Comparative Study of Ultrasonography, Computed Tomography, Magnetic Resonance Imaging, and Magnetic Resonance Spectroscopy for the Diagnosis of Fatty Liver in a Rat Model

Noh, H M; Song, Xiaoli; Heo, Suk Hee; Kim, Jin Woong; Shin, Sang Soo; Ahn, Kyu Youn; Jeong, Yong Yeon; Kang, Hyejung

doi:10.3348/jksr.2017.76.1.14

Cited by 5 publications

(17 citation statements)

References 40 publications

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“…Von den 14 Studien zur Fettquantifizierung in der Leber wurde die Fettquantifizierung in der DECT in 9 Studien gegenüber der Histologie verglichen [22][23][24][25][26][27][28][29][30], in 5 gegenüber der MRT-Chemical-Shift-Relaxometrie [5,24,25,27,28], in 4 gegenüber der MRS [5,25,28,29] und in 9 gegenüber der SECT [5,22,24,27,28,[31][32][33][34] (▶ Tab. 1).…”

Section: Ergebnisse: Fettquantifizierung In Der Leberunclassified

“…In den Studien, in denen die DECT gegenüber der Histologie verglichen wurde, wurden die DECT-Daten in den ältesten 1998 und 2003 erschienenen Studien sequenziell generiert [22,23]. Danach erfolgte die DECT-Datenerhebung über die Fast-kVP-Switching-DECT [24,25,27,29,30] sowie ab 2014 partiell auch mit der Dual-Source-DECT [26,28]. Lediglich 2 Studien untersuchten Patienten (v. a. Steatosis im Ultraschall) [22,29], die restlichen Messungen fanden in Ratten oder Kaninchen statt.…”

Section: Stellenwert Der Dect Gegenüber Der Histologieunclassified

“…Die höchste Korrelation wurde dabei für die DECT-ΔHU in einer sequenziellen DECT-Studie in Kaninchen angegeben (r = 0,95, für 90, 120 kV [23]). Die Validität dieses einzelnen Studienergebnisses vorausgesetzt, entspricht das Korrelationsniveau dem der Histologie mit der MRS, die als Goldstandard der radiologischen Fettquantifizierung gilt (r = 0,916 [28], r = 0,894 [25]). Eine ebenfalls gute, leicht geringere Korrelation mit der Histologie wurde für die in 2 Studien mit Fast-kVP-Switching-DECT in Mäusen und Ratten untersuchten Abschwächungskoeffizienten auf virtuell monoenergetischen Bildern genannt (r = -0,892 bei 40 keV [25], r 2 ≤ 0,88 bei 65 keV [24]).…”

Section: Stellenwert Der Dect Gegenüber Der Histologieunclassified

“…Eine ebenfalls gute, leicht geringere Korrelation mit der Histologie wurde für die in 2 Studien mit Fast-kVP-Switching-DECT in Mäusen und Ratten untersuchten Abschwächungskoeffizienten auf virtuell monoenergetischen Bildern genannt (r = -0,892 bei 40 keV [25], r 2 ≤ 0,88 bei 65 keV [24]). Dieses Korrelationsniveau war vergleichbar mit der Korrelation zwischen Histologie und MRT-Fettfraktion (MRT-FF), die über T2*-und DIXON-Chemical-Shift-Relaxometrie bestimmt wurde (r 2 ≤ 0,95 [24], r = 0,834 [27], r = 0,869 [28], r = 0,882 [25]).…”

Section: Stellenwert Der Dect Gegenüber Der Histologieunclassified

“…Ausgenommen einer Studie zur Dual-Source-DECT [28] verwendeten 5 weitere Studien, die die DECT mit der MRT verglichen, Fast-kVp-Switching-DECT. Von diesen verglich eine Studie die DECT mit der MRT-Chemical-Shift-Relaxometrie in Patienten [5].…”

Section: Stellenwert Der Dect Gegenüber Der Histologieunclassified

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Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review

