The EEG Oscillations and Psychology Propensities of Autonomous Sensory Meridian Response.

Autonomous sensory meridian response is believed as a perceptual phenomenon to specific sensory stimuli. To explore the underlying mechanism and emotional effect, the EEG under video and audio triggers of autonomous sensory meridian response was analyzed. The differential entropy and power spectral density by Burg method on δ, θ, α, ß, γ and high γ frequencies were employed as quantitative features. The results indicate that the modulation of autonomous sensory meridian response on brain activities is broadband. Video trigger owns better performance of autonomous sensory meridian response than other triggers. Moreover, the results also reveal that autonomous sensory meridian response has a close relationship with neuroticism and its three sub-dimensions, anxiety, self-consciousness and vulnerability, with the scores of self-rating depression scale, but without emotions, happiness, sadness, or fear. This suggests that the responders of autonomous sensory meridian response may have the tendencies of neuroticism and depressive disorder.

Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration.

Registration and fusion of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland can provide high-quality guidance for prostate interventions. However, accurate MR-TRUS registration remains a challenging task, due to the great intensity variation between two modalities, the lack of intrinsic fiducials within the prostate, the large gland deformation caused by the TRUS probe insertion, and distinctive biomechanical properties in patients and prostate zones. To address these challenges, a personalized model-to-surface registration approach is proposed in this study. The main contributions of this paper can be threefold. First, a new personalized statistical deformable model (PSDM) is proposed with the finite element analysis and the patient-specific tissue parameters measured from the ultrasound elastography. Second, a hybrid point matching method is developed by introducing the modality independent neighborhood descriptor (MIND) to weight the Euclidean distance between points to establish reliable surface point correspondence. Third, the hybrid point matching is further guided by the PSDM for more physically plausible deformation estimation. Eighteen sets of patient data are included to test the efficacy of the proposed method. The experimental results demonstrate that our approach provides more accurate and robust MR-TRUS registration than state-of-the-art methods do. The averaged target registration error is 1.44 mm, which meets the clinical requirement of 1.9 mm for the accurate tumor volume detection. It can be concluded that the presented method can effectively fuse the heterogeneous image information in the elastography, MR, and TRUS to attain satisfactory image alignment performance.

Luteolin, ellagic acid and punicic acid are natural products that inhibit prostate cancer metastasis.

Prostate cancer (PCa) is the second cause of cancer deaths in men in the USA. When the cancer recurs, early stages can be controlled with hormone ablation therapy to delay the rate of cancer progression but, over time, the cancer overcomes its hormone dependence, becomes highly aggressive and metastasizes. Clinical trials have shown that pomegranate juice (PJ) inhibits PCa progression. We have previously shown that the PJ components luteolin (L), ellagic acid (E) and punicic acid (P) together inhibit growth of hormone-dependent and -independent PCa cells and inhibit their migration and chemotaxis towards CXCL12, a chemokine that is important in PCa metastasis. On the basis of these findings, we hypothesized that L+E+P inhibit PCa metastasis in vivo. To test this possibility, we used a severe combined immunodeficiency mouse model in which luciferase-expressing human PCa cells were injected subcutaneously near the prostate. Tumor progression was monitored with bioluminescence imaging weekly. We found that L+E+P inhibits PC-3M-luc primary tumor growth, inhibits the CXCL12/CXCR4 axis for metastasis and none of the tumors metastasized. In addition, L+E+P significantly inhibits growth and metastasis of highly invasive Pten (-/-) ;K-ras (G12D) prostate tumors. Furthermore, L+E+P inhibits angiogenesis in vivo, prevents human endothelial cell (EC) tube formation in culture and disrupts preformed EC tubes, indicating inhibition of EC adhesion to each other. L+E+P also inhibits the angiogenic factors interleukin-8 and vascular endothelial growth factor as well as their induced signaling pathways in ECs. In conclusion, these results show that L+E+P inhibits PCa progression and metastasis.

Template-based automatic breast segmentation on MRI by excluding the chest region.

