Extension of the angular spectrum method to model the pressure field of a cylindrically curved array transducer.

An extension to the angular spectrum approach for modelling pressure fields of a cylindrically curved array transducer is described in this paper. The proposed technique is based on representing the curved transducer surface as a set of planar elements whose contributions are combined at a selected intermediate plane from which the field is further propagated using the conventional angular spectrum approach. The accuracy of the proposed technique is validated through comparison with Field II simulations.

Fast, accurate, and robust automatic marker detection for motion correction based on oblique kV or MV projection image pairs.

PURPOSE: A robust and accurate method that allows the automatic detection of fiducial markers in MV and kV projection image pairs is proposed. The method allows to automatically correct for inter or intrafraction motion. METHODS: Intratreatment MV projection images are acquired during each of five treatment beams of prostate cancer patients with four implanted fiducial markers. The projection images are first preprocessed using a series of marker enhancing filters. 2D candidate marker locations are generated for each of the filtered projection images and 3D candidate marker locations are reconstructed by pairing candidates in subsequent projection images. The correct marker positions are retrieved in 3D by the minimization of a cost function that combines 2D image intensity and 3D geometric or shape information for the entire marker configuration simultaneously. This optimization problem is solved using dynamic programming such that the globally optimal configuration for all markers is always found. Translational interfraction and intrafraction prostate motion and the required patient repositioning is assessed from the position of the centroid of the detected markers in different MV image pairs. The method was validated on a phantom using CT as ground-truth and on clinical data sets of 16 patients using manual marker annotations as ground-truth. RESULTS: The entire setup was confirmed to be accurate to around 1 mm by the phantom measurements. The reproducibility of the manual marker selection was less than 3.5 pixels in the MV images. In patient images, markers were correctly identified in at least 99% of the cases for anterior projection images and 96% of the cases for oblique projection images. The average marker detection accuracy was 1.4 +/- 1.8 pixels in the projection images. The centroid of all four reconstructed marker positions in 3D was positioned within 2 mm of the ground-truth position in 99.73% of all cases. Detecting four markers in a pair of MV images takes a little less than a second where most time is spent on the image preprocessing. CONCLUSIONS: The authors have developed a method to automatically detect multiple markers in a pair of projection images that is robust, accurate, and sufficiently fast for clinical use. It can be used for kV, MV, or mixed image pairs and can cope with limited motion between the projection images.

A semi-automated 2D/3D marker-based registration algorithm modelling prostate shrinkage during radiotherapy for prostate cancer.

BACKGROUND AND PURPOSE: Currently, most available patient alignment tools based on implanted markers use manual marker matching and rigid registration transformations to measure the needed translational shifts. To quantify the particular effect of prostate gland shrinkage, implanted gold markers were tracked during a course of radiotherapy including an isotropic scaling factor to model prostate shrinkage. MATERIALS AND METHODS: Eight patients with prostate cancer had gold markers implanted transrectally and seven were treated with (neo) adjuvant androgen deprivation therapy. After patient alignment to skin tattoos, orthogonal electronic portal images (EPIs) were taken. A semi-automated 2D/3D marker-based registration was performed to calculate the necessary couch shifts. The registration consists of a rigid transformation combined with an isotropic scaling to model prostate shrinkage. RESULTS: The inclusion of an isotropic shrinkage model in the registration algorithm cancelled the corresponding increase in registration error. The mean scaling factor was 0.89+/-0.09. For all but two patients, a decrease of the isotropic scaling factor during treatment was observed. However, there was almost no difference in the translation offset between the manual matching of the EPIs to the digitally reconstructed radiographs and the semi-automated 2D/3D registration. A decrease in the intermarker distance was found correlating with prostate shrinkage rather than with random marker migration. CONCLUSIONS: Inclusion of shrinkage in the registration process reduces registration errors during a course of radiotherapy. Nevertheless, this did not lead to a clinically significant change in the proposed table translations when compared to translations obtained with manual marker matching without a scaling correction.

Attenuation estimation by repeatedly solving the forward scattering problem.

