928 research outputs found
Fraction-variant beam orientation optimization for non-coplanar IMRT
Conventional beam orientation optimization (BOO) algorithms for IMRT assume
that the same set of beam angles is used for all treatment fractions. In this
paper we present a BOO formulation based on group sparsity that simultaneously
optimizes non-coplanar beam angles for all fractions, yielding a
fraction-variant (FV) treatment plan. Beam angles are selected by solving a
multi-fraction FMO problem involving 500-700 candidate beams per fraction, with
an additional group sparsity term that encourages most candidate beams to be
inactive. The optimization problem is solved using the Fast Iterative
Shrinkage-Thresholding Algorithm. Our FV BOO algorithm is used to create
non-coplanar, five-fraction treatment plans for prostate and lung cases, as
well as a non-coplanar 30-fraction plan for a head and neck case. A homogeneous
PTV dose coverage is maintained in all fractions. The treatment plans are
compared with fraction-invariant plans that use a fixed set of beam angles for
all fractions. The FV plans reduced mean and max OAR dose on average by 3.3%
and 3.7% of the prescription dose, respectively. Notably, mean OAR dose was
reduced by 14.3% of prescription dose (rectum), 11.6% (penile bulb), 10.7%
(seminal vesicle), 5.5% (right femur), 3.5% (bladder), 4.0% (normal left lung),
15.5% (cochleas), and 5.2% (chiasm). Max OAR dose was reduced by 14.9% of
prescription dose (right femur), 8.2% (penile bulb), 12.7% (prox. bronchus),
4.1% (normal left lung), 15.2% (cochleas), 10.1% (orbits), 9.1% (chiasm), 8.7%
(brainstem), and 7.1% (parotids). Meanwhile, PTV homogeneity defined as D95/D5
improved from .95 to .98 (prostate case) and from .94 to .97 (lung case), and
remained constant for the head and neck case. Moreover, the FV plans are
dosimetrically similar to conventional plans that use twice as many beams per
fraction. Thus, FV BOO offers the potential to reduce delivery time for
non-coplanar IMRT
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Performance Comparison of Knowledge-Based Dose Prediction Techniques Based on Limited Patient Data.
PurposeThe accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data.MethodsStatistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy.ResultsStatistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (-43.9%) and support vector regression (-42.8%) and lung support vector regression (-24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases.ConclusionCompared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method
Methods for comparative ChIA-PET and Hi-C data analysis.
The three-dimensional architecture of chromatin in the nucleus is important for genome regulation and function. Advanced high-throughput sequencing-based methods have been developed for capturing chromatin interactions (Hi-C, genome-wide chromosome conformation capture) or enriching for those involving a specific protein (ChIA-PET, chromatin interaction analysis with paired-end tag sequencing). There is widespread interest in utilizing and interpreting ChIA-PET and Hi-C. We review methods for comparative ChIA-PET and Hi-C data analysis and visualization. The topics reviewed include: downloading ChIA-PET and Hi-C data from the ENCODE and 4DN portals; processing ChIA-PET data using ChIA-PIPE; processing Hi-C data using Juicer or distiller and cooler; viewing 2D contact maps using Juicebox or Higlass; viewing peaks, loops, and domains using BASIC Browser; annotating convergent and tandem CTCF loops
Discontinuity Preserving Regularization for Modeling Sliding in Medical Image Registration
Sliding effects often occur along tissue/organ boundaries. However, most conventional registration techniques either use smooth parametric bases or apply homogeneous smoothness regularization, and fail to address the sliding issue. In this study, we propose a class of discontinuity-preserving regularizers that fit naturally into optimization-based registration. The proposed regularization encourages smooth deformations in most regions, but preserves large discontinuities supported by the data. Variational techniques are used to derive the descending flows. We discuss general conditions on such discontinuity-preserving regularizers, and their properties based on an anisotropic filtering interpretation. Preliminary tests with 2D CT data show promising results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85986/1/Fessler234.pd
Discriminative Sliding Preserving Regularization in Medical Image Registration
Sliding effects often occur along tissue/organ boundaries. For instance, it is widely observed that the lung and diaphragm slide against the rib cage and the atria during breathing. Conventional homogeneous smooth registration methods fail to address this issue. Some recent studies preserve motion discontinuities by either using joint registration/segmentation or utilizing robust regularization energy on the motion field. However, allowing all types of discontinuities is not strict enough for physical deformations. In particular, flows that generate local vacuums or mass collisions should be discouraged by the energy functional. In this study, we propose a regularization energy that encodes a discriminative treatment of different types of motion discontinuities. The key idea is motivated by the Helmholtz-Hodge decomposition, and regards the underlying motion flow as a superposition of a solenoidal component, an irrotational component and a harmonic part. The proposed method applies a homogeneous penalty on the divergence, discouraging local volume change caused by the irrotational component, thus avoiding local vacuum or collision; it regularizes the curl field with a robust functional so that the resulting solenoidal component vanishes almost everywhere except on a singular set where the large shear values are preserved. This singularity set corresponds to sliding interfaces. Preliminary tests with both simulated and clinical data showed promising results.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85988/1/Fessler242.pd
