KEYWORDS: Digital breast tomosynthesis, Mammography, Breast, X-rays, Modulation transfer functions, Imaging systems, Tomosynthesis, Spatial resolution, Breast cancer
Digital breast tomosynthesis (DBT) enables significantly higher cancer detection rates compared to full-field digital mammography (FFDM) without compromising the recall rate. However, regarding microcalcification assessment established tomosynthesis system concepts still tend to be inferior to FFDM. To further boost the clinical role of DBT in breast cancer screening and diagnosis, a system concept was developed that enables fast wide-angle DBT with the unique in-plane resolution capabilities known from FFDM. The concept comprises a novel x-ray tube concept that incorporates an adaptive focal spot position, fast flat-panel detector technology, and innovative algorithmic concepts for image reconstruction. We have built a DBT system that provides tomosynthesis image stacks and synthetic mammograms from 50° tomosynthesis scans realized in less than five seconds. In this contribution, we motivate the design of the system concept, present a physics characterization of its imaging performance, and outline the algorithmic concepts used for image processing. We conclude with illustrating the potential clinical impact by means of clinical case examples from first evaluations in Europe.
Denoising algorithms are sensitive to the noise level and noise power spectrum of the input image and their ability to adapt to this. In the worst-case, image structures can be accidentally removed or even added. This holds up for analytical image filters but even more for deep learning-based denoising algorithms due to their high parameter space and their data-driven nature. We propose to use the knowledge about the noise distribution of the image at hand to limit the influence and ability of denoising algorithms to a known and plausible range. Specifically, we can use the physical knowledge of X-ray radiography by considering the Poisson noise distribution and the noise power spectrum of the detector. Through this approach, we can limit the change of the acquired signal by the denoising algorithm to the expected noise range, and therefore prevent the removal or hallucination of small relevant structures. The presented method allows to use denoising algorithms and especially deep learning-based methods in a controlled and safe fashion in medical x-ray imaging.
Wide–angle digital breast tomosynthesis (DBT) is well known to offer benefits in mass perceptibility compared to narrow–angle DBT due to reduced anatomical overlap. Regarding the perceptibility of micro–calcifications the situation is somehow inverted. On the one hand this can be related to effects during data acquisition and their impact on the system MTF. On the other hand there is a wider spread of calcifications in depth direction in narrow–angle DBT, which distributes calcifications over more slices. This is equivalent to an inherent thicker slice for high spatial frequencies. In this work we want to assume an equivalent quality of raw data and only focus on the effects of different acquisition angles in the reconstruction. We propose an algorithm which creates so–called hybrid thick DBT slices and optimizes the visualization of calcifications while preserving the high mass perceptibility of thin wide–angle DBT slices. The algorithm is purely based on filtered backprojection (FBP) and can be implemented in an efficient manner. For validation simulation studies using the VICTRE (FDA) pipeline are performed. Our results indicate that hybrid thick–slices in wide-angle DBT enable to successfully solve the contrarian imaging tasks of high mass and high calcification perception within one imaging setup.
Metallic implants are responsible for various artifacts in
flat-detector computed tomography visible as streaks
and dark areas in the reconstructed volumetric images. In this paper a novel method for a fast reduction
of these metal artifacts is presented using a three-step correction procedure to approximate the missing parts
of the raw data. In addition to image quality aspects, this paper deals with the problem of high correction
latencies by proposing a reconstruction and correction framework, that utilizes the massive computational power
of graphics processing units (GPUs). An initial volume is reconstructed, followed by a 3-dimensional metal voxel
segmentation algorithm. These metal voxels allow us to identify
metal-influenced detector elements by using a
simplified geometric forward projection. Consequently, these areas are corrected using a 3D interpolation scheme
in the raw data domain, followed by a second reconstruction. This volume is then segmented into three materials
with respect to bone structures using a threshold-based algorithm. A forward projection of the obtained tissueclass
model substitutes missing or corrupted attenuation values for each detector element affected by metal and
is followed by a final reconstruction. The entire process including the initial reconstruction, takes less than a
minute (5123 volume with 496 projections of size 1240x960) and offers significant improvements of image quality.
The method was evaluated with data from two FD-CT C-arm systems (Artis Zee and Artis Zeego, Siemens
Healthcare, Forchheim, Germany).
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