Automatic segmentation of bones in computed tomography (CT) images is used for instance in beam hardening correction algorithms where it improves the accuracy of resulting CT numbers. Of special interest are pelvic bones, which—because of their strong attenuation—affect the accuracy of brachytherapy in this region. This work evaluated the performance of the JJ2016 algorithm with the performance of MK2014v2 and JS2018 algorithms; all these algorithms were developed by authors. Visual comparison, and, in the latter case, also Dice similarity coefficients derived from the ground truth were used. It was found that the 3D-based JJ2016 performed better than the 2D-based MK2014v2, mainly because of the more accurate hole filling that benefitted from information in adjacent slices. The neural network-based JS2018 outperformed both traditional algorithms. It was, however, limited to the resolution of 128³ owing to the limited amount of memory in the graphical processing unit (GPU).

Deep learning algorithms have improved the speed and quality of segmentation for certain tasks in medical imaging. The aim of this work is to design and evaluate an algorithm capable of segmenting bones in dual-energy CT data sets. A convolutional neural network based on the 3D U-Net architecture was implemented and evaluated using high tube voltage images, mixed images and dual-energy images from 30 patients. The network performed well on all the data sets; the mean Dice coefficient for the test data was larger than 0.963. Of special interest is that it performed better on dual-energy CT volumes compared to mixed images that mimicked images taken at 120 kV. The corresponding increase in the Dice coefficient from 0.965 to 0.966 was small since the enhancements were mainly at the edges of the bones. The method can easily be extended to the segmentation of multi-energy CT data.

Purpose: To develop and evaluate-in a proof-of-concept configuration-a novel iterative reconstruction algorithm (DIRA) for quantitative determination of elemental composition of patient tissues for application to brachytherapy with low energy (amp;lt; 50 keV) photons and proton therapy. Methods: DIRA was designed as a model-based iterative reconstruction algorithm, which uses filtered backprojection, automatic segmentation and multimaterial tissue decomposition. The evaluation was done for a phantom derived from the voxelized ICRP 110 male phantom. Soft tissues were decomposed to the lipid, protein and water triplet, bones were decomposed to the compact bone and bone marrow doublet. Projections were derived using the Drasim simulation code for an axial scanning configuration resembling a typical DECT (dual-energy CT) scanner with 80 kV and Sn140 kV x-ray spectra. The iterative loop produced mono-energetic images at 50 and 88 keV without beam hardening artifacts. Different noise levels were considered: no noise, a typical noise level in diagnostic imaging and reduced noise level corresponding to tenfold higher doses. An uncertainty analysis of the results was performed using type A and B evaluations. The two approaches were compared. Results: Linear attenuation coefficients averaged over a region were obtained with relative errors less than 0.5% for all evaluated regions. Errors in average mass fractions of the three-material decomposition were less than 0.04 for no noise and reduced noise levels and less than 0.11 for the typical noise level. Mass fractions of individual pixels were strongly affected by noise, which slightly increased after the first iteration but subsequently stabilized. Estimates of uncertainties in mass fractions provided by the type B evaluation differed from the type A estimates by less than 1.5% for most cases. The algorithm was fast, the results converged after 5 iterations. The algorithmic complexity of forward polyenergetic projection calculation was much reduced by using material doublets and triplets. Conclusions: The simulations indicated that DIRA is capable of determining elemental composition of tissues, which are needed in brachytherapy with low energy (amp;lt; 50 keV) photons and proton therapy. The algorithm provided quantitative monoenergetic images with beam hardening artifacts removed. Its convergence was fast, image sharpness expressed via the modulation transfer function was maintained, and image noise did not increase with the number of iterations. c 2017 American Association of Physicists in Medicine

Advanced model-based iterative reconstruction algorithms in quantitative computed tomography (CT) perform automatic segmentation of tissues to estimate material properties of the imaged object. Compared with conventional methods, these algorithms may improve quality of reconstructed images and accuracy of radiation treatment planning. Automatic segmentation of tissues is, however, a difficult task. The aim of this work was to develop and evaluate an algorithm that automatically segments tissues in CT images of the male pelvis. The newly developed algorithm (MK2014) combines histogram matching, thresholding, region growing, deformable model and atlas-based registration techniques for the segmentation of bones, adipose tissue, prostate and muscles in CT images. Visual inspection of segmented images showed that the algorithm performed well for the five analysed images. The tissues were identified and outlined with accuracy sufficient for the dual-energy iterative reconstruction algorithm whose aim is to improve the accuracy of radiation treatment planning in brachytherapy of the prostate.

