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Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

ABSTRACT: Quantification of breast density based on three-dimensional breast MRI may provide useful information for the early detection of breast cancer. However, the field inhomogeneity can severely challenge the computerized image segmentation process. In this work, the effect of the bias field in breast density quantification has been investigated with a postmortem study.T1-weighted images of 20 pairs of postmortem breasts were acquired on a 1.5 T breast MRI scanner. Two computer-assisted algorithms were used to quantify the volumetric breast density. First, standard fuzzy c-means (FCM) clustering was used on raw images with the bias field present. Then, the coherent local intensity clustering (CLIC) method estimated and corrected the bias field during the iterative tissue segmentation process. Finally, FCM clustering was performed on the bias-field-corrected images produced by CLIC method. The left-right correlation for breasts in the same pair was studied for both segmentation algorithms to evaluate the precision of the tissue classification. Finally, the breast densities measured with the three methods were compared to the gold standard tissue compositions obtained from chemical analysis. The linear correlation coefficient, Pearson's r, was used to evaluate the two image segmentation algorithms and the effect of bias field.The CLIC method successfully corrected the intensity inhomogeneity induced by the bias field. In left-right comparisons, the CLIC method significantly improved the slope and the correlation coefficient of the linear fitting for the glandular volume estimation. The left-right breast density correlation was also increased from 0.93 to 0.98. When compared with the percent fibroglandular volume (%FGV) from chemical analysis, results after bias field correction from both the CLIC the FCM algorithms showed improved linear correlation. As a result, the Pearson's r increased from 0.86 to 0.92 with the bias field correction.The investigated CLIC method significantly increased the precision and accuracy of breast density quantification using breast MRI images by effectively correcting the bias field. It is expected that a fully automated computerized algorithm for breast density quantification may have great potential in clinical MRI applications.

SUBMITTER: Ding H

PROVIDER: S-EPMC3862600 | biostudies-other | 2013 Dec

REPOSITORIES: biostudies-other

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Project description:BackgroundPostmortem magnetic resonance imaging (MRI) has been used to investigate the cause of death, but due to time constraints, it is not widely applied to the heart. Therefore, MRI analysis of the heart after formalin fixation was previously performed. However, the changes in MRI signal values based on the fixation time of formalin were not investigated. The objective was to investigate changes over time in the T1- and T2-values of MRI signals in normal areas of hearts removed during autopsy, hearts subsequently fixed in formalin, and heart specimens sliced for the preparation of pathological specimens.MethodsThe study subjects were 21 autopsy cases in our hospital between May 26, 2019 and February 16, 2020 whose hearts were removed and scanned by MRI. The male:female ratio was 14:7, and their ages at death ranged from 9 to 92 years (mean age 65.0 ± 19.7 years). Postmortem (PM)-MRI was conducted with a 0.3-Tesla (0.3-T) scanner containing a permanent magnet. A 4-channel QD head coil was used as the receiver coil. Scans were performed immediately after removal, post-formalin fixation, and after slicing; 7 cases were scanned at all three time points.ResultsThe T1- and T2-values were calculated from the MRI signals of each sample organ at each scanning stage. Specimens were sliced from removed organs after formalin fixation, and the changes in T1- and T2-values over time were graphed to obtain an approximate curve. The median T1-values at each measurement time point tended to decrease from immediately after removal. The T2-values showed the same tendency to decrease, but this tendency was more pronounced for the T1-values.ConclusionMRI signal changes in images of heart specimens were investigated. Formalin fixation shortened both T1- and T2-values over time, and approximation formulae were derived to show these decreases over time. The shortening of T1- and T2-values can be understood as commensurate with the reduction in the water content (water molecules) of the formalin-fixed heart.

