5,799 research outputs found
Beam Orientation Optimization for Intensity Modulated Radiation Therapy using Adaptive l1 Minimization
Beam orientation optimization (BOO) is a key component in the process of IMRT
treatment planning. It determines to what degree one can achieve a good
treatment plan quality in the subsequent plan optimization process. In this
paper, we have developed a BOO algorithm via adaptive l_1 minimization.
Specifically, we introduce a sparsity energy function term into our model which
contains weighting factors for each beam angle adaptively adjusted during the
optimization process. Such an energy term favors small number of beam angles.
By optimizing a total energy function containing a dosimetric term and the
sparsity term, we are able to identify the unimportant beam angles and
gradually remove them without largely sacrificing the dosimetric objective. In
one typical prostate case, the convergence property of our algorithm, as well
as the how the beam angles are selected during the optimization process, is
demonstrated. Fluence map optimization (FMO) is then performed based on the
optimized beam angles. The resulted plan quality is presented and found to be
better than that obtained from unoptimized (equiangular) beam orientations. We
have further systematically validated our algorithm in the contexts of 5-9
coplanar beams for 5 prostate cases and 1 head and neck case. For each case,
the final FMO objective function value is used to compare the optimized beam
orientations and the equiangular ones. It is found that, our BOO algorithm can
lead to beam configurations which attain lower FMO objective function values
than corresponding equiangular cases, indicating the effectiveness of our BOO
algorithm.Comment: 19 pages, 2 tables, and 5 figure
Technical Note: Enhancing Soft Tissue Contrast And RadiationâInduced Image Changes With DualâEnergy CT For Radiation Therapy
Purpose
The purpose of this work is to investigate the use of lowâenergy monoenergetic decompositions obtained from dualâenergy CT (DECT) to enhance image contrast and the detection of radiationâinduced changes of CT textures in pancreatic cancer. Methods
The DECT data acquired for 10 consecutive pancreatic cancer patients during routine nongated CTâguided radiation therapy (RT) using an inâroom CT (Definition AS Open, Siemens Healthcare, Malvern, PA) were analyzed. With a sequential DE protocol, the scanner rapidly performs two helical acquisitions, the first at a tube voltage of 80 kVp and the second at a tube voltage of 140 kVp. Virtual monoenergetic images across a range of energies from 40 to 140 keV were reconstructed using an imageâbased material decomposition. Intravenous (IV) bolusâfree contrast enhancement in pancreas patient tumors was measured across a spectrum of monoenergies. For treatment response assessment, the changes in CT histogram features (including mean CT number (MCTN), entropy, kurtosis) in pancreas tumors were measured during treatment. The results from the monoenergetic decompositions were compared to those obtained from the standard 120 kVp CT protocol for the same subjects. Results
Data of monoenergetic decompositions of the 10 patients confirmed the expected enhancement of soft tissue contrast as the energy is decreased. The changes in the selected CT histogram features in the pancreas during RT delivery were amplified with the lowâenergy monoenergetic decompositions, as compared to the changes measured from the 120 kVp CTs. For the patients studied, the average reduction in the MCTN in pancreas from the first to the last (the 28th) treatment fraction was 4.09 HU for the standard 120 kVp and 11.15 HU for the 40 keV monoenergetic decomposition. Conclusions
Lowâenergy monoenergetic decompositions from DECT substantially increase soft tissue contrast and increase the magnitude of radiationâinduced changes in CT histogram textures during RT delivery for pancreatic cancer. Therefore, quantitative DECT may assist the detection of early RT response
GPU-based Iterative Cone Beam CT Reconstruction Using Tight Frame Regularization
X-ray imaging dose from serial cone-beam CT (CBCT) scans raises a clinical
concern in most image guided radiation therapy procedures. It is the goal of
this paper to develop a fast GPU-based algorithm to reconstruct high quality
CBCT images from undersampled and noisy projection data so as to lower the
imaging dose. For this purpose, we have developed an iterative tight frame (TF)
based CBCT reconstruction algorithm. A condition that a real CBCT image has a
sparse representation under a TF basis is imposed in the iteration process as
regularization to the solution. To speed up the computation, a multi-grid
method is employed. Our GPU implementation has achieved high computational
efficiency and a CBCT image of resolution 512\times512\times70 can be
reconstructed in ~5 min. We have tested our algorithm on a digital NCAT phantom
and a physical Catphan phantom. It is found that our TF-based algorithm is able
to reconstrct CBCT in the context of undersampling and low mAs levels. We have
also quantitatively analyzed the reconstructed CBCT image quality in terms of
modulation-transfer-function and contrast-to-noise ratio under various scanning
conditions. The results confirm the high CBCT image quality obtained from our
TF algorithm. Moreover, our algorithm has also been validated in a real
clinical context using a head-and-neck patient case. Comparisons of the
developed TF algorithm and the current state-of-the-art TV algorithm have also
been made in various cases studied in terms of reconstructed image quality and
computation efficiency.Comment: 24 pages, 8 figures, accepted by Phys. Med. Bio
Compressed Sensing Based Reconstruction Algorithm for X-ray Dose Reduction in Synchrotron Source Micro Computed Tomography
Synchrotron computed tomography requires a large number of angular projections to reconstruct tomographic images with high resolution for detailed and accurate diagnosis. However, this exposes the specimen to a large amount of x-ray radiation. Furthermore, this increases scan time and, consequently, the likelihood of involuntary specimen movements. One approach for decreasing the total scan time and radiation dose is to reduce the number of projection views needed to reconstruct the images. However, the aliasing artifacts appearing in the image due to the reduced number of projection data, visibly degrade the image quality. According to the compressed sensing theory, a signal can be accurately reconstructed from highly undersampled data by solving an optimization problem, provided that the signal can be sparsely represented in a predefined transform domain. Therefore, this thesis is mainly concerned with designing compressed sensing-based reconstruction algorithms to suppress aliasing artifacts while preserving spatial resolution in the resulting reconstructed image. First, the reduced-view synchrotron computed tomography reconstruction is formulated as a total variation regularized compressed sensing problem. The Douglas-Rachford Splitting and the randomized Kaczmarz methods are utilized to solve the optimization problem of the compressed sensing formulation.