 In medical helical cone-beam CT, it is common that the region-of-interest (ROI) is contained inside the helix cylinder, while the complete object is long and extends outside the top and the bottom of the cylinder. This is the Long Object Problem. Analytical reconstruction methods for helical cone-beam CT have been designed to handle this problem. It has been shown that a moderate amount of over-scanning is sufficient for reconstruction of a certain ROI. The over-scanning projection rays travel both through the ROI as well as outside the ROI. This is unfortunate for iterative methods since it seems impossible to compute accurate values for the projection rays which travel partly inside and partly outside the ROI. Therefore, it seems that the useful ROI will diminish for every iteration step. We propose the following solution to the problem. Firstly, we reconstruct volume regions also outside the ROI. These volume regions will certainly be incompletely reconstructed, but our experimental results show that they serve well for projection generation. This is rather counter-intuitive and contradictory to our initial assumptions. Secondly, we use careful extrapolation and masking of projection data. This is not a general necessity, but needed for the chosen iterative algorithm, which includes rebinning and iterative filtered backprojection. Our idea here was to use an approximate reconstruction method which gives cone-beam artifacts and then improve the reconstructed result by iterative filtered backprojection. The experimental results seem very encouraging. The cone-beam artifacts can indeed be removed. Even voxels close to the boundary of the ROI are as well enhanced by the iterative loop as those in the middle of the ROI.

This paper presents novel results from an ongoing feasibility study of fully 3D X-ray scanning of Pinus Sylvestris (Scots Pine) logs. Logs are assumed to be translated through two identical and static cone beam systems with the beams rotated 90degrees relative to eachother, providing a dual set of 2D-projections. For reasons of both cost and speed, each 2D-detector in these two systems consists of a limited number of line detectors. The quality of the reconstructed images is far from perfect, due to sparse detector data and missing projection angles. In spite of this we show that by employing a shape- and direction discriminative technique based on second derivatives, we are able to enhance knot-like features in these data. In the enhanced images it is then possible to detect and localize the pith for each whorl of knots, and subsequently also to perform a full segmentation of the knots in the heartwood.

Better knowledge of elemental composition of patient tissues may improve the accuracy of absorbed dose delivery in brachytherapy. Deficiencies of water-based protocols have been recognized and work is ongoing to implement patient-specific radiation treatment protocols. A model based iterative image reconstruction algorithm DIRA has been developed by the authors to automatically decompose patient tissues to two or three base components via dual-energy computed tomography. Performance of an updated version of DIRA was evaluated for the determination of prostate calcification. A computer simulation using an anthropomorphic phantom showed that the mass fraction of calcium in the prostate tissue was determined with accuracy better than 9%. The calculated mass fraction was little affected by the choice of the material triplet for the surrounding soft tissue. Relative differences between true and approximated values of linear attenuation coefficient and mass energy absorption coefficient for the prostate tissue were less than 6% for photon energies from 1 keV to 2 MeV. The results indicate that DIRA has the potential to improve the accuracy of dose delivery in brachytherapy despite the fact that base material triplets only approximate surrounding soft tissues.

Quantitative tissue classification using dual-energy CT has the potential to improve accuracy in radiation therapy dose planning as it provides more information about material composition of scanned objects than the currently used methods based on single-energy CT. One problem that hinders successful application of both single-and dualenergy CT is the presence of beam hardening and scatter artifacts in reconstructed data. Current pre-and post-correction methods used for image reconstruction often bias CT numbers and thus limit their applicability for quantitative tissue classification. Here we demonstrate simulation studies with a novel iterative algorithm that decomposes every soft tissue voxel into three base materials: water, protein and adipose. The results demonstrate that beam hardening artifacts can effectively be removed and accurate estimation of mass fractions of all base materials can be achieved. In the future, the algorithm may be developed further to include segmentation of soft and bone tissue and subsequent bone decomposition, extension from 2-D to 3-D and scatter correction.

Radial sampling of k-space is known to simultaneously provide both high spatial and high temporal resolution. Recently, an optimal radial profile time order based on the Golden Ratio was presented in [1]. We have adopted and modified the idea, with a focus onhigher temporal resolution without sacrificing any image quality.

