Differentiation of benign and malignant sub-1 cm breast lesions using dynamic contrast enhanced MRI

The Influence of Data-Driven Compressed Sensing Reconstruction on Quantitative Pharmacokinetic Analysis in Breast DCE MRI

Radial acquisition with MOCCO reconstruction has been previously proposed for high spatial and temporal resolution breast DCE imaging. In this work, we characterize MOCCO across a wide range of temporal contrast enhancement in a digital reference object (DRO). Time-resolved radial data was simulated using a DRO with lesions in different PK parameters. The under sampled data were reconstructed at 5 s temporal resolution using the data-driven low-rank temporal model for MOCCO, compressed sensing with temporal total variation (CS-TV) and more conventional low-rank reconstruction (PCB). Our results demonstrated that MOCCO was able to recover curves with K^trans values ranging from 0.01 to 0.8 min⁻¹ and fixed V_e = 0.3, where the fitted results are within a 10% bias error range. MOCCO reconstruction showed less impact on the selection of different temporal models than conventional low-rank reconstruction and the greater error was observed with PCB. CS-TV showed overall underestimation in both K^trans and V_e. For the Monte-Carlo simulations, MOCCO was found to provide the most accurate reconstruction results for curves with intermediate lesion kinetics in the presence of noise. Initial in vivo experiences are reported in one patient volunteer. Overall, MOCCO was able to provide reconstructed time-series data that resulted in a more accurate measurement of PK parameters than PCB and CS-TV.

Improved value of whole-lesion histogram analysis on DCE parametric maps for diagnosing small breast cancer (≤ 1 cm)

OBJECTIVES

To determine if whole-lesion histogram analysis on dynamic contrast-enhanced (DCE) parametric maps help to improve the diagnostic accuracy of small suspicious breast lesions (≤ 1 cm).

METHODS

This retrospective study included 99 female patients with 114 lesions (40 malignant and 74 benign lesions) suspicious on magnetic resonance imaging (MRI).Two radiologists reviewed all lesions and descripted the morphologic and kinetic characteristics according to BI-RADS by consensus. Whole lesions were segmented on DCE parametric maps (washin and washout), and quantitative histogram features were extracted. Univariate analysis and multivariate logistic regression analysis with forward stepwise covariate selection were performed to identify significant variables. Diagnostic performance was assessed and compared with that of qualitative BI-RADS assessment and quantitative histogram analysis by ROC analysis.

RESULTS

For malignancy defined as a washout or plateau pattern, the qualitative kinetic pattern showed a significant difference between the two groups (p = 0.023), yielding an AUC of 0.603 (95% confidence interval [CI]: 0.507, 0.694). The mean and median of washout were independent quantitative predictors of malignancy (p = 0.002, 0.010), achieving an AUC of 0.796 (95% CI: 0. 709, 0.865). The AUC of the quantitative model was better than that of the qualitative model (p < 0.001).

CONCLUSIONS

Compared with the qualitative BI-RADS assessment, quantitative whole-lesion histogram analysis on DCE parametric maps was better to discriminate between small benign and malignant breast lesions (≤ 1 cm) initially defined as suspicious on DCE-MRI.

KEY POINTS

• For malignancy defined as a washout or plateau, the kinetic pattern may provide information to diagnose small breast cancer. • The mean and median of washout map were significantly lower for small malignant breast lesions than for benign lesions. • Quantitative histogram analysis on MRI parametric maps improves diagnostic accuracy for small breast cancer, which may obviate unnecessary biopsy.

A dynamic lesion model for differentiation of malignant and benign pathologies

Malignant lesions have a high tendency to invade their surrounding environment compared to benign ones. This paper proposes a dynamic lesion model and explores the 2nd order derivatives at each image voxel, which reflect the rate of change of image intensity, as a quantitative measure of the tendency. The 2nd order derivatives at each image voxel are usually represented by the Hessian matrix, but it is difficult to quantify a matrix field (or image) through the lesion space as a measure of the tendency. We conjecture that the three eigenvalues contain important information of the Hessian matrix and are chosen as the surrogate representation of the Hessian matrix. By treating the three eigenvalues as a vector, called Hessian vector, which is defined in a local coordinate formed by three orthogonal Hessian eigenvectors and further adapting the gray level occurrence computing method to extract the vector texture descriptors (or measures) from the Hessian vector, a quantitative presentation for the dynamic lesion model is completed. The vector texture descriptors were applied to differentiate malignant from benign lesions from two pathologically proven datasets: colon polyps and lung nodules. The classification results not only outperform four state-of-the-art methods but also three radiologist experts.

Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer

PURPOSE

The purpose of this study was to assess the clinical value of conventional magnetic resonance imaging (MRI) and intravoxel incoherent motion (IVIM) features for predicting the risk of axillary lymph node (ALN) metastasis in patients with breast cancer.

METHODS

This retrospective study involved 265 patients with breast cancer who underwent 3.0 T breast magnetic resonance imaging examinations prior to surgery and other treatment. Of these, 119 underwent IVIM examination. The features of MRI and IVIM and postoperative pathologic results were collected. The association of MRI features of breast cancer with ALN metastasis were determined by univariate and multivariate analyses. Comparison of IVIM parameters between breast cancer patients with and without ALN metastasis was performed using the Mann-Whitney U test.

RESULTS

Among the 265 patients, 144 (54.3%) had ALN metastasis, and 121 (45.7%) did not. The size and shape of the tumours, T2WI signal, inhomogeneous enhancement, washout intensity-time curves and the values of slow ADC, fast ADC and fraction of fast ADC parameters were significantly associated with ALN metastasis. The AUC of conventional MRI for diagnosing axillary lymph node metastasis was 0.722. The AUC of MRI combined with slow ADC, fast ADC and fraction of fast ADC parameters that were used to diagnose breast cancer with ALN metastasis were 0.814, 0.803 and 0.900, respectively.