Molwitz

Leiderer

Özden

et al. 2020

Rofo

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Background With dual-energy computed tomography (DECT) it is possible to quantify certain elements and tissues by their specific attenuation, which is dependent on the X-ray spectrum. This systematic review provides an overview of the suitability of DECT for fat quantification in clinical diagnostics compared to established methods, such as histology, magnetic resonance imaging (MRI) and single-energy computed tomography (SECT). Method Following a systematic literature search, studies which validated DECT fat quantification by other modalities were included. The methodological heterogeneity of all included studies was processed. The study results are presented and discussed according to the target organ and specifically for each modality of comparison. Results Heterogeneity of the study methodology was high. The DECT data was generated by sequential CT scans, fast-kVp-switching DECT, or dual-source DECT. All included studies focused on the suitability of DECT for the diagnosis of hepatic steatosis and for the determination of the bone marrow fat percentage and the influence of bone marrow fat on the measurement of bone mineral density. Fat quantification in the liver and bone marrow by DECT showed valid results compared to histology, MRI chemical shift relaxometry, magnetic resonance spectroscopy, and SECT. For determination of hepatic steatosis in contrast-enhanced CT images, DECT was clearly superior to SECT. The measurement of bone marrow fat percentage via DECT enabled the bone mineral density quantification more reliably. Conclusion DECT is an overall valid method for fat quantification in the liver and bone marrow. In contrast to SECT, it is especially advantageous to diagnose hepatic steatosis in contrast-enhanced CT examinations. In the bone marrow DECT fat quantification allows more valid quantification of bone mineral density than conventional methods. Complementary studies concerning DECT fat quantification by split-filter DECT or dual-layer spectral CT and further studies on other organ systems should be conducted. Key points: Citation Format

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Section: Ergebnisse: Fettquantifizierung In Der Leberunclassified

Section: Stellenwert Der Dect Gegenüber Der Histologieunclassified

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Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review

Molwitz

Leiderer

Özden

et al. 2020

Rofo

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Dual-Energy Computed Tomography in Diffuse Liver Diseases

Marri

Madhusudhan

2022

Journal of Gastrointestinal and Abdominal Radiology

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Dual-energy computed tomography (DECT) is an advancement in the field of CT, where images are acquired at two energies. Materials are identified and quantified based on their attenuation pattern at two different energy beams using various material decomposition algorithms. With its ability to identify and quantify materials such as fat, calcium, iron, and iodine, DECT adds great value to conventional CT and has innumerable applications in body imaging. Continuous technological advances in CT scanner hardware, material decomposition algorithms, and image reconstruction software have led to considerable growth of these applications. Among all organs, the liver is the most widely investigated by DECT, and DECT has shown promising results in most liver applications. In this article, we aim to provide an overview of the role of DECT in the assessment of diffuse liver diseases, mainly the deposition of fat, fibrosis, and iron and review the most relevant literature.

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Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study

Kim¹,

Lee²,

Choi³

2021

JMIR Med Inform

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Background Fat fraction values obtained from magnetic resonance imaging (MRI) can be used to obtain an accurate diagnosis of fatty liver diseases. However, MRI is expensive and cannot be performed for everyone. Objective In this study, we aim to develop multi-view ultrasound image–based convolutional deep learning models to detect fatty liver disease and yield fat fraction values. Methods We extracted 90 ultrasound images of the right intercostal view and 90 ultrasound images of the right intercostal view containing the right renal cortex from 39 cases of fatty liver (MRI–proton density fat fraction [MRI–PDFF] ≥ 5%) and 51 normal subjects (MRI–PDFF < 5%), with MRI–PDFF values obtained from Good Gang-An Hospital. We obtained combined liver and kidney-liver (CLKL) images to train the deep learning models and developed classification and regression models based on the VGG19 model to classify fatty liver disease and yield fat fraction values. We employed the data augmentation techniques such as flip and rotation to prevent the deep learning model from overfitting. We determined the deep learning model with performance metrics such as accuracy, sensitivity, specificity, and coefficient of determination (R2). Results In demographic information, all metrics such as age and sex were similar between the two groups—fatty liver disease and normal subjects. In classification, the model trained on CLKL images achieved 80.1% accuracy, 86.2% precision, and 80.5% specificity to detect fatty liver disease. In regression, the predicted fat fraction values of the regression model trained on CLKL images correlated with MRI–PDFF values (R2=0.633), indicating that the predicted fat fraction values were moderately estimated. Conclusions With deep learning techniques and multi-view ultrasound images, it is potentially possible to replace MRI–PDFF values with deep learning predictions for detecting fatty liver disease and estimating fat fraction values.

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Comparative Study of Ultrasonography, Computed Tomography, Magnetic Resonance Imaging, and Magnetic Resonance Spectroscopy for the Diagnosis of Fatty Liver in a Rat Model

Cited by 5 publications

References 40 publications

Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review

Dual-Energy Computed Tomography for Fat Quantification in the Liver and Bone Marrow: A Literature Review

Dual-Energy Computed Tomography in Diffuse Liver Diseases

Deep Learning Techniques for Fatty Liver Using Multi-View Ultrasound Images Scanned by Different Scanners: Development and Validation Study

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