Registration accuracy for MR images of the prostate using a subvolume based registration protocol

<p>Abstract</p> <p>Background</p> <p>In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.</p> <p>Methods</p> <p>Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.</p> <p>Results</p> <p>We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.</p> <p>Conclusions</p> <p>Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.</p

Applications of statistical methods in quantitative magnetic resonance imaging

Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI). DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners. DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start. However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways. In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis

Using radial k-space sampling and temporal filters in MRI to improve temporal resolution

In this master thesis methods for increasing temporal resolution when reconstructing radially sampled MRI data have been developed and evaluated. This has been done in two steps; first the order in which data is sampled in k-space has been optimized, and second; temporal filters have been developed in order to utilize the high sampling density in central regions of k-space as a result of the polar sampling geometry to increase temporal resolution while maintaining image quality.By properly designing the temporal filters the temporal resolution is increased by a factor 3–20 depending on other variables such as imageresolution and the size of the time varying areas in the image. The results are obtained from simulated raw data and subsequent reconstruction. The next step should be to acquire and reconstruct raw data to confirm the results.This Master thesis work was performed at Dept. Radiation Physis, Linköping University, but examined at Dept. Radiation Physics, Umeå Universit

Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI

The purpose of this work is to investigate spatial statistical modelling approaches to improve contrast agent quantification in dynamic contrast enhanced MRI, by utilising the spatial dependence among image voxels. Bayesian hierarchical models (BHMs), such as Besag model and Leroux model, were studied using simulated MRI data. The models were built on smaller images where spatial dependence can be incorporated, and then extended to larger images using the maximum a posteriori (MAP) method. Notable improvements on contrast agent concentration estimation were obtained for both smaller and larger images. For smaller images: the BHMs provided substantial improved estimates in terms of the root mean squared error (rMSE), compared to the estimates from the existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperformed Besag models with two different dependence structures. Specifically, the Besag models increased the estimation precision by 27% around the peak of the dynamic curve, while the Leroux model improved the estimation by 40% at the peak, compared with the existing estimation method. For larger images: the proposed MAP estimators showed clear improvements on rMSE for vessels, tumor rim and white matter.Originally included in thesis in manuscript form.</p

Gray-level invariant Haralick texture features

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.Originally included in thesis in manuscript form</p

3D Magnetic Resonance Imaging of the Human Brain - Novel Radial Sampling, Filtering and Reconstruction

We have suggested a novel method PRESTO-CAN including radial sampling, filtering and reconstruction of k-space data for 3D-plus-time resolved MRI. The angular increment of the profiles was based on the golden ratio, but the number of angular positions N was locked to be a prime number which guaranteed fix angle positions.The time resolution increased dramatically when the pro-files were partly removed from the k-space using the hourglass filter.We aim for utilizing the MRI-data for fMRI, where the echo times are long, TE ≈ 37-40 ms. This will result in field inhomogeneities and phase variations in the reconstructed images. Therefore, a new calibration and correction procedure was developed. We show that we are able to reconstruct images of the human brain with an image quality in line with what can be obtained by conventional Cartesian sampling.The pulse sequence for PRESTO-CAN was implemented by modifying an existing PRESTO sequence for Cartesian sampling. The effort involved was relatively small and a great advantage will be that we are able to use standard procedures for speeding up data acquisition, i.e. parallel imaging with SENSE

Combining Phase and Magnitude Information for Contrast Agent Quantification in Dynamic Contrast-Enhanced MRI Using Statistical Modeling

Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI. Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T. Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter. Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo. (C) 2014 Wiley Periodicals, Inc

3D Magnetic Resonance Imaging of the Human Brain - Novel Radial Sampling, Filtering and Reconstruction

We have suggested a novel method PRESTO-CAN including radial sampling, filtering and reconstruction of k-space data for 3D-plus-time resolved MRI. The angular increment of the profiles was based on the golden ratio, but the number of angular positions N was locked to be a prime number which guaranteed fix angle positions.The time resolution increased dramatically when the pro-files were partly removed from the k-space using the hourglass filter.We aim for utilizing the MRI-data for fMRI, where the echo times are long, TE ≈ 37-40 ms. This will result in field inhomogeneities and phase variations in the reconstructed images. Therefore, a new calibration and correction procedure was developed. We show that we are able to reconstruct images of the human brain with an image quality in line with what can be obtained by conventional Cartesian sampling.The pulse sequence for PRESTO-CAN was implemented by modifying an existing PRESTO sequence for Cartesian sampling. The effort involved was relatively small and a great advantage will be that we are able to use standard procedures for speeding up data acquisition, i.e. parallel imaging with SENSE

Dissecting Motor and Cognitive Component Processes of a Finger-Tapping Task With Hybrid Dopamine Positron Emission Tomography and Functional Magnetic Resonance Imaging

Striatal dopamine is involved in facilitation of motor action as well as various cognitive and emotional functions. Positron emission tomography (PET) is the primary imaging method used to investigate dopamine function in humans. Previous PET studies have shown striatal dopamine release during simple finger tapping in both the putamen and the caudate. It is likely that dopamine release in the putamen is related to motor processes while dopamine release in the caudate could signal sustained cognitive component processes of the task, but the poor temporal resolution of PET has hindered firm conclusions. In this study we simultaneously collected [11C]Raclopride PET and functional Magnetic Resonance Imaging (fMRI) data while participants performed finger tapping, with fMRI being able to isolate activations related to individual tapping events. The results revealed fMRI-PET overlap in the bilateral putamen, which is consistent with a motor component process. Selective PET responses in the caudate, ventral striatum, and right posterior putamen, were also observed but did not overlap with fMRI responses to tapping events, suggesting that these reflect non-motor component processes of finger tapping. Our findings suggest an interplay between motor and non-motor-related dopamine release during simple finger tapping and illustrate the potential of hybrid PET-fMRI in revealing distinct component processes of cognitive functions