In X-ray examinations, it is essential that radiographers carefully use collimation to the appropriate anatomy of interest to minimize the overall integral dose to the patient. The shadow regions are not diagnostically meaningful and could impair the overall image quality. Thus, it is desirable to detect the collimation and exclude the shadow regions to optimize image display. However, due to the large variability of collimated images, collimation detection remains a challenging task. In this paper, we consider a region of interest (ROI) in an image, such as the collimation, can be described by two distinct views, a cluster of pixels within the ROI and the corners of the ROI. Based on this observation, we propose a robust multi-view learning based strategy for collimation detection in digital radiography. Specifically, one view is from random forests learning based region detector, which provides pixel-wise image classification and each pixel is labeled as either in-collimation or out-of-collimation. The other view is from a discriminative, learning-based landmark detector, which detects the corners and localizes the collimation within the image. Nevertheless, given the huge variability of the collimated images, the detection from either view alone may not be perfect. Therefore, we adopt an adaptive view fusing step to obtain the final detection by combining region and corner detection. We evaluate our algorithm in a database with 665 X-ray images in a wide variety of types and dosages and obtain a high detection accuracy (95%), compared with using region detector alone (87%) and landmark detector alone (83%).
The short-scan Feldkamp David Kress (FDK) method for C-arm CT reconstruction involves a heuristic raybased
weighting scheme to handle data redundancies. This scheme is known to be approximate under general
circumstances and it often creates low frequency image artifacts in regions away from the central axial plane.
Alternative algorithms, such as the one proposed by Defrise and Clack (DC),1 can handle data redundancy in
a theoretically exact manner and thus notably improve image quality. The DC algorithm, however, is computationally
more complex than FDK, as it requires a shift-variant 2D filtering of the data instead of a efficient 1D
filtering. In this paper, a modification of the original DC algorithm is investigated, which applies the efficient
FDK filtering scheme whereever possible and the DC filtering scheme only where it is required. This modification
leads to a more efficient implementation of the DC algorithm, in which filtering effort can be reduced by up to
about 70%, dependent on the specific geometry set-up. This gain in computation speed makes the DC method
even more attractive for use in an interventional environment, where fast and interactive X-ray imaging is a
crucial requirement.
Anti-scatter grids used in full-field digital mammography not only attenuate scattered radiation but also attenuate
primary radiation. Dose saving could be achieved if the effect of scattered radiation is compensated with a software-based scatter correction not attenuating the primary radiation. In this work, we have carried out phantom studies in order to investigate dose saving and image quality of grid-less acquisition in combination with software-based scatter correction. The results show that similar image quality (contrast-to-noise ratio and contrast-detail visibility) can be obtained with this alternative acquisition and post-processing scheme at reduced dose. The relative dose reduction is breast-thickness-dependent and is >20% for typical breast thicknesses. We have carried out a clinical study with 75 patients that showed non-inferior image quality at reduced dose with our novel approach compared to the standard method.
In X-ray imaging, a reduction of the field of view (FOV) is proportional to a reduction in radiation dose. The resulting
truncation, however, is incompatible with conventional tomographic reconstruction algorithms. This problem has been
studied extensively. Very recently, a novel method for region of interest (ROI) reconstruction from truncated projections
with neither the use of prior knowledge nor explicit extrapolation has been published, named Approximated Truncation
Robust Algorithm for Computed Tomography (ATRACT). It is based on a decomposition of the standard ramp filter into a
2D Laplace filtering (local operation) and a 2D Radon-based filtering step (non-local operation).
The 2D Radon-based filtering that involves many interpolations complicates the filtering procedure in ATRACT, which
essentially limits its practicality. In this paper, an optimization for this shortcoming is presented. That is to apply ATRACT
in one dimension, which implies that we decompose the standard ramp filter into the 1D Laplace filter and a 1D convolutionbased
filter. The convolution kernel was determined numerically by computing the 1D impulse response of the standard
ramp filtering coupled with the second order anti-derivative operation. The proposed algorithm was evaluated by using a
reconstruction benchmark test, a real phantom and a clinical data set in terms of spatial resolution, computational efficiency
as well as robustness of correction quality.
The evaluation outcomes were encouraging. The proposed algorithm showed improvement in computational performance
with respect to the 2D ATRACT algorithm and furthermore maintained reconstructions of high accuracy in presence
of data truncation.
A new algorithm is suggested to compute one or several virtual projection images directly from cone-beam data
acquired in a tomosynthesis geometry. One main feature of this algorithm is that it does not involve the explicit
reconstruction of a 3D volume, and a subsequent forward-projection operation, but rather operates using solely
2D image processing steps. The required 2D processing is furthermore based on the use of pre-computed entities,
so that a significant speed-up in the computations can be obtained. The presented algorithm can be applied
to a variety of CT geometries, and is here investigated for a mammography application, to simulate virtual
mammograms from a set of low-dose tomosynthesis projection images. A first evaluation from real measured
data is given.
