With the emergence of energy-resolved x-ray photon counting detectors multi-material spectral x-ray imaging has been made possible. This form of imaging allows the discrimination and quantification of individual materials comprising an inspected anatomical area. However, the acquisition of quantitative material data puts strong requirements on the performance capabilities of a given x-ray system. Scattered radiation is one of the key sources of influencing the quality of material quantification accuracy. The aim of the present investigation was to assess the impact of x-ray scatter on quantitative spectral CT imaging using a pre-clinical photon counting scanner prototype. Acquisitions of a cylindrical phantom with and without scatter were performed. The phantom contained iodine and gadolinium inserts placed at various locations. The projection data was then decomposed onto a water-iodine-gadolinium material basis and reconstructed. An analysis of the resulting iodine and gadolinium material images with and without scatter was conducted. It was concluded that, at an SPR level of up to 3.5%, scatter does not compromise material quantification for all investigated gadolinium concentrations, but for iodine a substantial underestimation was observed. The findings in this study suggest that scatter has a lower impact on K-edge material imaging in comparison with material imaging not featuring a K-edge.
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.
It has previously been shown that 2D spectral mammography can be used to discriminate between (likely benign) cystic and (potentially malignant) solid lesions in order to reduce unnecessary recalls in mammography. One limitation of the technique is, however, that the composition of overlapping tissue needs to be interpolated from a region surrounding the lesion. The purpose of this investigation was to demonstrate that lesion characterization can be done with spectral tomosynthesis, and to investigate whether the 3D information available in tomosynthesis can reduce the uncertainty from the interpolation of surrounding tissue. A phantom experiment was designed to simulate a cyst and a tumor, where the tumor was overlaid with a structure that made it mimic a cyst. In 2D, the two targets appeared similar in composition, whereas spectral tomosynthesis revealed the exact compositional difference. However, the loss of discrimination signal due to spread from the plane of interest was of the same strength as the reduction of anatomical noise. Results from a preliminary investigation on clinical tomosynthesis images of solid lesions yielded results that were consistent with the phantom experiments, but were still to some extent inconclusive. We conclude that lesion characterization is feasible in spectral tomosynthesis, but more data, as well as refinement of the calibration and discrimination algorithms, are needed to draw final conclusions about the benefit compared to 2D.
The promising increase in cancer detection rates1, 2 makes digital breast tomosynthesis (DBT) an interesting alternative to full-field digital mammography (FFDM) in breast cancer screening. However, this benefit comes at the cost of an increased average glandular dose in a combined DBT plus FFDM acquisition protocol. Synthetic mammograms, which are computed from the reconstructed tomosynthesis volume data, have demonstrated to be an alternative to a regular FFDM exposure in a DBT plus synthetic 2D reading mode.3 Besides weighted averaging and modified maximum intensity projection (MIP) methods,4, 5 the integration of CAD techniques for computing a weighting function in the forward projection step of the synthetic mammogram generation has been recently proposed.6, 7 In this work, a novel and computationally efficient method is presented based on an edge-retaining algorithm, which directly computes the weighting function by an edge-detection filter.
The development of new x-ray imaging techniques often requires prior knowledge of tissue attenuation, but the sources of such information are sparse. We have measured the attenuation of adipose breast tissue using spectral imaging, in vitro and in vivo. For the in-vitro measurement, fixed samples of adipose breast tissue were imaged on a spectral mammography system, and the energy-dependent x-ray attenuation was measured in terms of equivalent thicknesses of aluminum and poly-methyl methacrylate (PMMA). For the in-vivo measurement, a similar procedure was applied on a number of spectral screening mammograms. The results of the two measurements agreed well and were consistent with published attenuation data and with measurements on tissue-equivalent material.
