Dataset Information

Fat-based registration of breast dynamic contrast enhanced water images.

ABSTRACT: PURPOSE:In this study, a 3D fat-based deformable registration algorithm was developed for registering dynamic contrast-enhanced breast images. METHODS:The mutual information similarity measure with free-form deformation motion correction in rapidly enhancing lesions can introduce motion. However, in Dixon-based fat-water separated acquisitions, the nonenhancing fat signal can directly be used to estimate deformable motion, which can be later used to deform the water images. Qualitative comparison of the fat-based registration method to a water-based registration method, and to the unregistered images, was performed by two experienced readers. Quantitative analysis of the registration was evaluated by estimating the mean-squared signal difference on the fat images. RESULTS:Using a scale of 0 (no motion) to 2 (?>?4 voxels of motion), the average image quality score of the fat-based registered images was 0.5?±?0.6, water-based registration was 0.8?±?0.8, and the unregistered dataset was 1.6?±?0.6. The mean-squared-signal-difference metric on the fat images was significantly lower for fat-based registered images compared with both water-based registered and unregistered images. CONCLUSIONS:Fat-based registration of breast dynamic contrast-enhanced images is a promising technique for performing deformable motion correction of breast without introducing new motion. Magn Reson Med 79:2408-2414, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Srinivasan S

PROVIDER: S-EPMC6063085 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Fat-based registration of breast dynamic contrast enhanced water images.

Srinivasan Subashini S Hargreaves Brian A BA Daniel Bruce L BL

Magnetic resonance in medicine 20170726 4

<h4>Purpose</h4>In this study, a 3D fat-based deformable registration algorithm was developed for registering dynamic contrast-enhanced breast images.<h4>Methods</h4>The mutual information similarity measure with free-form deformation motion correction in rapidly enhancing lesions can introduce motion. However, in Dixon-based fat-water separated acquisitions, the nonenhancing fat signal can directly be used to estimate deformable motion, which can be later used to deform the water images. Qualit ...[more]

PMID: 28745402

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Project description:PurposeTo assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions.Materials and methodsIn this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were first selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classification performance was assessed with receiver operating characteristic analysis.ResultsDifferentiation of DCIS from IDC lesions yielded an area under the receiver operating characteristic curve (AUC) of 0.83 +/- 0.03 (standard error). AUCs were 0.85 +/- 0.02 for differentiation between IDC and benign lesions and 0.79 +/- 0.03 for differentiation between DCIS and benign lesions. Differentiation between IDC lesions associated with positive LNs and IDC lesions associated with negative LNs yielded an AUC of 0.82 +/- 0.04. AUCs were 0.86 +/- 0.03 for differentiation between IDC lesions associated with positive LNs and benign lesions and 0.83 +/- 0.03 for differentiation between IDC lesions associated with negative LNs and benign lesions.ConclusionComputer-aided diagnosis of breast DCE MR imaging-depicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.Supplemental materialhttp://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.09090838/-/DC1.

Project description:PurposeRenal dynamic contrast-enhanced (DCE) MRI provides information on renal perfusion and filtration. However, clinical implementation is hampered by challenges in postprocessing as a result of misalignment of the kidneys due to respiration. We propose to perform automated image registration using the fat-only images derived from a modified Dixon reconstruction of a dual-echo acquisition because these provide consistent contrast over the dynamic series.MethodsDCE data of 10 hypertensive patients was used. Dual-echo images were acquired at 1.5 T with temporal resolution of 3.9 s during contrast agent injection. Dixon fat, water, and in-phase and opposed-phase (OP) images were reconstructed. Postprocessing was automated. Registration was performed both to fat images and OP images for comparison. Perfusion and filtration values were extracted from a two-compartment model fit.ResultsAutomatic registration to fat images performed better than automatic registration to OP images with visible contrast enhancement. Median vertical misalignment of the kidneys was 14 mm prior to registration, compared to 3 mm and 5 mm with registration to fat images and OP images, respectively (P = 0.03). Mean perfusion values and MR-based glomerular filtration rates (GFR) were 233 ± 64 mL/100 mL/min and 60 ± 36 mL/minute, respectively, based on fat-registered images. MR-based GFR correlated with creatinine-based GFR (P = 0.04) for fat-registered images. For unregistered and OP-registered images, this correlation was not significant.ConclusionAbsence of contrast changes on Dixon fat images improves registration in renal DCE MRI and enables automated postprocessing, resulting in a more accurate estimation of GFR. Magn Reson Med 80:66-76, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Project description:To propose a method to quantify T1 and contrast agent uptake in breast dynamic contrast-enhanced (DCE) examinations undertaken with standard clinical fat-suppressed MRI sequences and to demonstrate the proposed approach by comparing the enhancement characteristics of lobular and ductal carcinomas.A standard fat-suppressed DCE of the breast was performed at 1.5 T (Siemens Aera), followed by the acquisition of a proton density (PD)-weighted sequence, also fat suppressed. Both sequences were characterized with test objects (T1 ranging from 30 ms to 2,400 ms) and calibration curves were obtained to enable T1 calculation. The reproducibility and accuracy of the calibration curves were also investigated. Healthy volunteers and patients were scanned with Ethics Committee approval. The effect of B0 field inhomogeneity was assessed in test objects and healthy volunteers. The T1 of breast tumors was calculated at different time points (pre-, peak-, and post-contrast agent administration) for 20 patients, pre-treatment (10 lobular and 10 ductal carcinomas) and the two cancer types were compared (Wilcoxon rank-sum test).The calibration curves proved to be highly reproducible (coefficient of variation under 10%). T1 measurements were affected by B0 field inhomogeneity, but frequency shifts below 50 Hz introduced only 3% change to fat-suppressed T1 measurements of breast parenchyma in volunteers. The values of T1 measured pre-, peak-, and post-contrast agent administration demonstrated that the dynamic range of the DCE sequence was correct, that is, image intensity is approximately directly proportional to 1/T1 for that range. Significant differences were identified in the width of the distributions of the post-contrast T1 values between lobular and ductal carcinomas (P < 0.05); lobular carcinomas demonstrated a wider range of post-contrast T1 values, potentially related to their infiltrative growth pattern.This work has demonstrated the feasibility of fat-suppressed T1 measurements as a tool for clinical studies. The proposed quantitative approach is practical, enabled the detection of differences between lobular and invasive ductal carcinomas, and further enables the optimization of DCE protocols by tailoring the dynamic range of the sequence to the values of T1 measured.

Project description:BACKGROUND:Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. METHODS:Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4-6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7-4.6?s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values <?0.05 were considered statistically significant. RESULTS:Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P?<?0.001 and P?=?0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1-2 (P?=?0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P?=?0.002), and triple negative or HER2 type vs. luminal type (P?<?0.001). CONCLUSIONS:Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

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Fat-based registration of breast dynamic contrast enhanced water images.

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Fat-based registration of breast dynamic contrast enhanced water images.

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