PURPOSE: Methods for quantification of breast density on MRI using semiautomatic approaches are commonly used. In this study, the authors report on a fully automatic chest template-based method. METHODS: Nonfat-suppressed breast MR images from 31 healthy women were analyzed. Among them, one case was randomly selected and used as the template, and the remaining 30 cases were used for testing. Unlike most model-based breast segmentation methods that use the breast region as the template, the chest body region on a middle slice was used as the template. Within the chest template, three body landmarks (thoracic spine and bilateral boundary of the pectoral muscle) were identified for performing the initial V-shape cut to determine the posterior lateral boundary of the breast. The chest template was mapped to each subject's image space to obtain a subject-specific chest model for exclusion. On the remaining image, the chest wall muscle was identified and excluded to obtain clean breast segmentation. The chest and muscle boundaries determined on the middle slice were used as the reference for the segmentation of adjacent slices, and the process continued superiorly and inferiorly until all 3D slices were segmented. The segmentation results were evaluated by an experienced radiologist to mark voxels that were wrongly included or excluded for error analysis. RESULTS: The breast volumes measured by the proposed algorithm were very close to the radiologist's corrected volumes, showing a % difference ranging from 0.01% to 3.04% in 30 tested subjects with a mean of 0.86% ± 0.72%. The total error was calculated by adding the inclusion and the exclusion errors (so they did not cancel each other out), which ranged from 0.05% to 6.75% with a mean of 3.05% ± 1.93%. The fibroglandular tissue segmented within the breast region determined by the algorithm and the radiologist were also very close, showing a % difference ranging from 0.02% to 2.52% with a mean of 1.03% ± 1.03%. The total error by adding the inclusion and exclusion errors ranged from 0.16% to 11.8%, with a mean of 2.89% ± 2.55%. CONCLUSIONS: The automatic chest template-based breast MRI segmentation method worked well for cases with different body and breast shapes and different density patterns. Compared to the radiologist-established truth, the mean difference in segmented breast volume was approximately 1%, and the total error by considering the additive inclusion and exclusion errors was approximately 3%. This method may provide a reliable tool for MRI-based segmentation of breast density.

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

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Background parenchymal enhancement in the contralateral normal breast of patients undergoing neoadjuvant chemotherapy measured by DCE-MRI.

The purpose of this study was to analyze background parenchymal enhancement (BPE) in the contralateral normal breast of cancer patients during the course of neoadjuvant chemotherapy (NAC). Forty-five subjects were analyzed. Each patient had three MRIs, one baseline (B/L) and two follow-up (F/U) studies. The fibroglandular tissue in the contralateral normal breast was segmented using a computer-assisted algorithm. Based on the segmented fibroglandular tissue, BPE was calculated. BPE measured in baseline (B/L) and follow-up (F/U) MR studies were compared. The baseline BPE was also correlated with age and compared between pre/peri-menopausal (<55 years old) and post-menopausal women (≥55 years old). The pre-treatment BPE measured in B/L MRI was significantly higher in women <55 years old than in women ≥55 years old (20.1%±7.4% vs. 12.1%±5.1%, p≤0.01). A trend of negative correlation between BPE and age was noted (r=-0.29). In women <55years old, BPE at F/U-1 (18.8%±6.9%) was decreased compared to B/L, and was further decreased in F/U-2 (13.3%±5.7%) which was significant compared to B/L and F/U-1. In women ≥55 years old, no significant difference was noted in any paired comparison among B/L, F/U-1 and F/U-2 MRI. A higher baseline BPE was associated with a greater reduction of BPE in F/U-2 MRI (r=0.73). Our study showed that younger women tended to have higher BPE than older women. BPE was significantly decreased in F/U-2 MRI after NAC in women <55 years old. The reduction in BPE was most likely due to the ovarian ablation induced by chemotherapeutic agents.

Developmental changes in hippocampal shape among preadolescent children.