Estimation of the attenuation is important in medical ultrasound not only for correct time-gain compensation but also for tissue characterization. In this paper, the feasibility of a new method for attenuation estimation is tested. The proposed method estimates the attenuation by repeatedly solving the forward wave propagation problem and matching the simulated signals to the measured ones. This approach allows avoiding common assumptions made by other methodologies and potentially allows to account and correct for other acoustic effects that may bias the attenuation estimate. The performance of the method was validated on simulated data and on data recorded in tissue mimicking phantoms with known attenuation properties, and was compared to the spectral-shift and spectral-difference methods. Simulation results showed the different methods to have good accuracy when noise-free signals were considered (the average relative error of the attenuation estimation did not exceed 15%). However, the accuracy of the conventional methods decreased rapidly in the presence of measurement noise and varying scatterer concentration, while the relative error of the proposed method remained below 15%. Furthermore, the proposed method outperformed conventional attenuation estimators in the experimental phantom study, where its average error was 8%, while the average error of the spectral-shift and spectral-difference methods was 26% and 32%, respectively. In summary, these findings demonstrate the feasibility of the proposed approach and motivate us to refine the method for solving more general problems.

Anatomical Image Registration Using Volume Conservation to Assess Cardiac Deformation From 3D Ultrasound Recordings.

Myocardial deformation imaging can provide valuable insights in myocardial mechanics and help in the diagnosis, prognosis and follow-up of cardiac diseases. However, extracting these indices in 3D is challenging due to the limitations in spatial and temporal resolution of the current volumetric ultrasound systems. For this purpose, we developed an anatomical free-form deformation image registration framework which is locally adapted to the anatomy of the heart. In this work we explored whether incorporating a myocardial volume conservation regularizer would improve strain estimates. We evaluated our technique on in silico echo sequences featuring realistic speckle textures and showed the volume conservation regularizer to be beneficial in reducing strain errors further when used in combination with a smoothness penalty. This combination led to more physiological boundary conditions. It also made distinguishing ischemic from normal segments easier in clinical images.

Delay and Standard Deviation Beamforming to Enhance Specular Reflections in Ultrasound Imaging.

Although interventional devices, such as needles, guide wires, and catheters, are best visualized by X-ray, real-time volumetric echography could offer an attractive alternative as it avoids ionizing radiation; it provides good soft tissue contrast, and it is mobile and relatively cheap. Unfortunately, as echography is traditionally used to image soft tissue and blood flow, the appearance of interventional devices in conventional ultrasound images remains relatively poor, which is a major obstacle toward ultrasound-guided interventions. The objective of this paper was therefore to enhance the appearance of interventional devices in ultrasound images. Thereto, a modified ultrasound beamforming process using conventional-focused transmit beams is proposed that exploits the properties of received signals containing specular reflections (as arising from these devices). This new beamforming approach referred to as delay and standard deviation beamforming (DASD) was quantitatively tested using simulated as well as experimental data using a linear array transducer. Furthermore, the influence of different imaging settings (i.e., transmit focus, imaging depth, and scan angle) on the obtained image contrast was evaluated. The study showed that the image contrast of specular regions improved by 5-30 dB using DASD beamforming compared with traditional delay and sum (DAS) beamforming. The highest gain in contrast was observed when the interventional device was tilted away from being orthogonal to the transmit beam, which is a major limitation in standard DAS imaging. As such, the proposed beamforming methodology can offer an improved visualization of interventional devices in the ultrasound image with potential implications for ultrasound-guided interventions.

Unsupervised segmentation, clustering, and groupwise registration of heterogeneous populations of brain MR images.