CONCLUSIONS

The features of IVIM parameters and conventional MRI can be used to predict the ALN metastasis in patients with breast cancer. MRI combined with fraction of fast ADC showed higher diagnostic efficiency for ALN metastasis in breast cancer than MRI did.

Cortical bone vessel identification and quantification on contrast-enhanced MR images

Background

Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores.

Methods

For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm³) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth.

Results

In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm³, and mean average vessel volume was 0.032±0.012 mm³/vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm.

Conclusions

We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion.

Characterization of Sub-1 cm Breast Lesions Using Radiomics Analysis

BACKGROUND

Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail.

PURPOSE

To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps.

STUDY TYPE

Retrospective, single center.

POPULATION

In all, 149 patients, with a total of 165 lesions scored as BI-RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm³ .

FIELD STRENGTH/SEQUENCE

Higher spatial resolution T₁ -weighted dynamic contrast-enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T.

ASSESSMENT

Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first-order statistics, gray level co-occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases).

STATISTICAL TESTS

Comparison of medians was assessed using the nonparametric Mann-Whitney U-test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models.

RESULTS

Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75-0.81. High negative (>89%) and positive predictive values (>83%) were found for all models.

DATA CONCLUSION

Radiomics analysis of small contrast-enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468-1477.

Role of pharmacokinetic parameters derived with high temporal resolution DCE MRI using simultaneous PET/MRI system in breast cancer: A feasibility study

PURPOSE

To evaluate the reliability of pharmacokinetic parameters like K^trans, Kep and v_e derived through DCE MRI breast protocol using 3T Simultaneous PET/MRI (3Tesla Positron Emission Tomography/Magnetic Resonance Imaging) system in distinguishing benign and malignant lesions.

MATERIALS AND METHODS

High temporal resolution DCE (Dynamic Contrast Enhancement) MRI performed as routine breast MRI for diagnosis or as a part of PET/MRI for cancer staging using a 3T simultaneous PET/MRI system in 98 women having 109 breast lesions were analyzed for calculation of pharmacokinetic parameters (K^trans, v_e, and Kep) at 60s time point using an in-house developed computation scheme.

RESULTS

Receiver operating characteristic (ROC) curve analysis revealed a cut off value for K^trans, Kep, v_e as 0.50, 2.59, 0.15 respectively which reliably distinguished benign and malignant breast lesions. Data analysis revealed an overall accuracy of 94.50%, 79.82% and 87.16% for K^trans, Kep, v_e respectively. Introduction of native T1 normalization with an externally placed phantom showed a higher accuracy (94.50%) than without native T1 normalization (93.50%) with an increase in specificity of 87% vs 84%.

CONCLUSION

Overall the results indicate that reliable measurement of pharmacokinetic parameters with reduced acquisition time is feasible in a 3TMRI embedded PET/MRI system with reasonable accuracy and application may be extended to exploit the potential of simultaneous PET/MRI in further work on breast cancer.

Models and methods for analyzing DCE-MRI: a review

PURPOSE

To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches.

METHODS

DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases.

RESULTS

Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors.

CONCLUSIONS

Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.

Differentiation of benign and malignant sub-1-cm breast lesions using contrast-enhanced sonography

OBJECTIVES

The purpose of this study was to prospectively assess the diagnostic efficacy of contrast-enhanced sonography for differential diagnosis of sub-1-cm breast lesions.

METHODS

Contrast-enhanced sonography was performed in 46 women with 46 sub-1-cm breast lesions scheduled for surgery or biopsy. Histologic results were used as a reference standard. The contrast enhancement pattern, enhancement degree, direction, margin, radial vessels surrounding the lesion, and lesion size discrepancy between contrast-enhanced and conventional sonography were evaluated.

RESULTS

The detection rates for increased size, radial vessels surrounding the lesion, and hyperenhancement on contrast-enhanced sonography were significantly higher in the malignant than the benign group (P < .05). The sensitivity, specificity, and accuracy for increased size were 72.7%, 80.0%, and 78.2%, respectively. The sensitivity, specificity, and accuracy for radial vessels surrounding the lesion were 54.5%, 97.1%, and 87.0%. The sensitivity, specificity, and accuracy for hyperenhancement were 81.8%, 60.0%, and 65.0%. No significant difference was found for the enhancement pattern, direction, and margin between the groups.

CONCLUSIONS

Contrast-enhanced sonographic features of sub-1-cm breast lesions included increased size on contrast-enhanced sonography, radial vessels surrounding the lesion, and hyperenhancement. Identification of these features is useful for differentiation of small breast lesions.

Belief elicitation to populate health economic models of medical diagnostic devices in development

BACKGROUND AND OBJECTIVE

Bayesian methods can be used to elicit experts' beliefs about the clinical value of healthcare technologies. This study investigates a belief-elicitation method for estimating diagnostic performance in an early stage of development of photoacoustic mammography (PAM) imaging versus magnetic resonance imaging (MRI) for detecting breast cancer.

RESEARCH DESIGN

Eighteen experienced radiologists ranked tumor characteristics regarding their importance to detect malignancies. With reference to MRI, radiologists estimated the true positives and negatives of PAM using the variable interval method. An overall probability density function was determined using linear opinion pooling, weighted for individual experts' experience.

RESULT

The most important tumor characteristics are mass margins and mass shape. Respondents considered MRI the better technology to visualize these characteristics. Belief elicitation confirmed this by providing an overall sensitivity of PAM ranging from 58.9 to 85.1% (mode 75.6%) and specificity ranging from 52.2 to 77.6% (mode 66.5%).

CONCLUSION

Belief elicitation allowed estimates to be obtained for the expected diagnostic performance of PAM, although radiologists expressed difficulties in doing so. Heterogeneity within and between experts reflects this uncertainty and the infancy of PAM. Further clinical trials are required to validate the extent to which this belief-elicitation method is predictive for observed test performance.

The effects of applying breast compression in dynamic contrast material-enhanced MR imaging

PURPOSE

To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions.