It is known that the largest developmental changes in the hippocampus take place during the prenatal period and during the first two years of postnatal life. Few studies have been conducted to address the normal developmental trajectory of the hippocampus during childhood. In this study shape analysis was applied to study the normal developing hippocampus in a group of 103 typically developing 6- to 10-year-old preadolescent children. The individual brain was normalized to a template, and then the hippocampus was manually segmented and further divided into the head, body, and tail sub-regions. Three different methods were applied for hippocampal shape analysis: radial distance mapping, surface-based template registration using the robust point matching (RPM) algorithm, and volume-based template registration using the Demons algorithm. All three methods show that the older children have bilateral expanded head segments compared to the younger children. The results analyzed based on radial distance to the centerline were consistent with those analyzed using template-based registration methods. In analyses stratified by sex, it was found that the age-associated anatomical changes were similar in boys and girls, but the age-association was strongest in girls. Total hippocampal volume and sub-regional volumes analyzed using manual segmentation did not show a significant age-association. Our results suggest that shape analysis is sensitive to detect sub-regional differences that are not revealed in volumetric analysis. The three methods presented in this study may be applied in future studies to investigate the normal developmental trajectory of the hippocampus in children. They may be further applied to detect early deviations from the normal developmental trajectory in young children for evaluating susceptibility for psychopathological disorders involving hippocampus.

Differentiation of myeloma and metastatic cancer in the spine using dynamic contrast-enhanced MRI.

Spinal myeloma and metastatic cancer cause similar symptoms and show similar imaging presentations, thus making them difficult to differentiate. In this study, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed to differentiate between 9 myelomas and 22 metastatic cancers that present as focal lesions in the spine. The characteristic DCE parameters, including the peak signal enhancement percentage (SE%), the steepest wash-in SE% during the ascending phase and the wash-out SE%, were calculated by normalizing to the precontrast signal intensity. The two-compartmental pharmacokinetic model was used to obtain K(trans) and kep. All nine myelomas showed the wash-out DCE pattern. Of the 22 metastatic cancers, 12 showed wash-out, 7 showed plateau, and 3 showed persistent enhancing patterns. The fraction of cases that showed the wash-out pattern was significantly higher in the myeloma group than the metastatic cancer group (9/9=100% vs. 12/22=55%, P=.03). Compared to the metastatic cancer group, the myeloma group had a higher peak SE% (226%±72% vs. 165%±60%, P=.044), a higher steepest wash-in SE% (169%±51% vs. 111%±41%, P=.01), a higher K(trans) (0.114±0.036 vs. 0.077±0.0281/min, P=.016) and a higher kep (0.88±0.26 vs. 0.49±0.23 1/min, P=.002). The receiver operating characteristic analysis to differentiate between these two groups showed that the area under the curve was 0.798 for K(trans), 0.864 for kep and 0.919 for combined K(trans) and kep. These results show that DCE-MRI may provide additional information for making differential diagnosis to aid in choosing the optimal subsequent procedures or treatments for spinal lesions.

Response of bilateral breasts to the endogenous hormonal fluctuation in a menstrual cycle evaluated using 3D MRI.

The normal breast tissue responds to the fluctuation of endogenous hormones during a menstrual cycle (MC) and shows changes in breast density. The changes between left and right breasts of the same women were compared to evaluate the symmetrical response. Twenty-four healthy women were recruited in this study. Four weekly magnetic resonance imaging (MRI) studies were performed during one MC. A computer algorithm was used to segment the breast and the fibroglandular tissue to measure the fibroglandular tissue volume (FV) and three morphological parameters: circularity, convexity and irregularity. The coefficient of variation (CV) for each parameter measured among four MRI studies was calculated; also, the maximal percent change between two MRI studies that show the highest and the lowest FV was calculated. These parameters measured from left and right breasts were compared using Pearson correlation. For the FV, the CV measured between left and right breasts of 24 subjects was highly correlated, with r=0.91; the maximal percent difference was also highly correlated, with r=0.93. Overall, the mean left-to-right difference in the measured FV was small: 1.2%±1.1% for CV and 2.6%±2.3% for the maximal percent difference. For the three morphological parameters, the mean left-to-right percentage difference was similar to the differences seen in FV; however, these morphological parameters do not reveal a high functional symmetry between left and right breasts. The results showed that the measured FV from left and right breasts of the same woman revealed a high functional symmetry. Since endogenous hormone plays an important role in the development of breast cancer, it would be interesting to investigate whether the functional asymmetry of response in some patients is associated with the risk of developing unilateral breast cancer.

