کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5491393 | 1525006 | 2017 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Accelerating MRI fat quantification using a signal model-based dictionary to assess gastric fat volume and distribution of fat fraction
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کلمات کلیدی
RMSEROISNROMPFat quantification - اندازه گیری چربیIdeal - ایده آلGastric emptying - تخلیه معدهMagnetic resonance - تشدید مغناطیسیParallel imaging - تصویربرداری موازیOrthogonal matching pursuit - تعقیب متعارف مطابقGastrointestinal - دستگاه گوارشtwo-dimensional - دو بعدیRoot mean square error - ریشه میانگین خطای مربعCompressed sensing - سنجش فشرده region-of-interest - منطقه مورد نظرSignal-to-noise ratio - نسبت سیگنال به نویزFat digestion - هضم چربیone-dimensional - یک بعدی
موضوعات مرتبط
مهندسی و علوم پایه
فیزیک و نجوم
فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
To quantify intragastric fat volume and distribution with accelerated magnetic resonance (MR) imaging using signal model-based dictionaries (DICT) in comparison to conventional parallel imaging (CG-SENSE). This study was approved by the local ethics committee and written informed consent was obtained. Seven healthy subjects were imaged after intake of a lipid emulsion and data at three different time points during the gastric emptying process was acquired in order to cover a range of fat fractions. Fully sampled and prospectively undersampled image data at a reduction factor of 4 were acquired using a multi gradient echo sequence at 1.5T. Retrospectively and prospectively undersampled data were reconstructed with DICT and CG-SENSE. Image quality of the retrospectively undersampled data was assessed relative to the fully sampled reference using the root mean square error (RMSE). In order to assess the agreement of fat volumes and intragastric fat distribution, Bland-Altman analysis and linear regression were performed on the data. The RMSE in intragastric content (ÎRMSE = 0.10 ± 0.01, P < 0.001) decreased significantly with DICT relative to CG-SENSE. CG-SENSE overestimated fat volumes (bias 2.1 ± 1.3 mL; confidence limits 5.4 and â 1.1 mL) in comparison to the prospective DICT reconstruction (bias â 0.1 ± 0.7 mL; confidence limits 1.8 and â 2.0 mL). There was a good agreement in fat distribution between the images reconstructed by retrospective DICT and the reference images (regression slope: 1.01, R2 = 0.961). Accelerating gastric MRI by integrating a dictionary-based signal model allows for improved image quality and increases accuracy of fat quantification during breathholds.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 37, April 2017, Pages 81-89
Journal: Magnetic Resonance Imaging - Volume 37, April 2017, Pages 81-89
نویسندگان
Dian Liu, Andreas Steingoetter, Helen L Parker, Jelena Curcic, Sebastian Kozerke,