MATERIALS AND METHODS

This was a HIPAA-compliant, institutional review board-approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22-76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the noncompressed breast. Percentage enhancement difference was calculated between noncompressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-masslike enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed.

RESULTS

Mean percentage compression was 31.3% ± 9.2 (range, 5.8%-53.2%). Percentage enhancement was higher in noncompressed- versus compressed-breast studies in early (146% ± 66 vs 107% ± 42, respectively; P < .001) and delayed (158% ± 68 vs 107% ± 42, respectively; P = .1) phases. Among breast lesions, 12% (seven of 59) were significantly smaller when compressed, which led to underestimation of TNM classification (P < .001). Breast masses (n = 35) showed significantly higher early percentage enhancement (157% ± 71) than lesions with NMLE (n = 15, 120% ± 40; P = .02) and a percentage enhancement difference (47.5% ± 64 vs 17% ± 28, respectively; P = .023). Kinetic curve performance for identifying invasive cancer decreased after compression (area under ROC curve = 0.53 vs 0.71, respectively; P = .02). Breast compression resulted in complete loss of enhancement of nine of 210 lesions (4%).

CONCLUSION

Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.

Feature selection in computer-aided breast cancer diagnosis via dynamic contrast-enhanced magnetic resonance images

The accuracy of computer-aided diagnosis (CAD) for early detection and classification of breast cancer in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is dependent upon the features used by the CAD classifier. Here, we show that fast orthogonal search (FOS), which provides a more efficient iterative manner of computing stepwise regression feature selection, can select features with predictive value from a set of kinetic and texture candidate features computed from dynamic contrast-enhanced magnetic resonance images. FOS can in minutes search candidate feature sets of millions of terms, which may include cross-products of features up to second-, third- or fourth-order. This method is tested on a set of 83 DCE-MRI images, of which 20 are for cancerous and 63 for benign cases, using a leave-one-out trial. The features selected by FOS were used in a FOS predictor and nearest-neighbour predictor and had an area under the receiver operating curve (AUC) of 0.889 and 0.791 respectively. The FOS predictor AUC is significantly improved over the signal enhancement ratio predictor with an AUC of 0.706 (p = 0.0035 for the difference in the AUCs). Moreover, using FOS-selected features in a support vector machine increased the AUC over that resulting when the features were manually selected.

Role of quantitative pharmacokinetic parameter (transfer constant: K(trans)) in the characterization of breast lesions on MRI

Background:

The semi-quantitative analysis of the time–intensity curves in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a limited specificity due to overlapping enhancement patterns after gadolinium administration. With the advances in technology and faster sequences, imaging of the entire breast can be done in a few seconds, which allows measuring the transit of contrast (transfer constant: K^trans) through the vascular bed at capillary level that reflects quantitative measure of porosity/permeability of tumor vessels.

Aim:

Our study aims to evaluate the pharmacokinetic parameter K^trans for enhancing breast lesions and correlate it with histopathology, and assess accuracy, sensitivity, and specificity of this parameter in discriminating benign and malignant breast lesions.

Materials and Methods:

One hundred and fifty-one women with 216 histologically proved enhancing breast lesions underwent high temporal resolution DCE-MRI for the early dynamic analysis for calculation of pharmacokinetic parameters (K^trans) using standard two compartment model. The calculated values of K^trans were correlated with histopathology to calculate the sensitivity, specificity, and accuracy.

Results:

Receiver operating characteristic (ROC) curve analysis revealed a mean K^trans value of 0.56, which reliably distinguished benign and malignant breast lesions with a sensitivity of 91.1% and specificity of 90.3% with an overall accuracy of 89.3%. The area under curve (AUC) was 0.907.

Conclusion:

K^trans is a reliable quantitative parameter for characterizing benign and malignant lesions in routine DCE-MRI of breasts.

Small breast lesion classification performance using the normalized axial-shear strain area feature

Breast cancers that are found and confirmed because they are causing symptoms tend to be larger and are more likely to have already spread to the lymph nodes and beyond. Thus, early detection and confirmation are of paramount importance. The normalized axial-shear strain area (NASSA) feature from the axial-shear strain elastogram (ASSE) has been shown to be a feature that can identify the boundary-bonding conditions that are indicative of the presence of cancer. Recently, we investigated and reported on the potential of the NASSA feature for breast lesion classification into fibroadenomas and cancers. In this article, we investigate the size distribution of the lesions that were part of the previous study and analyze classification performance specifically on small lesions (<10 mm diameter). A total of 33 biopsy-proven malignant tumors and 30 fibroadenomas were part of the study that involved three observers blinded to the Breast Imaging Reporting and Data System (BIRADS) ultrasound scores. The observers outlined the lesions on the sonograms and the lesion size (maximum circle-equivalent diameter in millimeters) was computed from this outline. The ASSE was automatically segmented and color-overlaid on the sonogram, and the NASSA feature from ASSE was computed semi-automatically. Receiver operating characteristic curves were then generated for the subset of cases involving small lesions. Box plots were produced for the two different lesion size groups, small and large, from a logistic regression classifier that was built previously. The results of our study show that approximately 38% and 22% of the fibroadenomas and cancers, respectively, were small. Furthermore, it was found that the NASSA feature resulted in a perfect classification of the small lesions, both in the training data and in the cross-validation. For lesions <10 mm the difference in fibroadenoma and cancer mean scores was 0.73 ± 0.13 (p < 0.001), whereas lesions >10 mm had a difference of 0.52 ± 0.24 (p < 0.001). The results also showed that the small lesions actually had better classification than the larger lesions (>10 mm). These results suggest that the ASSE feature can work equally well, even on small lesions, to improve the standard ultrasound BIRADS-based breast lesion classification of fibroadenoma and malignant tumors.