Consistency of breast density measured from the same women in four different MR scanners.

PURPOSE: To compare the breast volume (BV), fibroglandular tissue volume (FV), and percent density (PD) measured from breast MRI of the same women using four different MR scanners. METHODS: The study was performed in 34 healthy Asian volunteers using two 1.5T (GE and Siemens) and two 3T (GE and Philips) MR scanners. The BV, FV, and PD were measured on nonfat-suppressed T1-weighted images using a comprehensive computer algorithm-based segmentation method. The scanner-to-scanner measurement difference, and the coefficient of variation (CV) among the four scanners were calculated. The measurement variation between two density morphological patterns presenting as the central type and the intermingled type was separately analyzed and compared. RESULTS: All four scanners provided satisfactory image quality allowing for successful completion of the segmentation processes. The measured parameters between each pair of MR scanners were highly correlated, with R(2) ≥ 0.95 for BV, R(2) ≥ 0.99 for FV, and R(2) ≥ 0.97 for PD in all comparisons. The mean percent differences between each pair of scanners were 5.9%-7.8% for BV, 5.3%-6.5% for FV, 4.3%-7.3% for PD; with the overall CV of 5.8% for BV, 4.8% for FV, and 4.9% for PD. The variation of FV was smaller in the central type than in the intermingled type (p = 0.04). CONCLUSIONS: The results showed that the variation of FV and PD measured from four different MR scanners is around 5%, suggesting the parameters measured using different scanners can be used for a combined analysis in a multicenter study.

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

PURPOSE: To investigate the difference of MR percent breast density measured from fat-suppressed versus nonfat-suppressed imaging sequences. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Breast cancer: evaluation of response to neoadjuvant chemotherapy with 3.0-T MR imaging.

PURPOSE: To assess how the molecular biomarker status of a breast cancer, including human epidermal growth factor receptor 2 (HER2), hormone receptors, and the proliferation marker Ki-67 status, affects the diagnosis at 3.0-T magnetic resonance (MR) imaging. MATERIALS AND METHODS: This study was approved by the institutional review board and was HIPAA compliant. Fifty patients (age range, 28-82 years; mean age, 49 years) receiving neoadjuvant chemotherapy were monitored with 3.0-T MR imaging. The longest dimension of the residual cancer was measured at MR imaging and correlated with pathologic findings. Patients were further divided into subgroups on the basis of HER2, hormone receptor, and Ki-67 status. Pathologic complete response (pCR) was defined as when there were no residual invasive cancer cells. The Pearson correlation was used to correlate MR imaging-determined and pathologic tumor size, and the unpaired t test was used to compare MR imaging-pathologic size discrepancies. RESULTS: Of the 50 women, 14 achieved pCR. There were seven false-negative diagnoses at MR imaging. The overall sensitivity, specificity, and accuracy for diagnosing invasive residual disease at MR imaging were 81%, 93%, and 84%, respectively. The mean MR imaging-pathologic size discrepancy was 0.5 cm ± 0.9 (standard deviation) for HER2-positive cancer and 2.3 cm ± 3.5 for HER2-negative cancer (P = .009). In the HER2-negative group, the size discrepancy was smaller for hormone receptor-negative than for hormone receptor-positive cancers (1.0 cm ± 1.1 vs 3.0 cm ± 4.0, P = .04). The size discrepancy was smaller in patients with 40% or greater Ki-67 expression (0.8 cm ± 1.1) than in patients with 10% or less Ki-67 expression (3.9 cm ± 5.1, P = .06). CONCLUSION: The diagnostic accuracy of breast MR imaging is better in more aggressive than in less aggressive cancers. When MR imaging is used for surgical planning, caution should be taken with HER2-negative hormone receptor-positive cancers.