[1] Winkelmann et. al.: An optimal radial profile order based on the golden ratio for time-resolved MRI, IEEE Trans. Med. Im, Vol.26, No.1,2007.

The paper presentsa method for projection generation through a 2-D pixel image or a3-D voxel volume. During the design of the method, we have strivedto apply knowledge from signal processing theory. Introductoryexperiments, were the projection generation method was used in aniterative CT reconstruction loop, indicate that the method issound. Our hope is that the method could be applied in manydifferent connections, were one task is to compute projectionsthrough a 2-D pixel image or a 3-D voxel volume. In the future weplan to do more experiments, both in 2-D and 3-D, which hopefullyfurther demonstrates the usefulness of themethod. 

This largelytutorial treatise presents a Fourier based model for 2D-projection,the latter being a most important ingredient in any iterativereconstruction method. For sampled images the model requires anassumed basis function, which implicitly defines the necessarywindow and interpolation functions. We unravel the basis and windowfunctions for some projection techniques described as procedures.Circular symmetric basis functions make it simple to findinterpolation coefficients but require well tuned interpolationfunctions to avoid aliasing. We find it unnecessary to distinguishbetween voxel and ray driven projection. These two techniquesconcern only the innermost loop and both can be applied to anyinterpolation function, and to projection and back-projectionalike. 

A common wish in non-destructive testing is to investigate a large object with a small interesting detail inside. Due to practical circumstances, the projections may sometimes be truncated. According to the theory on tomography, it is then impossible to reconstruct the object. However, sometimes it is possible to receive an approximate result. It turns out that the key-point is how to implement the ramp-filter. The quality of the result depends on the object itself. We show one good experiment on real data, linear cone-beam tomography for logs. We also show experiments on the Shepp-Logan phantom, well-known from medical CT, and discuss the varying reconstruction quality.

The MATLAB/C program take version 3.1 is a program for simulation of X-ray projections from 3D volume data. It is based on an older C version by Muller-Merbach as well as an extended C version by Turbell. The program can simulate 2D X-ray projections from 3D objects. These data can then be input to 3D reconstruction algorithms. Here however, we only demonstrate a couple of 2D reconstruction algorithms, written in MATLAB. Simple MATLAB examples show how to generate the take projections followed by subsequent reconstruction. Compared to the old take version, the C code have been carefully revised. A preliminary, rather untested feature of using a polychromatic X-ray source with different energy levels was already included in the old take version. The current polychromatic feature X-ray is however carefully tested. For example, it has been compared with the results from the program described by Malusek et al. We also demonstrate experiments with a polychromatic X-ray source and a Plexiglass object giving the beam-hardening artefact. Detector sensitivity for different energy levels is not included in take. However, in section~\refsec:realexperiment, we describe a technique to include the detector sensitivity into the energy spectrum. Finally, an experiment with comparison of real and simulated data were performed. The result wasn't completely successful, but we still demonstrate it. Contemporary analytical reconstruction methods for helical cone-beam CT have to be designed to handle the Long Object Problem. Normally, a moderate amount of over-scanning is sufficient for reconstruction of a certain Region-of-interest (ROI). Unfortunately, for iterative methods, it seems that the useful ROI will diminish for every iteration step. The remedies proposed here are twofold. Firstly, we use careful extrapolation and masking of projection data. Secondly, we generate and utilize projection data from incompletely reconstructed volume parts, which is rather counter-intuitive and contradictory to our initial assumptions. The results seem very encouraging. Even voxels close to the boundary in the original ROI are as well enhanced by the iterative loop as the middle part.

Next generation helical cone-beam CT will feature pitches around 80 mm. It is predicted that reconstruction algorithms to be used in these machines with still rather modest cone angles may not necessarily be exact, but rather have an emphasis on simplicity and speed. The PImethods are a family of non-exact algorithms, all of which are based on complete data capture with a detector collimated to the Tam-window followed by rebinning to obliquely parallel ray geometry. The non-exactness is identified as inconsistency in the space invariant one-dimensional ramp-filtering step. It is shown that this inconsistency can be reduced resulting in significant improvement in image quality and increased tolerance for higher pitch and cone angle. A short theoretical background for the PI-methods is given but the algorithms themselves are not given in any detail. A set of experiments on mathematical phantoms illustrate (among other things) how the amount of artefacts grow with increasing cone angles.