Diffusion tensor magnetic resonance imaging of the normal breast: reproducibility of DTI-derived fractional anisotropy and apparent diffusion coefficient at 3.0 T

PURPOSE

Diffusion-weighted imaging (DWI) may improve the diagnostic performance of conventional breast magnetic resonance imaging (MRI). Diffusion tensor imaging (DTI) is an extension of DWI. If DTI-derived measurements are to be clinically useful, particularly for predicting and/or monitoring therapeutic effects, they must be robust and reliable. The purpose of this study was to assess intra- and interobserver reproducibility of DTI-derived fractional anisotropy (FA) and apparent diffusion coefficient (ADC) at 3.0 T.

MATERIALS AND METHODS

This prospective study was approved by the Institutional Review Board, and participants provided written informed consent. Sixty normal contralateral breasts of 60 patients (28-85 years, median 57) were analysed with a DWI sequence following a standard MRI protocol. Four authors performed all postprocessing and analyses independently and in different sessions. The same authors, blinded to the initial results, repeated the image postprocessing and analysis 4 weeks after the initial session.

RESULTS

Mean ADC and FA for DTI sequences were, respectively, 1.92±0.30 and 0.32±0.09. Intra- and interobserver agreement of the four radiologists for ADC and FA were good (acceptable). Kappa values for ADC were intra-R1=0.82; intra-R2=0.84; intra-R3=0.89; intra-R4=0.88; inter-R1-R2=0.73; inter-R1-R3=0.74; inter-R1-R4=0.81; inter-R2-R3=0.76; inter-R2-R4=0.77; inter-R3-R4=0.83. Kappa values for FA were intra-R1=0.60; intra-R2=0.72; intra-R3=0.84; intra-R4 = 0.66; inter-R1-R2=0.64; inter-R1-R3=0.69; inter-R1-R4=0.72; inter-R2-R3=0.80; inter-R2-R4=0.71; inter-R3-R4=0.73. Within-subject coefficient of variation was 15% for ADC and 30% for FA. Repeatability with α=0.05 was 0.37×10-3 mm(2)/s for ADC and 0.112 for FA.

CONCLUSIONS

ADC and FA measurements obtained with DTI are reproducible and may be valid, reliable and sensitive to change. ADC values obtained with DTI are more reproducible than FA.

Evaluation of novel genetic algorithm generated schemes for positron emission tomography (PET)/magnetic resonance imaging (MRI) image fusion

The use and benefits of a multimodality approach in the context of breast cancer imaging are discussed. Fusion techniques that allow multiple images to be viewed simultaneously are discussed. Many of these fusion techniques rely on the use of color tables. A genetic algorithm that generates color tables that have desired properties such as satisfying the order principle, the rows, and columns principle, have perceivable uniformity and have maximum contrast is introduced. The generated 2D color tables can be used for displaying fused datasets. The advantage the proposed method has over other techniques is the ability to consider a much larger set of possible color tables, ensuring that the best one is found. We asked radiologists to perform a set of tasks reading fused PET/MRI breast images obtained using eight different fusion techniques. This preliminary study clearly demonstrates the need and benefit of a joint display by estimating the inaccuracies incurred when using a side-by-side display. The study suggests that the color tables generated by the genetic algorithm are good choices for fusing MR and PET images. It is interesting to note that popular techniques such as the Fire/Gray and techniques based on the HSV color space, which are prevalent in the literature and clinical practice, appear to give poorer performance.

Accuracy of magnetic resonance in suspicious breast lesions: a systematic quantitative review and meta-analysis

Dynamic contrast-enhanced breast magnetic resonance (MR) is a promising emerging technique for evaluating breast lesions. A quantitative systematic review was performed to estimate the accuracy of breast MR in the diagnosis of high-risk breast lesions and breast cancer. A comprehensive search of the Cochrane Library, MEDLINE, CANCERLIT, LILACS, and EMBASE databases was performed from January 1985 to August 2010. The medical subjects heading (MeSH) and text words for the terms "breast neoplasm", "breast lesions", "breast cancer" and "magnetic resonance" were combined with the MeSH term diagnosis ("sensitivity and specificity"). Studies that compared breast MR with paraffin-embedded sections parameters for the diagnosis of breast lesions (benign, high-risk borderline, and breast cancer) were included. Sixty-nine studies were analyzed, which included 9,298 women with 9,884 breast lesions. Interrater overall agreement between breast MR and paraffin section diagnosis was 79% (κ = 0.55), indicating moderate agreement. Pooled sensitivity and specificity were 90% [95% CI 88-92%] and 75% [95% CI 70-79%], respectively. The pooled likelihood positive ratio was 3.64 (95% CI 3.0-4.2) and the negative ratio was 0.12 (95% CI 0.09-0.15). For breast cancer or high-risk lesions versus benign lesions, the AUC was 0.91 for breast MR and the point Q* was 0.84. In summary, breast MR is a useful pre-operative test for predicting the diagnosis of breast lesions.

Diffusion tensor magnetic resonance imaging of the breast: a pilot study

OBJECTIVES

Diffusion-weighted MR imaging has shown diagnostic value for differential diagnosis of breast lesions. Diffusion tensor imaging (DTI) adds information about tissue microstructure by addressing diffusion direction. We have examined the diagnostic application of DTI of the breast.

METHODS

A total of 59 patients (71 lesions: 54 malignant, 17 benign) successfully underwent prospective echo planar imaging-DTI (EPI-DTI) (1.5 T). First, diffusion direction both of parenchyma as well as lesions was assessed on parametric maps. Subsequently, apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values were measured. Statistics included univariate (Mann-Whitney U test, receiver operating analysis) and multivariate (logistic regression analysis, LRA) tests.

RESULTS

Main diffusion direction of parenchyma was anterior-posterior in the majority of cases (66.1%), whereas lesions (benign, malignant) showed no predominant diffusion direction in the majority of cases (23.9%). ADC values showed highest differences between benign and malignant lesions (P<0.001) with resulting area under the curve (AUC) of 0.899. FA values were lower in benign (interquartile range, IR, 0.14-0.24) compared to malignant lesions (IR 0.21-0.35, P<0.002) with an AUC of 0.751-0.770. Following LRA, FA did not prove to have incremental value for differential diagnosis over ADC values.

