Harald S. Heese

Breast‐density measurement using photon‐counting spectral mammography

Purpose To evaluate a method for measuring breast density using photon‐counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. Methods The measurement method employs the spectral difference in x‐ray attenuation between adipose and fibro‐glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue‐equivalent material (slabs and breast‐shaped phantoms) and using clinical data from a screening population (Symbol). A state‐of‐the‐art nonspectral method for breast‐density assessment was used for benchmarking. Symbol. No caption available. Results The precision of the spectral method was estimated to be 1.5–1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7–2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. Conclusions The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast‐density measurement on an individual basis such as treatment monitoring and personalized screening.

Spectral volumetric glandularity assessment

Breast density is associated with an increased risk of developing breast cancer, and several methods have been proposed recently for the fully-automatic assessment of volumetric breast density. However, conventional algorithms require an accurate estimation of the breast shape and thickness for the separation into adipose and glandular tissue within the breast. Here, a spectral extension of a recently developed automatic volumetric breast density algorithm is investigated. The proposed approach measures the adipose and glandular tissue content without any additional breast thickness model. The feasibility of the spectral glandularity assessment is illustrated with measurements from an energy-resolving photon-counting mammography system using reference materials including the BR3D phantom.

Automatic volumetric glandularity assessment from full field digital mammograms

Estimation of breast density suffers from high inter-observer variability. A fully automated solution for objective and consistent assessment of breast density from full field digital mammography (FFDM) data is presented. For the computation of glandularity a region of interest (ROI) with a corresponding height model is automatically extracted from the mammograms. Assessment of adipose and glandular tissue volumes is performed by means of calibration data. Volumetric breast density is finally computed as the fraction of glandular tissue volume to overall breast volume with respect to the ROI. The fully automated approach provides volumetric breast density estimates that show strong non-linear correlation with the manual reference (R2=0.80) and high intra-patient consistency (R∈[0.92,0.97]) among mammograms of different orientation or laterality.

Robust estimation of mammographic breast density: a patient-based approach

Breast density has become an established risk indicator for developing breast cancer. Current clinical practice reflects this by grading mammograms patient-wise as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense based on visual perception. Existing (semi-) automated methods work on a per-image basis and mimic clinical practice by calculating an area fraction of fibroglandular tissue (mammographic percent density). We suggest a method that follows clinical practice more strictly by segmenting the fibroglandular tissue portion directly from the joint data of all four available mammographic views (cranio-caudal and medio-lateral oblique, left and right), and by subsequently calculating a consistently patient-based mammographic percent density estimate. In particular, each mammographic view is first processed separately to determine a region of interest (ROI) for segmentation into fibroglandular and adipose tissue. ROI determination includes breast outline detection via edge-based methods, peripheral tissue suppression via geometric breast height modeling, and - for medio-lateral oblique views only - pectoral muscle outline detection based on optimizing a three-parameter analytic curve with respect to local appearance. Intensity harmonization based on separately acquired calibration data is performed with respect to compression height and tube voltage to facilitate joint segmentation of available mammographic views. A Gaussian mixture model (GMM) on the joint histogram data with a posteriori calibration guided plausibility correction is finally employed for tissue separation. The proposed method was tested on patient data from 82 subjects. Results show excellent correlation (r = 0.86) to radiologists grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).

Application of three-pass metal artefact reduction to photon-counting breast tomosynthesis

Digital breast tomosynthesis is a rising modality in breast cancer screening and diagnosis. As such, there is also increasing interest in employing breast tomosynthesis in diagnostic tasks like tomosynthesis-guided stereotactic breast biopsy, which includes imaging in presence of metal objects. Since reconstruction techniques in tomosynthesis operate on projection data from a limited angular range, highly attenuating metal objects create strong streak-like tomosynthesis artefacts, which are accompanied by strong undershoots at the object boundaries in the focal and adjacent slices. These artefacts can significantly hamper image quality by obscuring anatomical detail in the vicinity of the metal object. In this contribution, we therefore present an approach for reducing such metal artefacts by means of a three-pass reconstruction method. The method analyzes the reconstructed tomosynthesis volume for metal contributions. It eventually determines corresponding pixels in the projection data, and decomposes the projections accordingly into metal and nonmetal projections. After each projection set is reconstructed independently, the final, enhanced tomosynthesis volume is obtained by a non-linear blending operation. The proposed approach was evaluated on a set of eight clinical cases. Each breast contained a metal clip, which is typically left as marker after biopsy. The proposed method achieved to retain the appearance of the metal object in the focal and its adjacent slices. At the same time complete removal of streak artefacts in all distant slices was achieved. Efficacy of the method in presence of larger objects was demonstrated in phantom studies, where visibility of microcalcifications was completely restored.

