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Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences.

ABSTRACT: To investigate the difference of MR percent breast density measured from fat-suppressed versus nonfat-suppressed imaging sequences.Breast magnetic resonance imaging (MRI) with and without fat suppression was acquired from 38 subjects. Breasts were divided into subgroups of different morphological patterns ("central" and "intermingled" types). Breast volume, fibroglandular tissue volume, and percent density were measured. The results were compared using nonparametric statistical tests and regarded as significant at p < 0.05.Breast volume, fibroglandular volume, and percent density between fat-suppressed and nonfat-suppressed sequences were highly correlated. Breast volumes measured on these two sequences were almost identical. Fibroglandular tissue volume and percent density, however, had small (<5%) yet significant differences between the two sequences-they were both higher on the fat-suppressed sequence. Intraobserver variability was within 4% for both sequences and different morphological types. The fibroglandular tissue volume measured on downsampled images showed a small (<5%) yet significant difference.The measurement of breast density made on MRI acquired using fat-suppressed and nonfat-suppressed T1W images was about 5% difference, only slightly higher than the intraobserver variability of 3%-4%. When the density data from multiple centers were to be combined, evaluating the degree of difference is needed to take this difference into account.

SUBMITTER: Chang DH

PROVIDER: S-EPMC3215688 | biostudies-other | 2011 Nov

REPOSITORIES: biostudies-other

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Project description:ObjectiveTo compare the reliability of different methods for measuring fat content of pancreas by MR modified Dixon(mDixon) Sequence and accurately evaluate pancreatic fat in as simple a way as possible.MethodsThis is a retrospective study, 64 patients were included in this study who underwent abdominal MR scan that contained the mDixon sequence from June 2019 to May 2020(Included 7 patients with type 2 diabetes and 4 patients with impaired glucose tolerance (IGT), they were admitted to hospital through the obesity clinic set up by endocrine department, all of them were initially diagnosed and untreated). All of the 64 patients were scanned in 3.0T MR (Philips Ingenia II) due to their condition, 10-34 slice pancreas images were obtained, which were different from each other. Three different methods of measurement were employed by two observers using Philips Intellispace Portal software: (1) All images (whole-pancreas) measurement, the whole-pancreatic fat fraction (wPFF) was calculated by software. (2) Interval slices measurement, that is half-pancreatic slices fat fraction (hPFF) measured in the same way, fat fraction obtained by the interlayer assay was calculated. (3) As usual, the fat content of pancreatic head, body and tail fat was measured respectively, and in order to improve credibility, we also measured head、 body and tail in every layer, and its average value was taken. The elapsed time of the above different measurement methods was recorded. Intra-group correlation coefficient (ICC) was used to analyze the consistency of the measured data within and between observers. T-tests and Friedman tests were applied to compare the difference of measured values among groups.ResultsNo matter in normal person or diabetic or IGT, hPFF has shown good stability (ICChPFF = 0.988), and there was no significant difference compared with wPFF. But the average fat percentage composition of head, body and tail were significantly different from wPFF and hPFF (P < 0.01). At the same time, compared with normal person, pancreatic fat content in IGT and diabetic patients showed progressive significance(P<0.05).ConclusionThe distribution of pancreatic fat is not uniform, the method of measuring half pancreas by interlayer data collection can reflect the fat content of the entire pancreas, this suggests that measuring 50% of the pancreas is sufficient, this method effectively saves time and effort without affecting the results, which may have a better clinical application prospect.

Project description:PurposeThe purpose of this study was to evaluate the agreement between paraspinal muscle composition measurements obtained from fat-water images using % fat-signal fraction (%FSF) in comparison to those obtained from T2-weighted magnetic resonance images (MRI) using a thresholding method.MethodsA sample of 35 subjects (19 females, 16 males; 40.26 ± 11.3 years old) was selected from a cohort of patients with chronic low back pain (LBP). Axial T2-weighted and IDEAL (Lava-Flex, 2 echo sequence) fat and water MR images were obtained using a 3.0 Tesla GE scanner. Multifidus, erector spinae, and psoas major muscle composition measurements were acquired bilaterally at L4-L5 and L5-S1 using both imaging sequences and related measurement methods. All measurements were obtained by the same rater, with a minimum of 7 days between each method. Intra-class correlation coefficients (ICCs) were calculated to assess intra-rater reliability. Pearson Correlation and Bland-Altman 95% limits of agreement were used to assess the agreement between both measurement methods.ResultsThe intra-rater reliability was excellent for all measurements with ICCs varying between 0.851 and 0.997. Strong positive correlations indicating a strong relationship between composition measurements were obtained from fat-water and T2-weighted images for bilateral multifidus and erector spinae muscles at both spinal levels and the right psoas major muscle at L4-L5, with correlation coefficient r ranging between 0.67 and 0.92. Bland-Altman plots for bilateral multifidus and erector spinae muscles at both levels revealed excellent agreement between the two methods, however, systematic differences between both methods were evident for psoas major fat measurements.ConclusionOur findings suggest that utilizing fat-water and T2-weighted MR images are comparable for quantifying multifidus and erector spinae muscle composition but not of the psoas major. While this suggests that both methods could be used interchangeably for the multifidus and erector spinae, further evaluation is required to expand and confirm our findings to other spinal levels.

Project description:To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

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Comparison of breast density measured on MR images acquired using fat-suppressed versus nonfat-suppressed sequences.

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