Menstrual cycle-related fluctuations in breast density measured by using three-dimensional MR imaging.

PURPOSE: To investigate the fluctuation of fibroglandular tissue volume (FV) and percentage of breast density (PD) during the menstrual cycle and compare with postmenopausal women by using three-dimensional magnetic resonance (MR)-based segmentation methods. MATERIALS AND METHODS: This study was approved by the Institutional Review Board and was HIPAA compliant. Written informed consent was obtained. Thirty healthy female subjects, 24 premenopausal and six postmenopausal, were recruited. All subjects underwent MR imaging examination each week for 4 consecutive weeks. The breast volume (BV), FV, and PD were measured by two operators to evaluate interoperator variation. The fluctuation of each parameter measured over the course of the four examinations was evaluated on the basis of the coefficient of variation (CV). RESULTS: The results from two operators showed a high Pearson correlation for BV (R(2) = 0.99), FV (R(2) = 0.98), and PD (R(2) = 0.96). The interoperator variation was 3% for BV and around 5%-6% for FV and PD. In the respective premenopausal and postmenopausal groups, the mean CV was 5.0% and 5.6% for BV, 7.6% and 4.2% for FV, and 7.1% and 6.0% for PD. The difference between premenopausal and postmenopausal groups was not significant (all P values > .05). CONCLUSION: The fluctuation of breast density measured at MR imaging during a menstrual cycle was around 7%. The results may help the design and interpretation of future studies by using the change of breast density as a surrogate marker to evaluate the efficacy of hormone-modifying drugs for cancer treatment or cancer prevention.

Spatial shrinkage/expansion patterns between breast density measured in two MRI scans evaluated by non-rigid registration.

Breast MRI acquires many images from the breast, and computer-aided algorithms and display tools are often used to assist the radiologist's interpretation. Women with lifetime risk greater than 20% of developing breast cancer are recommended to receive annual screening MRI, but the current breast MRI computer-aided-diagnosis systems do not provide the necessary function for comparison of images acquired at different times. The purpose of this work was to develop registration methods for evaluating the spatial change pattern of fibroglandular tissue between two breast MRI scans of the same woman taken at different times. The registration method is based on rigid alignment followed by a non-rigid Demons algorithm. The method was tested on three different subjects who had different degrees of changes in the fibroglandular tissue, including two patients who showed different spatial shrinkage patterns after receiving neoadjuvant chemotherapy before surgery, and one control case from a normal volunteer. Based on the transformation matrix, the collapse of multiple voxels on the baseline images to one voxel on the follow-up images is used to calculate the shrinkage factor. Conversely, based on the reverse transformation matrix the expansion factor can be calculated. The shrinkage/expansion factor, the deformation magnitude and direction, as well as the Jacobian determinate at each location can be displayed in a 3D rendering view to show the spatial changes between two MRI scans. These different parameters show consistent results and can be used for quantitative evaluation of the spatial change patterns. The presented registration method can be further developed into a clinical tool for evaluating therapy-induced changes and for early diagnosis of breast cancer in screening MRI.

A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI.