CONCLUSIONS

Microanatomical differences between benign and malignant breast lesions as well as breast parenchyma can be visualized by using DTI.

Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach

RATIONALE AND OBJECTIVES

Enhancement characteristics after administration of a contrast agent are regarded as a major criterion for differential diagnosis in magnetic resonance mammography (MRM). However, no consensus exists about the best measurement method to assess contrast enhancement kinetics. This systematic investigation was performed to compare visual estimation with manual region of interest (ROI) and computer-aided diagnosis (CAD) analysis for time curve measurements in MRM.

MATERIALS AND METHODS

A total of 329 patients undergoing surgery after MRM (1.5 T) were analyzed prospectively. Dynamic data were measured using visual estimation, including ROI as well as CAD methods, and classified depending on initial signal increase and delayed enhancement.

RESULTS

Pathology revealed 469 lesions (279 malignant, 190 benign). Kappa agreement between the methods ranged from 0.78 to 0.81. Diagnostic accuracies of 74.4% (visual), 75.7% (ROI), and 76.6% (CAD) were found without statistical significant differences.

CONCLUSIONS

According to our results, curve type measurements are useful as a diagnostic criterion in breast lesions irrespective of the method used.

Dynamic contrast-enhanced MRI in the diagnosis and management of breast cancer

Dynamic contrast-enhanced MRI (DCE-MRI) is an evolving tool for determining breast disease, which benefits from the move to imaging at 3 T. It has major capabilities for the diagnosis, detection and monitoring of malignancy. It benefits from being non-invasive and three-dimensional, allowing visualisation of the extent of disease and its angiogenic properties, visualisation of lesion heterogeneity, detection of changes in angiogenic properties before morphological alterations, and the potential to predict the overall response either before the start of therapy or early during treatment. In addition, DCE-MRI is emerging as a powerful tool for screening high-risk patients and for detecting high-grade ductal carcinoma in situ. However, there are also a number of limitations, including the overlap in enhancement patterns between malignant and benign disease, the failure to resolve microscopic disease particularly in the neoadjuvant setting, and the inconsistent predictive value of the enhancement pattern for clinical outcome. Careful consideration should be given to the technical requirements of individual examinations and the need for automation of post-processing techniques to appropriately handle the growing volume of data acquired. Research continues, focusing on the use of higher field strengths with improved spatial and temporal resolution data, improving understanding of the mechanism of contrast enhancement at the cellular level, and developing macromolecular and targeted contrast agents.

Magnetic resonance imaging identifies multifocal and multicentric disease in breast cancer patients who are eligible for partial breast irradiation

BACKGROUND

In this retrospective study, the authors hypothesized that magnetic resonance imaging (MRI) would alter partial breast irradiation (PBI) eligibility by identifying cancers outside the PBI volume compared with mammography alone.

METHODS

Since 2002, MRI was used nonselectively at the authors' institution for the staging of patients with nonmetastatic breast cancer. Of 450 consecutive patients with invasive breast cancer, 110 patients who were eligible for PBI were identified by using criteria outlined by National Surgical Adjuvant Breast and Bowel Project B-39/Radiation Oncology Group trial 0413 based on mammography, ultrasonography, and initial pathology. In that trial, patients were randomized (stage I/II invasive cancers that measured <or=3 cm and <or=3 positive lymph nodes) to receive either whole-breast radiotherapy or PBI. MRI reports were reviewed to determine whether MRI identified secondary lesions 1) within the same quadrant (multifocal), 2) in a different quadrant (multicentric), or 3) in the contralateral breast. These lesions were pathologically proven carcinoma and would have rendered the patient ineligible for PBI.

RESULTS

MRI identified secondary lesions in 10% of patients (95% confidence interval [CI], 4.4%-15.6%). Multifocal disease was identified in 3.6% (95% CI, 1.4%-9%), multicentric disease was identified in 4.5% (95% CI, 2%-10.2%), and contralateral disease was identified in 1.8% (95% CI, 0.5%-6.4%). The proportion of patients with false-positive MRI findings was 4.5% (95% CI, 2%-10.2%). The positive predictive value of MRI was 72.2% (95% CI, 46.4%-89.3%).

CONCLUSIONS

MRI identified frequent secondary cancers that would not be removed routinely by surgery or targeted in the radiation field if treated with PBI. The current data suggest that MRI should be considered to assess PBI eligibility to minimize potential local failures.

New potential and applications of contrast-enhanced ultrasound of the breast: Own investigations and review of the literature

Imaging of angiogenesis is a challenge for modern imaging. Velocimetry in malignant breast lesions and density of malignant vessels are very low. In breast imaging, first results of contrast-enhanced ultrasound (CEUS) were disappointing. Microbubbles are fragile when examined with high frequency US, commonly used in breast imaging. Second-generation contrast agents increase intensively the signal level of breast lesions and new sequences like CPS (Coherence Pulse Sequencing) might be accurate to detect malignant vessels in breast lesions for characterization, to assess the extent of infiltrative breast carcinoma or to evaluate the tumor response after chemotherapy. Another interesting clinical application is the differentiation between post-operative changes and recurrences. In this review, we detail the main results obtained with contrast ultrasonography in a characterization study. In malignant lesions, enhancement was fast, starting with less than 20s. Compared to MR, enhancement appeared faster. Malignant vessels were predominant in the external ring of the nodule, conversely vessels were seen in the center of the lesion in benign nodules. Malignant vessels were also seen outside the lesion. This knowledge could lead the surgeon to perform a larger lumpectomy in these cases, to obtain sane margins and to reduce recurrences.