Spectral X-ray imaging allows to differentiate between two given tissue types, provided their spectral absorption characteristics differ measurably. In mammography, this method is used clinically to determine a decomposition of the breast into adipose and glandular tissue compartments, from which the glandular tissue fraction and, hence, the volumetric breast density (VBD) can be computed. Another potential application of this technique is the characterization of lesions by spectral mammography. In particular, round lesions are relatively easily detected by experienced radiologists, but are often difficult to characterize. Here, a method is described that aims at discriminating cystic from solid lesions directly on a spectral mammogram, obtained with a calibrated spectral mammography system and using a hypothesis-testing algorithm based on a maximum likelihood approach. The method includes a parametric model describing the lesion shape, compression height variations and breast composition. With the maximum likelihood algorithm, the model parameters are estimated separately under the cyst and solid hypothesis. The resulting ratio of the maximum likelihood values is used for the final tissue characterization. Initial results using simulations and phantom measurements are presented.
Filtered backprojection (FBP) has been commonly used as an efficient and robust reconstruction technique in
tomographic X-ray imaging during the last decades. For standard geometries like circle or helix it is known how
to efficiently filter the data. However, for geometries with only few projection views or with a limited angular
range, the application of FBP algorithms generally provides poor results. In digital breast tomosynthesis (DBT)
these limitations give rise to image artifacts due to the limited angular range and the coarse angular sampling. In
this work, a generalized FBP algorithm is presented, which uses the filtered projection data of all acquired views
for backprojection along one direction. The proposed method yields a computationally efficient generalized FBP
algorithm for DBT, which provides similar image quality as iterative reconstruction techniques while preserving
the ability for region of interest reconstructions. To demonstrate the excellent performance of this method,
examples are given with a simulated breast phantom and the hardware BR3D phantom.
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 radiologist's grading with deviations ranging between -28%, (q = 0.025) and +16%, (q = 0.975).
Digital breast tomosynthesis (DBT) allows a quasi-3D reconstruction of the breast with high in-plane and poor
depth resolution by the principles of limited angle tomography. The limited angular range and the coarse
angular sampling result in prominent streak artifacts arising from high-contrast structures such as calcifications.
These artifacts do not only degrade the image quality but also hold the risk of overlaying suspicious tissue
structure in neighbouring slices, which might therefore be overlooked. This work presents a second pass method
for correcting these kinds of high-contrast streak artifacts. In a first pass reconstruction the candidate highcontrast
calcifications are segmented and subtracted from the original projection data to generate a subsequent
artifact-free second pass reconstruction. The method is demonstrated in a simulation study using software breast
phantoms, which have been derived from segmented MRI data.
Rotational X-ray data acquisition in combination with gated reconstruction is applied most commonly to
reconstruct 3D or 4D images of the heart on interventional X-ray systems. The data are acquired during
breath hold and with intravenous contrast agent injection. Unfortunately, when using a single circular arc
acquisition with parallel ECG recording, the gating of the projections leads to under-sampling artifacts in the
reconstruction volume. In this contribution an artifact reduction method is suggested which is based on an
initial gated reconstruction of a cardiac volume at limited quality, the subsequent segmentation of the volume
and a second pass correction to enhance the signal-to-noise ratio in the reconstruction volume and to reduce
the artifacts due to gating. The method is applied to both phantom and animal data.
The tomographic reconstruction of the beating heart requires dedicated methods. One possibility is gated
reconstruction, where only data corresponding to a certain motion state are incorporated. Another one is motioncompensated
reconstruction with a pre-computed motion vector field, which requires a preceding estimation of
the motion. Here, results of a new approach are presented: simultaneous reconstruction of a three-dimensional
object and its motion over time, yielding a fully four-dimensional representation. The object motion is modeled
by a time-dependent elastic transformation. The reconstruction is carried out with an iterative gradient-descent
algorithm which simultaneously optimizes the three-dimensional image and the motion parameters. The method
was tested on a simulated rotational X-ray acquisition of a dynamic coronary artery phantom, acquired on a
C-arm system with a slowly rotating C-arm. Accurate reconstruction of both absorption coefficient and motion
could be achieved. First results from experiments on clinical rotational X-ray coronary angiography data are
shown. The resulting reconstructions enable the analysis of both static properties, such as vessel geometry and
cross-sectional areas, and dynamic properties, like magnitude, speed, and synchrony of motion during the cardiac