In contrast with the first part, where consistent simulated projection data are generated for image reconstruction, the reduced-view inconsistent real ex-vivo synchrotron absorption contrast micro computed tomography bone data are used in the second part. A gradient regularized compressed sensing problem is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The wavelet image denoising algorithm is used as the post-processing algorithm to attenuate the unwanted staircase artifact generated by the reconstruction algorithm.
Finally, a noisy and highly reduced-view inconsistent real in-vivo synchrotron phase-contrast computed tomography bone data are used for image reconstruction. A combination of prior image constrained compressed sensing framework, and the wavelet regularization is formulated, and the Douglas-Rachford Splitting and the preconditioned conjugate gradient methods are utilized to solve the optimization problem of the compressed sensing formulation. The prior image constrained compressed sensing framework takes advantage of the prior image to promote the sparsity of the target image. It may lead to an unwanted staircase artifact when applied to noisy and texture images, so the wavelet regularization is used to attenuate the unwanted staircase artifact generated by the prior image constrained compressed sensing reconstruction algorithm.
The visual and quantitative performance assessments with the reduced-view simulated and real computed tomography data from canine prostate tissue, rat forelimb, and femoral cortical bone samples, show that the proposed algorithms have fewer artifacts and reconstruction errors than other conventional reconstruction algorithms at the same x-ray dose
Full modelling of high-intensity focused ultrasound and thermal heating in the kidney using realistic patient models
Objective: High-intensity focused ultrasound (HIFU) therapy can be used for
non-invasive treatment of kidney (renal) cancer, but the clinical outcomes have
been variable. In this study, the efficacy of renal HIFU therapy was studied
using nonlinear acoustic and thermal simulations in three patients. Methods:
The acoustic simulations were conducted with and without refraction in order to
investigate its effect on the shape, size and pressure distribution at the
focus. The values for the attenuation, sound speed, perfusion and thermal
conductivity of the kidney were varied over the reported ranges to determine
the effect of variability on heating. Furthermore, the phase aberration was
studied in order to quantify the underlying phase shifts using a second order
polynomial function. Results: The ultrasound field intensity was found to drop
on average 11.1 dB with refraction and 6.4 dB without refraction. Reflection at
tissue interfaces was found to result in a loss less than 0.1 dB. Focal point
splitting due to refraction significantly reduced the heating efficacy.
Perfusion did not have a large effect on heating during short sonication
durations. Small changes in temperature were seen with varying attenuation and
thermal conductivity, but no visible changes were present with sound speed
variations. The aberration study revealed an underlying trend in the spatial
distribution of the phase shifts. Conclusion: The results show that the
efficacy of HIFU therapy in the kidney could be improved with aberration
correction. Significance: A method is proposed by which patient specific
pre-treatment calculations could be used to overcome the aberration and
therefore make ultrasound treatment possible.Comment: Journal paper, IEEE Transactions on Biomedical Engineering (2018
Patient-specific stopping power calibration for proton therapy planning based on single-detector proton radiography.
A simple robust optimizer has been developed that can produce patient-specific calibration curves to convert x-ray computed tomography (CT) numbers to relative stopping powers (HU-RSPs) for proton therapy treatment planning. The difference between a digitally reconstructed radiograph water-equivalent path length (DRRWEPL) map through the x-ray CT dataset and a proton radiograph (set as the ground truth) is minimized by optimizing the HU-RSP calibration curve. The function of the optimizer is validated with synthetic datasets that contain no noise and its robustness is shown against CT noise. Application of the procedure is then demonstrated on a plastic and a real tissue phantom, with proton radiographs produced using a single detector. The mean errors using generic/optimized calibration curves between the DRRWEPL map and the proton radiograph were 1.8/0.4% for a plastic phantom and -2.1/ - 0.2% for a real tissue phantom. It was then demonstrated that these optimized calibration curves offer a better prediction of the water equivalent path length at a therapeutic depth. We believe that these promising results are suggestive that a single proton radiograph could be used to generate a patient-specific calibration curve as part of the current proton treatment planning workflow
Characterisation and evaluation of a novel transmission detector for intra-fraction monitoring of radiotherapy.
The goal of this thesis was to characterise a novel transmission detector in the context of signal prediction. This was to eliminate the need to collect a baseline signal for the device before treatment. This not only saves time, but, by independently generating the baseline signal, the process is less prone to missing errors.
A simple analytical algorithm was designed and was found to be capable of detecting gross errors, however, it was shown not to be accurate enough to detect MLC position errors that could have a clinical effect on the delivery. MU check software was commissioned, however the fluence distribution it produced lacked the complexity for accurate signal prediction. A Monte Carlo model of a linac was built and validated then used to demonstrate that the detector could be modelled as two slabs of Perspex; the signal being proportional to the dose measured in the air between them. Two Monte Carlo models were then made using different systems, these were both evaluated by comparing predicted signals to measured signals for VMAT plans. Both models performed well and were capable of detecting leaf errors ~1mm; the merits of both are discussed with regard to error detection and ease of use
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