Inter- and intra-observer variations in the delineation of lesions in mammograms

Many clinical and research tasks require the delineation of lesions in radiological images. There is a variety of methods available for deriving such delineations, ranging from free hand manual contouring and manual positioning of lowparameter graphical objects, to (semi-)automatic computerized segmentation methods. In this paper we investigate the impact of the chosen segmentation method on the inter-observer variability of the resulting contour. Three different methods are compared in this paper, namely (1) manual positioning of an ellipse, (2) an automatic segmentation method, coined live-segmentation, which depends on the current mouse pointer position as input information and is updated in real-time as the user hovers with the mouse over the image and (3) free form segmentation which is realized by allowing the user to pull the result of method (2) to image positions that the contour is required to pass. Each of the three methods was used by three experienced radiologists to delineate a set of 215 round breast lesion images in digital mammograms. Agreement between contours was assessed by computing the Dice coefficient. The median Dice coefficient for the ellipses placed by different readers was 0.85. The intra-reader Dice coefficient comparing ellipses and livesegmentations was 0.84, thus showing that the live-segmentation results agree with ellipse segmentations to the same extent as readers agree on the ellipse placement. Inter-observer agreement when using the live-segmentation was higher than for the ellipses (median Dice = 0.91 vs. 0.85) showing that the live-segmentation is a more reproducible alternative to the ellipse placement.

Identification of corresponding lesions in multiple mammographic views using star-shaped iso-contours

It is common practice to assess lesions in two different mammographic views of each breast: medio-lateral oblique (MLO) and cranio-caudal (CC). We investigate methods that aim at automatic identification of a lesion which was indicated by the user in one view in the other view of the same breast. Automated matching of user indicated lesions has slightly different objectives than lesion segmentation or matching for improved computer aided detection, leading to different algorithmic choices. A novel computationally efficient algorithm is presented which is based on detection of star-shaped iso-contours with high sphericity and local consistency. The lesion likelihood is derived from a purely geometry based figure of merit and thus is invariant against monotonous intensity transformations (e.g. non-linear LUTs).Validation was carried out by virtue of FROC curves on a public database consisting of entirely digital mammograms with expert-delineated match pairs, showing superior performance as compared to gradient-based minimum cost path algorithms, with computation times faster by an order of magnitude and the potential of being fully parallelizable for GPU implementations.

A method for lesion visibility prediction in mammograms by local analysis of spectral anatomical noise

Detection of mass lesions in mammograms via visual readings is a challenging task, and the radiographic density of the breast tissue or its strong anatomical structure may render lesions completely invisible. In order to assess visibility of lesions of a certain size in a given mammogram, we propose a measure for prediction of lesion visibility that complements established approaches for breast density assessment by taking also local structure into account. This measure is based on the analysis of spectral anatomical noise in terms of local standard deviation values for several frequency bands of the mammogram. The resulting values are used to generate two dimensional visibility maps for different lesion sizes. Phantoms of structured tissue equivalent materials were imaged using a full-field digital mammography (FFDM) system, and spherical lesions of different sizes were artificially added to the images. In an observer study with ten observers visibility thresholds were determined from a total of 290 simulated lesions. The resulting nonlinear threshold curve was verified in a second observer study, where 66 lesions were artificially added in clinical mammograms of varying breast density according to BI-RADS classification. A prediction accuracy of 92% was obtained, suffering mostly from different image characteristics in the breast tissue regions near the skinline or the pectoral muscle.

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

To establish operator‐independent, fully automated planning of standard cardiac geometries and to determine the impact on interstudy reproducibility of cardiac functional parameters.

Automatic knee cartilage delineation using inheritable segmentation

We present a fully automatic method for segmentation of knee joint cartilage from fat suppressed MRI. The method first applies 3-D model-based segmentation technology, which allows to reliably segment the femur, patella, and tibia by iterative adaptation of the model according to image gradients. Thin plate spline interpolation is used in the next step to position deformable cartilage models for each of the three bones with reference to the segmented bone models. After initialization, the cartilage models are fine adjusted by automatic iterative adaptation to image data based on gray value gradients. The method has been validated on a collection of 8 (3 left, 5 right) fat suppressed datasets and demonstrated the sensitivity of 83±6% compared to manual segmentation on a per voxel basis as primary endpoint. Gross cartilage volume measurement yielded an average error of 9±7% as secondary endpoint. For cartilage being a thin structure, already small deviations in distance result in large errors on a per voxel basis, rendering the primary endpoint a hard criterion.