidney; if two kidneys, the right and left were averaged. Continuous variables were compared with Mann-Whitney U test; bivariate relationships were assessed using Spearman rank-order correlation. RESULTS Median estimated glomerular filtration rate (eGFR) was 112.3 ml/min/1.73 m2 in controls (n = 20, 10 females) and 55.0 ml/min/m2 in CKD patients (n = 12, 2 females) (p < 0.0001). Whole kidney (1333 vs. 1291 ms; p = 0.018) and cortical (1212 vs 1137 ms; p < 0.0001) T1 values were higher in CKD patients. Cortical T1 values correlated with eGFR (rho = - 0.62; p = 0.0003) and cystatin C (rho = 0.58; p = 0.0007). Whole kidney (1.87 vs. 2.02 10-3 mm2/s; p = 0.007), cortical (1.89 vs. 2.04 10-3 mm2/s; p = 0.008), and medullary (1.87 vs. 1.98 10-3 mm2/s; p = 0.0095) DWI apparent diffusion coefficients (ADC) were lower in CKD patients. Whole kidney ADC correlated with eGFR (rho = 0.45; p = 0.012) and cystatin C (rho = - 0.46; p = 0.009). Cortical histologic inflammation correlated with DWI ADC (rho = - 0.71; p = 0.011). CONCLUSION Renal T1 relaxation and DWI ADC measurements differ between pediatric healthy controls and CKD patients, correlate with laboratory markers of CKD, and may have histologic correlates.
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Affiliation(s)
- Jonathan R Dillman
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| | - Stefanie W Benoit
- Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Deep B Gandhi
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
| | - Andrew T Trout
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Jean A Tkach
- Department of Radiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45244, USA
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Katherine VandenHeuvel
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Pathology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pathology and Laboratory Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Prasad Devarajan
- Division of Nephrology and Hypertension, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
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12
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Diffusion-Weighted Imaging and Mapping of T1 and T2 Relaxation Time for Evaluation of Chronic Renal Allograft Rejection in a Translational Mouse Model. J Clin Med 2021; 10:jcm10194318. [PMID: 34640336 PMCID: PMC8509284 DOI: 10.3390/jcm10194318] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 12/16/2022] Open
Abstract
We hypothesized that multiparametric MRI is able to non-invasively assess, characterize and monitor renal allograft pathology in a translational mouse model of chronic allograft rejection. Chronic rejection was induced by allogenic kidney transplantation (ktx) of BALB/c-kidneys into C57BL/6-mice (n = 23). Animals after isogenic ktx (n = 18) and non-transplanted healthy animals (n = 22) served as controls. MRI sequences (7T) were acquired 3 and 6 weeks after ktx and quantitative T1, T2 and apparent diffusion coefficient (ADC) maps were calculated. In addition, in a subset of animals, histological changes after ktx were evaluated. Chronic rejection was associated with a significant prolongation of T1 time compared to isogenic ktx 3 (1965 ± 53 vs. 1457 ± 52 ms, p < 0.001) and 6 weeks after surgery (1899 ± 79 vs. 1393 ± 51 ms, p < 0.001). While mean T2 times and ADC were not significantly different between allogenic and isogenic kidney grafts, histogram-based analysis of ADC revealed significantly increased tissue heterogeneity in allografts at both time points (standard derivation/entropy/interquartile range, p < 0.05). Correspondingly, histological analysis showed severe inflammation, graft fibrosis and tissue heterogeneity in allogenic but not in isogenic kidney grafts. In conclusion, renal diffusion weighted imaging and mapping of T2 and T1 relaxation times enable detection of chronic renal allograft rejection in mice. The combined quantitative assessment of mean values and histograms provides non-invasive information of chronic changes in renal grafts and allows longitudinal monitoring.
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13
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Lin L, Zhou X, Dekkers IA, Lamb HJ. Cardiorenal Syndrome: Emerging Role of Medical Imaging for Clinical Diagnosis and Management. J Pers Med 2021; 11:734. [PMID: 34442378 PMCID: PMC8400880 DOI: 10.3390/jpm11080734] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 07/24/2021] [Accepted: 07/24/2021] [Indexed: 12/16/2022] Open
Abstract
Cardiorenal syndrome (CRS) concerns the interconnection between heart and kidneys in which the dysfunction of one organ leads to abnormalities of the other. The main clinical challenges associated with cardiorenal syndrome are the lack of tools for early diagnosis, prognosis, and evaluation of therapeutic effects. Ultrasound, computed tomography, nuclear medicine, and magnetic resonance imaging are increasingly used for clinical management of cardiovascular and renal diseases. In the last decade, rapid development of imaging techniques provides a number of promising biomarkers for functional evaluation and tissue characterization. This review summarizes the applicability as well as the future technological potential of each imaging modality in the assessment of CRS. Furthermore, opportunities for a comprehensive imaging approach for the evaluation of CRS are defined.
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Affiliation(s)
- Ling Lin
- Cardiovascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (L.L.); (I.A.D.); (H.J.L.)
| | - Xuhui Zhou
- Department of Radiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen 510833, China
| | - Ilona A. Dekkers
- Cardiovascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (L.L.); (I.A.D.); (H.J.L.)
| | - Hildo J. Lamb
- Cardiovascular Imaging Group (CVIG), Department of Radiology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (L.L.); (I.A.D.); (H.J.L.)