In computed tomography (CT) or conebeam tomography (CBT), filtered backprojection (FBP) has been known as an
efficient technique for reconstructing 3D-volumes from acquired projection data. Plain backprojection only would
result in systematically blurred objects. To compensate for this blurring, convolution-based filters have been derived that
are non-local and sampled with 2048 coefficients or more, dependent on the projection data size. This filtering operation
can be classified as finite impulse response (FIR) filtering. In terms of image quality, ideally-derived kernels sometimes
amplify too much the high frequency noise (e.g. due to X-ray quantum noise) from the input projections. In practice,
regularized filters are often preferred, damping higher frequencies while preserving the sharpness and signal dynamics
needed for the reconstructed 3D-objects. From discrete systems theory, another filter type with infinite impulse response
(IIR) has been known. Because such a filter recursively uses backward components, it requires very few coefficients
while the long-range filter effect is preserved. In the presented work, IIR filters have systematically been designed and
tested. They have been adjusted for the correction of a blurring system transfer function as well as for high-frequency
noise suppression. Image quality has carefully been inspected by reconstruction of phantom data and clinical cases. It has
been found that the filtering step in CBT/FBP can be realized as a recursive filter only, i.e. in self-contained IIRnotation,
including adaptions like e.g. apodisation. The number of filtering operations is significantly reduced hereby. So
with IIR filtering an efficient alternative for FBP filtering is available.
Over the last decade, significant progress has been made in terms of treatment of diseases using minimallyinvasive
procedures. This progress was facilitated through multiple refinements of the imaging capabilities of
C-arm systems in the interventional room, and more sophisticated procedures may become feasible by further
refining the performance of these systems. Our primary focus is to eliminate two strong limitations of the
current circular cone-beam imaging approach: cone-beam artifacts and limited extent of the volume covered in
the direction of the patient bed. To solve this problem, we seek a source trajectory that (i) is complete in terms
of Tuy's condition, (ii) can be periodically-repeated without discontinuities to allow long-object imaging, (iii)
is practical, and (iv) offers full R-line coverage (an R-line is a line that connects any two source positions). A
trajectory that satisfies all of our constraint is the
Arc-Extended-Line-Arc(AELA) trajectory. Unfortunately,
this trajectory does not allow smooth, continuous scanning at reasonable dose. In this work, we propose a new
data acquisition geometry: the Ellipse-Line-Ellipse (ELE) trajectory. This geometry satisfies all of our constraints
along with the attractive feature that smooth, continuous scanning at reasonable dose is enabled.
Between 2006 and 2008, the business volume of the top 20 orthopedic manufacturers increased by 30% to about
$35 Billion. Similar growth rates could be observed in the market of neurological devices, which went up in
2009 by 10.9% to a volume of $2.2 Billion in the US and by 7.0% to 500 Million in Europe.* These remarkable
increases are closely connected to the fact that nowadays, many medical procedures, such as implantations
in osteosynthesis or the placement of stents in neuroradiology can be performed using minimally-invasive approaches.
Such approaches require elaborate interoperative imaging technology. C-arm based tomographic X-ray
region-of-interest (ROI) tomography can deliver suitable imaging guidance in these circumstances: it can offer 3D
information in desired patient regions at reasonably low X-ray dose. Tomographic ROI reconstruction, however,
is in general challenging since projection images might be severely truncated. Recently, a novel, truncation-robust
algorithm (ATRACT) has been suggested for 3D C-arm ROI imaging. In this paper, we report for the first time
on the performance of ATRACT for reconstruction from real, angiographic C-arm data. Our results indicate
that the resulting ROI image quality is suitable for intraoperative imaging. We observe only little differences to
the images from a non-truncated acquisition, which would necessarily require significantly more X-ray dose.
X-ray 3D rotational angiography based on C-arm systems has become a versatile and established tomographic imaging modality for high contrast objects in interventional environment. Improvements in data acquisition, e.g. by use of flat panel detectors, will enable C-arm systems to resolve even low-contrast details. However, further progress will be limited by the incompleteness of data acquisition on the conventional short-scan circular source trajectories. Cone artifacts, which result from that incompleteness, significantly degrade image quality by severe smearing and shading. To assure data completeness a combination of a partial circle with one or several line segments is investigated. A new and efficient reconstruction algorithm is deduced from a general inversion formula based on 3D Radon theory. The method is theoretically exact, possesses shift-invariant filtered backprojection (FBP) structure, and solves the long object problem. The algorithm is flexible in dealing with various circle and line configurations. The reconstruction method requires nothing more than the theoretically minimum length of scan trajectory. It consists of a conventional short-scan circle and a line segment approximately twice as long as the height of the region-of-interest. Geometrical deviations from the ideal source trajectory are considered in the implementation in order to handle data of real C-arm systems. Reconstruction results show excellent image quality free of cone artifacts. The proposed scan trajectory and reconstruction algorithm assure excellent image quality and allow low-contrast tomographic imaging with C-arm based cone-beam systems. The method can be implemented without any hardware modifications on systems commercially available today.
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