Removing undersampling artifacts in DCE-MRI studies using independent components analysis

In breast MRI mammography both high temporal resolution and high spatial resolution have been shown to be important in improving specificity. Adaptive methods such as projection reconstruction time-resolved imaging of contrast kinetics (PR-TRICKS) allow images to be reconstructed at various temporal and spatial resolutions from the same data set. The main disadvantage is that the undersampling, which is necessary to produce high temporal resolution images, leads to the presence of streak artifacts in the images. We present a novel method of removing these artifacts using independent components analysis (ICA) and demonstrate that this results in a significant improvement in image quality for both simulation studies and for patient dynamic contrast-enhanced (DCE)-MRI images. We also investigate the effect of artifacts on two quantitative measures of contrast enhancement. Using simulation studies we demonstrate that streak artifacts lead to pronounced periodic oscillations in pixel concentration curves which, in turn, lead to increased errors and introduce bias into heuristic measurements. ICA filtering significantly reduces this bias and improves accuracy. Pharmacokinetic modeling was more robust and there was no evidence of bias due to the presence of streak artifacts. ICA filtering did not significantly reduce the errors in the estimated pharmacokinetic parameters; however, the chi-squared error was greatly reduced after ICA filtering.

Indeterminate or suspicious breast lesions detected initially with MR imaging: value of MRI-directed breast ultrasound

RATIONALE AND OBJECTIVES

To retrospectively determine the value of magnetic resonance imaging (MRI)-directed breast ultrasonography (US) in the evaluation of indeterminate or suspicious lesions identified on contrast-enhanced, breast MRI.

MATERIALS AND METHODS

A total of 395 patients presenting for breast MRI during a 4-year period was retrospectively reviewed. Seventy-one patients were recommended for MRI-directed US for further characterization of indeterminate or suspicious breast lesions detected on MRI. Fifty-five patients (all female; age 31-80 years) had US. Their MRI and US were reviewed and tested for correlations with histologic results or long term follow-up. Logistic regression analyses were used to test for associations between MRI lesion characteristics and US detection rate.

RESULTS

US identified 46 of 97 (47%) lesions depicted at MRI from 55 patients (47 [85%] of these patients had histories of breast malignancies). Twelve cancers were found from the 97 lesions (12%). Biopsy was avoidable in 10 lesions (10%). The detection rate with US was slightly higher with "mass" (55% [23/42]) lesions described in MRI than "non-mass" lesions or lymph nodes (42% [23/55]). There was a significant positive association (odd ratio = 1.23: 95% CI = 1.05-1.43, P = .01) between US detection rate and MRI mass lesion size. There was no statistical significance between US detection rate and the presence of malignancies; 42% (5/12) of MRI malignant lesions were not visualized with US.

CONCLUSIONS

MRI-directed US reduced the number of biopsies required for indeterminate or suspicious MRI lesions. Nevertheless, the lesions which were biopsied had a low rate of malignancy.

Contrast-enhanced magnetic resonance imaging of the breast: the value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in classifying lesions

The value of pharmacokinetic parameters derived from fast dynamic imaging during initial enhancement in characterizing breast lesions on magnetic resonance imaging (MRI) was evaluated. Sixty-eight malignant and 34 benign lesions were included. In the scanning protocol, high temporal resolution imaging was combined with high spatial resolution imaging. The high temporal resolution images were recorded every 4.1 s during initial enhancement (fast dynamic analysis). The high spatial resolution images were recorded at a temporal resolution of 86 s (slow dynamic analysis). In the fast dynamic evaluation pharmacokinetic parameters (K^trans, V_e and k_ep) were evaluated. In the slow dynamic analysis, each lesion was scored according to the BI-RADS classification. Two readers evaluated all data prospectively. ROC and multivariate analysis were performed. The slow dynamic analysis resulted in an AUC of 0.85 and 0.83, respectively. The fast dynamic analysis resulted in an AUC of 0.83 in both readers. The combination of both the slow and fast dynamic analyses resulted in a significant improvement of diagnostic performance with an AUC of 0.93 and 0.90 (P = 0.02). The increased diagnostic performance found when combining both methods demonstrates the additional value of our method in further improving the diagnostic performance of breast MRI.

Breast MR segmentation and lesion detection with cellular neural networks and 3D template matching

A novel fully automated system is introduced to facilitate lesion detection in dynamic contrast-enhanced, magnetic resonance mammography (DCE-MRM). The system extracts breast regions from pre-contrast images using a cellular neural network, generates normalized maximum intensity-time ratio (nMITR) maps and performs 3D template matching with three layers of 12x12 cells to detect lesions. A breast is considered to be properly segmented when relative overlap >0.85 and misclassification rate <0.10. Sensitivity, false-positive rate per slice and per lesion are used to assess detection performance. The system was tested with a dataset of 2064 breast MR images (344slicesx6 acquisitions over time) from 19 women containing 39 marked lesions. Ninety-seven percent of the breasts were segmented properly and all the lesions were detected correctly (detection sensitivity=100%), however, there were some false-positive detections (31%/lesion, 10%/slice).

[About mastopathy]

The authors review knowledge of the clinical, imaging and pathological diagnosis of mastopathy syndrome and the issues of its therapy and care. They observe that no unified nomenclature exists, all the disciplines judge the idea of mastopathy differently. Clinical and imaging diagnosis is uncertain, the correct diagnosis can be established only by the pathologist. The role of surgery is minimal. The precancerous nature of mastopathy is also discussed, the rate of malignant transformation is low. Malignant transformation of benign lesions is very rare. The most important tools are close observation, systematic care of patients by a team of professionals, possibly by the same persons, in the same institute. Further research, and more exact definitions are necessary.

Meta-analysis of MR imaging in the diagnosis of breast lesions

PURPOSE

To determine, in a meta-analysis, the diagnostic performance of contrast material-enhanced magnetic resonance (MR) imaging in patients with breast lesions.

MATERIALS AND METHODS

Studies to assess the diagnostic performance of MR imaging in patients suspected of having breast cancer who underwent MR imaging and biopsy from January 1985 through March 2005 were reviewed for inclusion. A summary receiver operating characteristic curve was constructed, and pooled weighted estimates of sensitivity and specificity were calculated by using the recently developed bivariate approach for diagnostic meta-analysis.