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14
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Zhang G, Liu Y, Sun H, Xu L, Sun J, An J, Zhou H, Liu Y, Chen L, Jin Z. Texture analysis based on quantitative magnetic resonance imaging to assess kidney function: a preliminary study. Quant Imaging Med Surg 2021; 11:1256-1270. [PMID: 33816165 DOI: 10.21037/qims-20-842] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background Magnetic resonance imaging (MRI) has demonstrated its potential in the evaluation of renal function. Texture analysis (TA) is a novel technique to quantify tissue heterogeneity. We aim to investigate the feasibility of using TA based on the apparent diffusion coefficient (ADC), as well as T1 and T2 maps to evaluate renal function. Methods Patients with impaired renal function and subjects with a normal renal function who underwent renal diffusion weighted imaging (DWI), as well as T1 and T2 mapping at 3T, were prospectively enrolled. The participants were classified into four groups according to the estimated glomerular filtration rate (eGFR, mL/min/1.73 m2): normal (eGFR ≥90), mildly impaired (60≤ eGFR <90), moderately impaired (30≤ eGFR <60), and severely impaired (eGFR <30) renal function groups. Texture features quantified from the renal cortex or medulla were selected to build classifiers to discriminate different renal function groups by plotting receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results In total, 116 candidates were included (94 patients and 22 healthy volunteers, mean age 37.9±14.9 years). There were 46 participants in the normal renal function group, 14 in the mildly impaired renal function group, 27 in the moderately impaired renal function group, and 29 in the severely impaired renal function group. Texture features from the ADC and T1 maps exhibited a good correlation to eGFR. The AUC, sensitivity, specificity, PPV, and NPV to differentiate between the normal and impaired renal function groups were 0.835, 0.792, 0.867, 0.905, and 0.722, respectively; to differentiate between the mildly impaired and moderately impaired groups were 0.937, 0.889, 0.857, 0.923, and 0.800, respectively; and to differentiate between the moderately impaired and severely impaired groups was 0.940, 0.759, 0.889, 0.880, and 0.774, respectively. Conclusions TA based on ADC and T1 maps is feasible for evaluating renal function with relatively good accuracy.
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Affiliation(s)
- Gumuyang Zhang
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Liu
- Department of Nephrology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Hao Sun
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Lili Xu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | | | - Jing An
- MR Collaboration, Siemens Healthcare Ltd., Beijing, China
| | - Hailong Zhou
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Yanhan Liu
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Limeng Chen
- Department of Nephrology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
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15
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Abstract
Interstitial fibrosis with tubule atrophy (IF/TA) is the response to virtually any sustained kidney injury and correlates inversely with kidney function and allograft survival. IF/TA is driven by various pathways that include hypoxia, renin-angiotensin-aldosterone system, transforming growth factor (TGF)-β signaling, cellular rejection, inflammation and others. In this review we will focus on key pathways in the progress of renal fibrosis, diagnosis and therapy of allograft fibrosis. This review discusses the role and origin of myofibroblasts as matrix producing cells and therapeutic targets in renal fibrosis with a particular focus on renal allografts. We summarize current trends to use multi-omic approaches to identify new biomarkers for IF/TA detection and to predict allograft survival. Furthermore, we review current imaging strategies that might help to identify and follow-up IF/TA complementary or as alternative to invasive biopsies. We further discuss current clinical trials and therapeutic strategies to treat kidney fibrosis.Supplemental Visual Abstract; http://links.lww.com/TP/C141.
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16
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Alnazer I, Bourdon P, Urruty T, Falou O, Khalil M, Shahin A, Fernandez-Maloigne C. Recent advances in medical image processing for the evaluation of chronic kidney disease. Med Image Anal 2021; 69:101960. [PMID: 33517241 DOI: 10.1016/j.media.2021.101960] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 11/18/2020] [Accepted: 12/31/2020] [Indexed: 12/31/2022]
Abstract
Assessment of renal function and structure accurately remains essential in the diagnosis and prognosis of Chronic Kidney Disease (CKD). Advanced imaging, including Magnetic Resonance Imaging (MRI), Ultrasound Elastography (UE), Computed Tomography (CT) and scintigraphy (PET, SPECT) offers the opportunity to non-invasively retrieve structural, functional and molecular information that could detect changes in renal tissue properties and functionality. Currently, the ability of artificial intelligence to turn conventional medical imaging into a full-automated diagnostic tool is widely investigated. In addition to the qualitative analysis performed on renal medical imaging, texture analysis was integrated with machine learning techniques as a quantification of renal tissue heterogeneity, providing a promising complementary tool in renal function decline prediction. Interestingly, deep learning holds the ability to be a novel approach of renal function diagnosis. This paper proposes a survey that covers both qualitative and quantitative analysis applied to novel medical imaging techniques to monitor the decline of renal function. First, we summarize the use of different medical imaging modalities to monitor CKD and then, we show the ability of Artificial Intelligence (AI) to guide renal function evaluation from segmentation to disease prediction, discussing how texture analysis and machine learning techniques have emerged in recent clinical researches in order to improve renal dysfunction monitoring and prediction. The paper gives a summary about the role of AI in renal segmentation.
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Affiliation(s)
- Israa Alnazer
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France; AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon.
| | - Pascal Bourdon
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Thierry Urruty
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
| | - Omar Falou
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon; American University of Culture and Education, Koura, Lebanon; Lebanese University, Faculty of Science, Tripoli, Lebanon
| | - Mohamad Khalil
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Ahmad Shahin
- AZM Center for Research in Biotechnology and its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Christine Fernandez-Maloigne
- XLIM-ICONES, UMR CNRS 7252, Université de Poitiers, France; Laboratoire commune CNRS/SIEMENS I3M, Poitiers, France
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