Real-time prediction of respiratory motion based on local regression methods
Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58097/2/pmb7_23_024.pd
2D antiscatter grid and scatter sampling based CBCT pipeline for image guided radiation therapy
Poor tissue visualization and quantitative accuracy in CBCT is a major
barrier in expanding the role of CBCT imaging from target localization to
quantitative treatment monitoring and plan adaptations in radiation therapy
sessions. To further improve image quality in CBCT, 2D antiscatter grid based
scatter rejection was combined with a raw data processing pipeline and
iterative image reconstruction. The culmination of these steps was referred as
quantitative CBCT, qCBCT. qCBCT data processing steps include 2D antiscatter
grid implementation, measurement based residual scatter, image lag, and beam
hardening correction for offset detector geometry CBCT with a bow tie filter.
Images were reconstructed with iterative image reconstruction to reduce image
noise. To evaluate image quality, qCBCT acquisitions were performed using a
variety of phantoms to investigate the effect of object size and its
composition on image quality. qCBCT image quality was benchmarked against
clinical CBCT and MDCT images. Addition of image lag and beam hardening
correction to scatter suppression reduced HU degradation in qCBCT by 10 HU and
40 HU, respectively. When compared to gold standard MDCT, mean HU errors in
qCBCT and clinical CBCT were 10 HU and 27 HU, respectively. HU inaccuracy due
to change in phantom size was 22 HU and 85 HU in qCBCT and clinical CBCT
images, respectively. With iterative reconstruction, contrast to noise ratio
improved by a factor of 1.25 when compared to clinical CBCT protocols. Robust
artifact and noise suppression in qCBCT images can reduce the image quality gap
between CBCT and MDCT, improving the promise of qCBCT in fulfilling the tasks
that demand high quantitative accuracy, such as CBCT based dose calculations
and treatment response assessment in image guided radiation therapy
Image Guided Respiratory Motion Analysis: Time Series and Image Registration.
The efficacy of Image guided radiation therapy (IGRT) systems relies on accurately extracting, modeling and predicting tumor movement with imaging techniques. This thesis
investigates two key problems associated with such systems: motion modeling and image
processing. For thoracic and upper abdominal tumors, respiratory motion is the dominant
factor for tumor movement. We have studied several special structured time series analysis techniques to incorporate the semi-periodicity characteristics of respiratory motion.
The proposed methods are robust towards large variations among fractions and populations; the algorithms perform stably in the presence of sparse radiographic observations
with noise. We have proposed a subspace projection method to quantitatively evaluate the
semi-periodicity of a given observation trace; a nonparametric local regression approach
for real-time prediction of respiratory motion; a state augmentation scheme to model hysteresis; and an ellipse tracking algorithm to estimate the trend of respiratory motion in
real time. For image processing, we have focused on designing regularizations to account
for prior information in image registration problems. We investigated a penalty function design that accommodates tissue-type-dependent elasticity information. We studied a class of discontinuity preserving regularizers that yield smooth deformation estimates
in most regions, yet allow discontinuities supported by data. We have further proposed a
discriminate regularizer that preserves shear discontinuity, but discourages folding or vacuum generating flows. In addition, we have initiated a preliminary principled study on the
fundamental performance limit of image registration problems. We proposed a statistical
generative model to account for noise effect in both source and target images, and investigated the approximate performance of the maximum-likelihood estimator corresponding
to the generative model and the commonly adopted M-estimator. A simple example suggests that the approximation is reasonably accurate.
Our studies in both time series analysis and image registration constitute essential
building-blocks for clinical applications such as adaptive treatment. Besides their theoretical interests, it is our sincere hope that with further justifications, the proposed techniques
would realize its clinical value, and improve the quality of life for patients.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60673/1/druan_1.pd
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