Quantification of liver fat infiltration by magnetic resonance

被引:3
|
作者
Herrera, Rodrigo [1 ]
Penaloza, Francisco [1 ]
Arrietal, Cristobal [1 ]
Zacconi, Flavia [2 ]
Saavedra, Victor [3 ]
Saavedra, Carla [3 ]
Branes, Cecilia [4 ]
Hack, Thomas [4 ]
Uribe, Sergio [1 ,5 ,6 ]
机构
[1] Pontificia Univ Catolica Chile, Ctr Imagenes Biomed, Vicuna Mackenna 4860, Santiago, Chile
[2] Pontificia Univ Catolica Chile, Fac Quim, Santiago, Chile
[3] Ctr Estudios Clin & Invest Med, Santiago, Chile
[4] Nat Consorcio Invest, Santiago, Chile
[5] Pontificia Univ Catolica Chile, Escuela Med, Dept Radiol, Santiago, Chile
[6] Nucleo Milenio Resonancia Magnet Cardiovasc, Santiago, Chile
关键词
Fatty Liver; Magnetic Resonance Imaging; CRYPTOGENIC CIRRHOSIS; NATURAL-HISTORY; DISEASE; STEATOSIS; FRACTION; ASSOCIATION; PREVALENCE; SEPARATION; DIAGNOSIS; ROBUST;
D O I
10.4067/S0034-98872019000700821
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: A simple and inexpensive method is required to assess fatty infiltration of the liver non-invasively. Aim: To develop and compare different methods to quantify liver fat by magnetic resonance and compare it against ultrasound. Material and Methods: Three algorithms were implemented: region growing (RG), graph cuts (GC) and hierarchical (HR), all based on the IDEAL method to obtain water and fat images. Using these images, the proton density fat fraction (PDFF) was calculated. The three methods were tested in phantoms with known fat percentages and later on we acquired images from 20 volunteers with an ultrasound diagnosis of fatty liver disease in different stages. For everyone, the PDFF of the nine liver segments was determined. Results: In phantoms, the mean error between the real fat percentage and the value obtained through the three methods was -1, 26, -1 and -0, 8 for RG, GC and HR, respectively. The hierarchical method was more precise and efficient to obtain PDFF. The results in volunteers revealed that ultrasound showed errors categorizing the severity of hepatic steatosis in more than 50% of volunteers. Conclusions: We developed a tool for magnetic resonance, which allows to quantify fat in the liver. This method is less operator dependent than ultrasound and describes the heterogeneity in the fat distribution along the nine hepatic segments.
引用
收藏
页码:821 / 827
页数:7
相关论文
共 50 条
  • [31] Liver Steatosis Quantification Using Magnetic Resonance Imaging: A Prospective Comparative Study With Liver Biopsy
    Mennesson, Nicolas
    Dumortier, Jerome
    Hervieu, Valerie
    Milot, Laurent
    Guillaud, Olivier
    Scoazec, Jean-Yves
    Pilleul, Frank
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2009, 33 (05) : 672 - 677
  • [32] Effect of Weight Loss on Magnetic Resonance Imaging Estimation of Liver Fat and Volume in Patients With Nonalcoholic Steatohepatitis
    Patel, Niraj S.
    Doycheva, Iliana
    Peterson, Michael R.
    Hooker, Jonathan
    Kisselva, Tatiana
    Schnabl, Bernd
    Seki, Ekihiro
    Sirlin, Claude B.
    Loomba, Rohit
    CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2015, 13 (03) : 561 - 568
  • [33] Hepatic Fat Quantification A Prospective Comparison of Magnetic Resonance Spectroscopy and Analysis Methods for Chemical-Shift Gradient Echo Magnetic Resonance Imaging With Histologic Assessment as the Reference Standard
    Kang, Bo-Kyeong
    Yu, Eun Sil
    Lee, Seung Soo
    Lee, Youngjoo
    Kim, Namkug
    Sirlin, Claude B.
    Cho, Eun Yoon
    Yeom, Suk Keu
    Byun, Jae Ho
    Park, Seong Ho
    Lee, Moon-Gyu
    INVESTIGATIVE RADIOLOGY, 2012, 47 (06) : 368 - 375
  • [34] Quantification of Liver Fat Content with CT and MRI: State of the Art
    Starekova, Jitka
    Hernando, Diego
    Pickhardt, Perry J.
    Reeder, Scott B.
    RADIOLOGY, 2021, 301 (02) : 250 - 262
  • [35] Emerging artificial intelligence applications in liver magnetic resonance imaging
    Hill, Charles E.
    Biasiolli, Luca
    Robson, Matthew D.
    Grau, Vicente
    Pavlides, Michael
    WORLD JOURNAL OF GASTROENTEROLOGY, 2021, 27 (40) : 6825 - 6843
  • [36] Fatty Liver in Acute Pancreatitis: Characteristics in Magnetic Resonance Imaging
    Xiao, Bo
    Zhang, Xiao Ming
    Jiang, Zhi Qiong
    Tang, Wei
    Huang, Xiao Hua
    Yang, Lin
    Feng, Zhi Song
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2012, 36 (04) : 400 - 405
  • [37] Quantitative ultrasound fatty liver evaluation in a pediatric population: comparison with magnetic resonance imaging of liver proton density fat fraction
    Polti, Giorgia
    Frigerio, Francesco
    Del Gaudio, Giovanni
    Pacini, Patrizia
    Dolcetti, Vincenzo
    Renda, Maurizio
    Angeletti, Sergio
    Di Martino, Michele
    Iannetti, Giovanni
    Perla, Francesco Massimo
    Poggiogalle, Eleonora
    Cantisani, Vito
    PEDIATRIC RADIOLOGY, 2023, 53 (12) : 2458 - 2465
  • [38] Marked difference in liver fat measured by histology vs. magnetic resonance-proton density fat fraction: A meta analysis
    Qadri, Sami
    Vartiainen, Emilia
    Lahelma, Mari
    Porthan, Kimmo
    Tang, An
    Idilman, Ilkay S.
    Runge, Jurgen H.
    Juuti, Anne
    Penttila, Anne K.
    Dabek, Juhani
    Lehtimaki, Tiina E.
    Seppaenen, Wenla
    Arola, Johanna
    Arkkila, Perttu
    Stoker, Jaap
    Karcaaltincaba, Musturay
    Pavlides, Michael
    Loomba, Rohit
    Sirlin, Claude B.
    Tukiainen, Taru
    Yki-Jarvinen, Hannele
    JHEP REPORTS, 2024, 6 (01)
  • [39] Proton density fat fraction: magnetic resonance imaging applications beyond the liver
    Idilman, Ilkay S.
    Yildiz, A. Elcin
    Karaosmanoglu, Ali Devrim
    Ozmen, Mustafa N.
    Akata, Deniz
    Karcaaltincaba, Musturay
    DIAGNOSTIC AND INTERVENTIONAL RADIOLOGY, 2022, 28 (01) : 83 - 91
  • [40] Hepatic fat quantification of magnetic resonance imaging whole-liver segmentation for assessing the severity of nonalcoholic fatty liver disease: comparison with a region of interest sampling method
    Zhang, Qin-He
    Zhao, Ying
    Tian, Shi-Feng
    Xie, Lu-Han
    Chen, Li-Hua
    Chen, An-Liang
    Wang, Nan
    Song, Qing-Wei
    Zhang, Hao-Nan
    Xie, Li-Zhi
    Shen, Zhi-Wei
    Liu, Ai-Lian
    QUANTITATIVE IMAGING IN MEDICINE AND SURGERY, 2021, 11 (07) : 2933 - 2942