PURPOSE: Quantitative breast density is known as a strong risk factor associated with the development of breast cancer. Measurement of breast density based on three-dimensional breast MRI may provide very useful information. One important step for quantitative analysis of breast density on MRI is the correction of field inhomogeneity to allow an accurate segmentation of the fibroglandular tissue (dense tissue). A new bias field correction method by combining the nonparametric nonuniformity normalization (N3) algorithm and fuzzy-C-means (FCM)-based inhomogeneity correction algorithm is developed in this work. METHODS: The analysis is performed on non-fat-sat T1-weighted images acquired using a 1.5 T MRI scanner. A total of 60 breasts from 30 healthy volunteers was analyzed. N3 is known as a robust correction method, but it cannot correct a strong bias field on a large area. FCM-based algorithm can correct the bias field on a large area, but it may change the tissue contrast and affect the segmentation quality. The proposed algorithm applies N3 first, followed by FCM, and then the generated bias field is smoothed using Gaussian kernal and B-spline surface fitting to minimize the problem of mistakenly changed tissue contrast. The segmentation results based on the N3+FCM corrected images were compared to the N3 and FCM alone corrected images and another method, coherent local intensity clustering (CLIC), corrected images. The segmentation quality based on different correction methods were evaluated by a radiologist and ranked. RESULTS: The authors demonstrated that the iterative N3+FCM correction method brightens the signal intensity of fatty tissues and that separates the histogram peaks between the fibroglandular and fatty tissues to allow an accurate segmentation between them. In the first reading session, the radiologist found (N3+FCM > N3 > FCM) ranking in 17 breasts, (N3+FCM > N3 = FCM) ranking in 7 breasts, (N3+FCM = N3 > FCM) in 32 breasts, (N3+FCM = N3 = FCM) in 2 breasts, and (N3 > N3+FCM > FCM) in 2 breasts. The results of the second reading session were similar. The performance in each pairwise Wilcoxon signed-rank test is significant, showing N3+FCM superior to both N3 and FCM, and N3 superior to FCM. The performance of the new N3+FCM algorithm was comparable to that of CLIC, showing equivalent quality in 57/60 breasts. CONCLUSIONS: Choosing an appropriate bias field correction method is a very important preprocessing step to allow an accurate segmentation of fibroglandular tissues based on breast MRI for quantitative measurement of breast density. The proposed algorithm combining N3+FCM and CLIC both yield satisfactory results.

Characterization of Pure Ductal Carcinoma In Situ on Dynamic Contrast-Enhanced MR Imaging: Do Nonhigh Grade and High Grade Show Different Imaging Features?

To characterize imaging features of pure DCIS on dynamic contrast-enhanced MR imaging (DCE-MRI), 31 consecutive patients (37-81 years old, mean 56), including 2 Grade I, 16 Grade II, and 13 Grade III, were studied. MR images were reviewed retrospectively and the morphological appearances and kinetic features of breast lesions were categorized according to the ACR BI-RADS breast MRI lexicon. DCE-MRI was a sensitive imaging modality in detecting pure DCIS. MR imaging showed enhancing lesions in 29/31 (94%) cases. Pure DCIS appeared as mass type or non-mass lesions on MRI with nearly equal frequency. The 29 MR detected lesions include 15 mass lesions (52%), and 14 lesions showing non-mass-like lesions (48%). For the mass lesions, the most frequent presentations were irregular shape (50%), irregular margin (50%) and heterogeneous enhancement (67%). For the non-mass-like lesions, the clumped internal enhancement pattern was the dominate feature, seen in 9/14 cases (64%). Regarding enhancement kinetic curve, 21/29 (78%) lesions showed suspicious malignant type kinetics. No significant difference was found in morphology (P > .05), tumor size (P = 0.21), and kinetic characteristics (P = .38) between non-high grade (I+II) and high-grade (III) pure DCIS.

Computational simulation of breast compression based on segmented breast and fibroglandular tissues on magnetic resonance images.

This study presents a finite element-based computational model to simulate the three-dimensional deformation of a breast and fibroglandular tissues under compression. The simulation was based on 3D MR images of the breast, and craniocaudal and mediolateral oblique compression, as used in mammography, was applied. The geometry of the whole breast and the segmented fibroglandular tissues within the breast were reconstructed using triangular meshes by using the Avizo 6.0 software package. Due to the large deformation in breast compression, a finite element model was used to simulate the nonlinear elastic tissue deformation under compression, using the MSC.Marc software package. The model was tested in four cases. The results showed a higher displacement along the compression direction compared to the other two directions. The compressed breast thickness in these four cases at a compression ratio of 60% was in the range of 5-7 cm, which is a typical range of thickness in mammography. The projection of the fibroglandular tissue mesh at a compression ratio of 60% was compared to the corresponding mammograms of two women, and they demonstrated spatially matched distributions. However, since the compression was based on magnetic resonance imaging (MRI), which has much coarser spatial resolution than the in-plane resolution of mammography, this method is unlikely to generate a synthetic mammogram close to the clinical quality. Whether this model may be used to understand the technical factors that may impact the variations in breast density needs further investigation. Since this method can be applied to simulate compression of the breast at different views and different compression levels, another possible application is to provide a tool for comparing breast images acquired using different imaging modalities--such as MRI, mammography, whole breast ultrasound and molecular imaging--that are performed using different body positions and under different compression conditions.