RESULTS

Of 251 eligible studies, 44 were included in the meta-analysis (sample size range, 14-821; cancer prevalence, 23%-84%). Pooled weighted estimates of sensitivity and specificity were 0.90 (95% confidence interval: 0.88, 0.92) and 0.72 (95% confidence interval: 0.67, 0.77), respectively. The performance of breast MR imaging was influenced by the prevalence of cancer in the studied population (P = .05) and by whether two criteria (ie, morphology, enhancement, and kinetic enhancement pattern)--versus one or three criteria--were used to differentiate benign from malignant lesions (P = .02).

CONCLUSION

MR imaging of the breast has high sensitivity and lower specificity in the evaluation of breast lesions.

SUPPLEMENTAL MATERIAL

http://radiology.rsnajnls.org/cgi/content/full/2461061298/DC1.

A fast method of generating pharmacokinetic maps from dynamic contrast-enhanced images of the breast

A new approach to fitting pharmacokinetic models to DCE-MRI data is described. The method relies on fitting individual concentration curves to a small set of basis functions and then making use of a look up table to relate the fitting coefficients to pre-calculated pharmacokinetic parameters. This is significantly faster than traditional non-linear fitting methods. Using simulated data and assuming a Tofts model, the accuracy of this direct approach is compared to the Levenberg-Marquardt algorithm. The effect of signal to noise ratio and the number of basis functions used on the accuracy is investigated. The basis fitting approach is slightly less accurate than the traditional non-linear least squares approach but the ten-fold improvement in speed makes the new technique useful as it can be used to generate pharmacokinetic maps in a clinically acceptable timeframe.

Improved lesion detection in MR mammography: three-dimensional segmentation, moving voxel sampling, and normalized maximum intensity-time ratio entropy

RATIONALE AND OBJECTIVES

The objective of this work was to develop a quantitative method for improving lesion detection in dynamic contrast-enhanced magnetic resonance mammography (DCEMRM). For this purpose, we segmented and analyzed suspicious regions according to their contrast enhancement dynamics, generated a normalized maximum intensity-time ratio (nMITR) projection, and explored it to extract important features, to improve accuracy and reproducibility of detection.

MATERIALS AND METHODS

A novel automated method is introduced to segment and analyze lesions in three dimensions. It consists of four consecutive stages: volume of interest selection, nMITR projection generation using a voxel sampling method based on a moving 3 x 3 mask, three-dimensional lesion segmentation, and feature extraction. The nMITR projection of the detected lesion is used to extract six features: mean, maximum, standard deviation, kurtosis, skewness, and entropy, and their diagnostic significance is studied in detail. High-resolution MR images of 52 breast masses from 46 women are analyzed using the technique developed.

RESULTS

Entropy, standard deviation, and the maximum and mean value features were found to have high significance (P < 0.001) and diagnostic accuracy (0.86-0.97). The kurtosis and skewness were not significant. Automated analysis of DCEMRM using nMITR was shown to be feasible.

CONCLUSION

The lesion detection method described is efficient and leads to improved, accurate, reproducible diagnoses. It is reliable in terms of observer variability and may allow for a better standardization of clinical evaluations. The findings demonstrate the usefulness of nMITR based features; nMITR-entropy shows the best performance for quantitative diagnosis.

Combined diffusion-weighted and dynamic contrast-enhanced MRI for prostate cancer diagnosis--correlation with biopsy and histopathology

PURPOSE

To determine whether the combination of diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI provides higher diagnostic sensitivity for prostate cancer than each technique alone.

MATERIALS AND METHODS

Fourteen patients with a clinical suspicion of prostate cancer underwent endorectal MRI on a 1.5T scanner prior to transrectal ultrasound (TRUS)-guided biopsies. The average values of the apparent diffusion coefficient (ADC, calculated from b-values of 0 and 600), K(trans), v(e), maximum gadolinium (Gd) concentration, onset time, mean gradient, and maximum enhancement were determined. Correlation with histology was based on biopsy (six patients) and prostatectomy specimen (eight patients) results. The Tukey-Kramer test was used for statistical analysis.

RESULTS

The average values of all MRI parameters, except v(e) and maximum Gd concentration, showed significant differences between tumor and normal prostate. The sensitivity and specificity values were respectively 54% (35-72%) and 100% (95-100%) for the ADC data, and 59% (39-77%) and 74% (63-83%) for the DCE data. When both ADC and DCE results were combined, the sensitivity increased to 87% (68-95%) and specificity decreased to 74% (62-83%).

CONCLUSION

All but two DW- and DCE-MRI parameters showed significant differences between tumor and normal prostate. Combining both techniques provides better sensitivity, with a small decrease in specificity.

Influence of post-treatment delay on the evaluation of the response to focused ultrasound surgery of breast cancer by dynamic contrast enhanced MRI

The assessment of the effectiveness of MRI-guided focused ultrasound surgery (MRIgFUS) of breast carcinomas can be performed by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters which monitor the presence of residual tumour. The aim of this study was to evaluate the effect of the post-treatment delay on this assessment. DCE-MRI data were acquired immediately and 3-14 days after MRIgFUS treatment of 26 tumours (<7 days, n = 6; = or > ge;7 days, n = 20). The percentage of residual tumour was determined histologically on the resected mass and correlated with two DCE-MRI parameters: increase in signal intensity (ISI) and positive enhancement integral (PEI). No correlation could be found between DCE-MRI data acquired immediately after treatment and the percentage of residual tumour. Good correlation coefficients were found for data acquired several days after treatment (ISI, r = 0.749; PEI, r = 0.778). However, they were higher when the post-treatment time interval was 7 days or more (ISI, r = 0.962; PEI, r = 0.934). These results suggest that a post-treatment delay of 7 days is necessary for the accurate assessment of the presence of residual tumour by DCE-MRI parameters.

Evaluation of a Prospective Scoring System Designed for a Multicenter Breast MR Imaging Screening Study

PURPOSE

To evaluate prospectively the accuracy of a lesion classification system designed for use in a magnetic resonance (MR) imaging high-breast-cancer-risk screening study.