Project description:In radiotherapy (RT) of prostate cancer, dose escalation has been shown to reduce biochemical failure. Dose escalation only to determinate prostate tumor habitats has the potential to improve tumor control with less toxicity than when the entire prostate is dose escalated. Other issues in the treatment of the RT patient include the choice of the RT technique (hypo- or standard fractionation) and the use and length of concurrent/adjuvant androgen deprivation therapy (ADT). Up to 50% of high-risk men demonstrate biochemical failure suggesting that additional strategies for defining and treating patients based on improved risk stratification are required. The use of multiparametric MRI (mpMRI) is rapidly gaining momentum in the management of prostate cancer because of its improved diagnostic potential and its ability to combine functional and anatomical information. Currently, the Prostate Imaging, Reporting and Diagnosis System (PIRADS) is the standard of care for region of interest (ROI) identification and risk classification. However, PIRADS was not designed for 3D tumor volume delineation; there is a large degree of subjectivity and PIRADS does not accurately and reproducibly elucidate inter- and intra-lesional spatial heterogeneity. "Radiomics", as it refers to the extraction and analysis of large number of advanced quantitative radiological features from medical images using high throughput methods, is perfectly suited as an engine to effectively sift through the multiple series of prostate mpMRI sequences and quantify regions of interest. The radiomic efforts can be summarized in two main areas: (I) detection/segmentation of the suspicious lesion; and (II) assessment of the aggressiveness of prostate cancer. As related to RT, the goal of the latter is in particular to identify patients at high risk for metastatic disease; and the aim of the former is to identify and segment cancerous lesions and thus provide targets for radiation boost. The article is structured as follows: first, we describe the radiomic approach; and second, we discuss the radiomic pipeline as tailored for RT of prostate cancer. In this process we summarize the current efforts and progress in integrating mpMRI radiomics into the radiotherapeutic management of prostate cancer with emphasis placed on its role in treatment target definition, treatment plan strategizing, and prognostic assessment. The described concepts, methods and tools are not currently applicable to the radiation oncology practice outside of the research setting. More data are required in the form of clinical trials to assess the robustness of radiomics-based predictive models, and to maximize the efficacy of these models.

Project description:BACKGROUND:A low excitation flip angle (??<?10°) steady-state free precession (SSFP) proton-density (PD) reference scan is often used to estimate the B1-field inhomogeneity for surface coil intensity correction (SCIC) of the saturation-recovery (SR) prepared high flip angle (??=?40-50°) SSFP myocardial perfusion images. The different SSFP off-resonance response for these two flip angles might lead to suboptimal SCIC when there is a spatial variation in the background B0-field. The low flip angle SSFP-PD frames are more prone to parallel imaging banding artifacts in the presence of off-resonance. The use of FLASH-PD frames would eliminate both the banding artifacts and the uneven frequency response in the presence of off-resonance in the surface coil inhomogeneity estimate and improve homogeneity of semi-quantitative and quantitative perfusion measurements. METHODS:B0-field maps, SSFP and FLASH-PD frames were acquired in 10 healthy volunteers to analyze the SSFP off-resonance response. Furthermore, perfusion scans preceded by both FLASH and SSFP-PD frames from 10 patients with no myocardial infarction were analyzed semi-quantitatively and quantitatively (rest n?=?10 and stress n?=?1). Intra-subject myocardial blood flow (MBF) coefficient of variation (CoV) over the whole left ventricle (LV), as well as intra-subject peak contrast (CE) and upslope (SLP) standard deviation (SD) over 6 LV sectors were investigated. RESULTS:In the 6 out of 10 cases where artifacts were apparent in the LV ROI of the SSFP-PD images, all three variability metrics were statistically significantly lower when using the FLASH-PD frames as input for the SCIC (CoVMBF-FLASH?=?0.3?±?0.1, CoVMBF-SSFP?=?0.4?±?0.1, p?=?0.03; SDCE-FLASH?=?10?±?2, SDCE-SSFP?=?32?±?7, p?=?0.01; SDSLP-FLASH?=?0.02?±?0.01, SDSLP-SSFP?=?0.06?±?0.02, p?=?0.03). Example rest and stress data sets from the patient pool demonstrate that the low flip angle SSFP protocol can exhibit severe ghosting artifacts originating from off-resonance banding artifacts at the edges of the field of view that parallel imaging is not able to unfold. These artifacts lead to errors in the quantitative perfusion maps and the semi-quantitative perfusion indexes, such as false positives. It is shown that this can be avoided by using FLASH-PD frames as input for the SCIC. CONCLUSIONS:FLASH-PD images are recommended as input for SCIC of SSFP perfusion images instead of low flip angle SSFP-PD images.

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Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study.

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