Decrease in breast density in the contralateral normal breast of patients receiving neoadjuvant chemotherapy: MR imaging evaluation.

PURPOSE: To investigate the change of breast density with quantitative magnetic resonance (MR) imaging in the contralateral normal breast of patients receiving neoadjuvant chemotherapy. MATERIALS AND METHODS: This study was approved by the institutional review board and was HIPAA compliant. Informed consent was obtained. Fifty-four patients with breast cancer (mean age, 47 years; age range, 30-74 years) treated with NAC protocol and enrolled in a breast MR imaging research study were studied. The density in the contralateral normal breast was analyzed by using an MR imaging-based segmentation method. The effect of chemotherapy on the change of density following the doxorubicin and cyclophosphamide (AC) and the AC and taxane regimen was evaluated. The dependence on age was investigated by using a multivariate regression model. RESULTS: In patients who underwent both AC and taxane follow-up, the mean percentage of change from the individual's baseline density was -10% (95% confidence interval: -12.8%, -7.2%) after AC and -12.7% (95% confidence interval: -16%, -9.4%) after AC and taxane. In patients who underwent both follow-up studies after one to two and four cycles of AC, the mean percentage of change was -9.4% (95% confidence interval: -13.5%, -5.3%) after one to two cycles of AC and -14.7% (95% confidence interval: -20.6%, -8.7%) after four cycles of AC. The percentage reduction of density was significantly dependent on age. Patients younger than 40 years had a greater reduction after chemotherapy than patients older than 55 years (P = .01). CONCLUSION: By using three-dimensional MR imaging, patients receiving chemotherapy showed reduction of breast density, and the effects were significant after initial treatment with one to two cycles of the AC regimen.

Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.

PURPOSE: To develop a computer-based algorithm for detecting blood vessels that appear in breast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI), and to evaluate the improvement in reducing the number of vascular pixels that are labeled by computer-aided diagnosis (CAD) systems as being suspicious of malignancy. MATERIALS AND METHODS: The analysis was performed in 34 cases. The algorithm applied a filter bank based on wavelet transform and the Hessian matrix to detect linear structures as blood vessels on a two-dimensional maximum intensity projection (MIP). The vessels running perpendicular to the MIP plane were then detected based on the connectivity of enhanced pixels above a threshold. The nonvessel enhancements were determined and excluded based on their morphological properties, including those showing scattered small segment enhancements or nodular or planar clusters. The detected vessels were first converted to a vasculature skeleton by thinning and subsequently compared to the vascular track manually drawn by a radiologist. RESULTS: When evaluating the performance of the algorithm in identifying vascular tissue, the correct-detection rate refers to pixels identified by both the algorithm and radiologist, while the incorrect-detection rate refers to pixels identified by only the algorithm, and the missed-detection rate refers to pixels identified only by the radiologist. From 34 analyzed cases the median correct-detection rate was 85.6% (mean 84.9% +/- 7.8%), the incorrect-detection rate was 13.1% (mean 15.1% +/- 7.8%), and the missed-detection rate was 19.2% (mean 21.3% +/- 12.8%). When detected vessels were excluded in the hot-spot color-coding of the CAD system, they could reduce the labeling of vascular vessels in 2.6%-68.6% of hot-spot pixels (mean 16.6% +/- 15.9%). CONCLUSION: The computer algorithm-based method can detect most large vessels and provide an effective means in reducing the labeling of vascular pixels as suspicious on a DCE-MRI CAD system. This algorithm may improve the workflow of radiologists using CAD for image display, but will be particularly useful for development of automated CAD that gives diagnostic impression.