MATERIALS AND METHODS

All participating patients provided written informed consent. Ethics committee approval was obtained. The results of 1541 contrast material-enhanced breast MR imaging examinations were analyzed; 1441 screening examinations were performed in 638 women aged 24-51 years at high risk for breast cancer, and 100 examinations were performed in 100 women aged 23-81 years. Lesion analysis was performed in 991 breasts, which were divided into design (491 breasts) and testing (500 breasts) sets. The reference standard was histologic analysis of biopsy samples, fine-needle aspiration cytology, or minimal follow-up of 24 months. The scoring system involved the use of five features: morphology (MOR), pattern of enhancement (POE), percentage of maximal focal enhancement (PMFE), maximal signal intensity-time ratio (MITR), and pattern of contrast material washout (POCW). The system was evaluated by means of (a) assessment of interreader agreement, as expressed in kappa statistics, for 315 breasts in which both readers analyzed the same lesion, (b) assessment of the diagnostic accuracy of the scored components with receiver operating characteristic curve analysis, and (c) logistic regression analysis to determine which components of the scoring system were critical to the final score. A new simplified scoring system developed with the design set was applied to the testing set.

RESULTS

There was moderate reader agreement regarding overall lesion outcome (ie, malignant, suspicious, or benign) (kappa=0.58) and less agreement regarding the scored components. The area under the receiver operating characteristic curve (AUC) for the overall lesion score, 0.88, was higher than the AUC for any one component. The components MOR, POE, and POCW yielded the best overall result. PMFE and MITR did not contribute to diagnostic utility. Applying a simplified scoring system to the testing set yielded a nonsignificantly (P=.2) higher AUC than did applying the original scoring system (sensitivity, 84%; specificity, 86.0%).

CONCLUSION

Good diagnostic accuracy can be achieved by using simple qualitative descriptors of lesion enhancement, including POCW. In the context of screening, quantitative enhancement parameters appear to be less useful for lesion characterization.

Total-body MR-imaging in oncology

Although MRI is an effective modality in oncology, state-of-the-art total-body MRI (TB-MRI) in the past was infeasible in the diagnostic work-up, due to the need for repeated examinations with repositioning and separate surface coils to cover all body parts. To overcome this limitation, either a moving table platform in combination with the body-coil or a special designed rolling table platform with one body phased-array coil have been implemented with promising results for both tumor staging and metastases screening. Since 2004, state-of-the-art TB-MR imaging with high spatial resolution has become feasible using a newly developed 1.5 Tesla TB-MRI system with multiple receiver channels. This review gives an overview based on the recent literature as well as our own experience concerning the possibilities, challenges, and limitations of TB-MRI in oncology, emphasizing both oncological staging and early tumor detection in asymptomatic subjects.

Treatment planning challenges in breast irradiation: the ideal and the practical

Radiotherapy has recently undergone some interesting developments, with the introduction of new technology and techniques in many departments. Arguably, with this comes an increase in the expectation of its capability. The treatment site that continues to represent most of the workload in our departments is breast. We should consider how relevant these contemporary changes are in the treatment of breast cancer. In this paper, we review some of the challenges in planning breast treatments and how they may be addressed with contemporary radiotherapy techniques.

MRI/PET nonrigid breast-image registration using skin fiducial markers

We propose a finite-element method (FEM) deformable breast model that does not require elastic breast data for nonrigid PET/MRI breast image registration. The model is applicable only if the stress conditions in the imaged breast are virtually the same in PET and MRI. Under these conditions, the observed intermodality displacements are solely due the imaging/reconstruction process. Similar stress conditions are assured by use of an MRI breast-antenna replica for breast support during PET, and use of the same positioning. The tetrahedral volume and triangular surface elements are used to construct the FEM mesh from the MRI image. Our model requires a number of fiducial skin markers (FSM) visible in PET and MRI. The displacement vectors of FSMs are measured followed by the dense displacement field estimation by first distributing the displacement, vectors linearly over the breast surface and then distributing them throughout the volume. Finally, the floating MRI image is warped to a fixed PET image, by using an appropriate shape function in the interpolation from mesh nodes to voxels. We tested our model on an elastic breast phantom with simulated internal lesions and on a small number of patients imaged, with FMS using PET and MRI. Using simulated lesions (in phantom) and real lesions (in patients) visible in both PET and MRI, we established that the target registration error (TRE) is below two pet voxels.

Pathologic correlates of false positive breast magnetic resonance imaging findings: which lesions warrant biopsy?

BACKGROUND

Contrast-enhanced breast magnetic resonance imaging (MRI) is highly sensitive for breast cancer. However, adoption of breast MRI is hampered by frequent false positive (FP) findings. Though ultimately proven benign, these suspicious findings require biopsy due to abnormal morphology and/or kinetic enhancement curves that simulate malignancy on MRI. We hypothesized that analysis of a series of FP MRI findings could reveal a pattern of association between certain "suspicious" lesions and benign disease that might help avoid unnecessary biopsy of such lesions in the future.

METHODS

A retrospective chart review identified women undergoing breast MRI between June 1995 and March 2002 with FP findings identified by MRI alone. Lesions were retrospectively characterized according to an MRI Breast Imaging-Reporting and Data System lexicon and matched to pathology.

RESULTS

Twenty-two women were identified with 29 FP lesions. Morphology revealed 1 focus (3.5%), 5 masses less than 5 mm (17%), 11 masses greater than 5 mm (38%), 1 (3.5%) linear enhancement, and 11 (38%) non-mass-like enhancement. Kinetic curves were suspicious in 15 (52%). Histology demonstrated 20 (69%) variants of normal tissue and 9 (31%) benign masses. MRI lesions less than 5 mm (n = 6, 20.5%) were small, well-delineated nodules of benign breast tissue.

CONCLUSION

Suspicious MRI lesions less than 5 mm often represent benign breast tissue and could potentially undergo surveillance instead of biopsy.