Population analysis of brain morphology from magnetic resonance images contributes to the study and understanding of neurological diseases. Such analysis typically involves segmentation of a large set of images and comparisons of these segmentations between relevant subgroups of images (e.g., "normal" versus "diseased"). The images of each subgroup are usually selected in advance in a supervised way based on clinical knowledge. Their segmentations are typically guided by one or more available atlases, assumed to be suitable for the images at hand. We present a data-driven probabilistic framework that simultaneously performs atlas-guided segmentation of a heterogeneous set of brain MR images and clusters the images in homogeneous subgroups, while constructing separate probabilistic atlases for each cluster to guide the segmentation. The main benefits of integrating segmentation, clustering and atlas construction in a single framework are that: 1) our method can handle images of a heterogeneous group of subjects and automatically identifies homogeneous subgroups in an unsupervised way with minimal prior knowledge, 2) the subgroups are formed by automatical detection of the relevant morphological features based on the segmentation, 3) the atlases used by our method are constructed from the images themselves and optimally adapted for guiding the segmentation of each subgroup, and 4) the probabilistic atlases represent the morphological pattern that is specific for each subgroup and expose the groupwise differences between different subgroups. We demonstrate the feasibility of the proposed framework and evaluate its performance with respect to image segmentation, clustering and atlas construction on simulated and real data sets including the publicly available BrainWeb and ADNI data. It is shown that combined segmentation and atlas construction leads to improved segmentation accuracy. Furthermore, it is demonstrated that the clusters generated by our unsupervised framework largely coincide with the clinically determined subgroups in case of disease-specific differences in brain morphology and that the differences between the cluster-specific atlases are in agreement with the expected disease-specific patterns, indicating that our method is capable of detecting the different modes in a population. Our method can thus be seen as a comprehensive image-driven population analysis framework that can contribute to the detection of novel subgroups and distinctive image features, potentially leading to new insights in the brain development and disease.

Intrafractional prostate motion during online image guided intensity-modulated radiotherapy for prostate cancer.

INTRODUCTION: Intrafractional motion consists of two components: (1) the movement between the on-line repositioning procedure and the treatment start and (2) the movement during the treatment delivery. The goal of this study is to estimate this intrafractional movement of the prostate during prostate cancer radiotherapy. MATERIAL AND METHODS: Twenty-seven patients with prostate cancer and implanted fiducials underwent a marker match procedure before a five-field IMRT treatment. For all fields, in-treatment images were obtained and then processed to enable automatic marker detection. Combining the subsequent projection images, five positions of each marker were determined using the shortest path approach. The residual set-up error (RSE) after kV-MV based prostate localization, the prostate position as a function of time during a radiotherapy session and the required margins to account for intrafractional motion were determined. RESULTS: The mean RSE and standard deviation in the antero-posterior, cranio-caudal and left-right direction were 2.3±1.5 mm, 0.2±1.1 mm and -0.1±1.1 mm, respectively. Almost all motions occurred in the posterior direction before the first treatment beam as the percentage of excursions>5 mm was reduced significantly when the RSE was not accounted for. The required margins for intrafractional motion increased with prolongation of the treatment. Application of a repositioning protocol after every beam could decrease the 1cm margin from CTV to PTV by 2 mm. CONCLUSIONS: The RSE is the main contributor to intrafractional motion. This RSE after on-line prostate localization and patient repositioning in the posterior direction emphasizes the need to speed up the marker match procedure. Also, a prostate IMRT treatment should be administered as fast as possible, to ensure that the pre-treatment repositioning efforts are not erased by intrafractional prostate motion. This warrants an optimized workflow with the use of faster treatment techniques.

Automatic 3-D breath-hold related motion correction of dynamic multislice MRI.

Magnetic resonance (MR) cine images are often used to clinically assess left ventricular cardiac function. In a typical study, multiple 2-D long axis (LA) and short axis (SA) cine images are acquired, each in a different breath-hold. Differences in lung volume during breath-hold and overall patient motion distort spatial alignment of the images thus complicating spatial integration of all image data in three dimensions. We present a fully automatic postprocessing approach to correct these slice misalignments. The approach is based on the constrained optimization of the intensity similarity of intersecting image lines after the automatic definition of a region of interest. It uses all views and all time frames simultaneously. Our method models both in-plane and out-of-plane translations and full 3-D rotations, can be applied retrospectively and does not require a cardiac wall segmentation. The method was validated on both healthy volunteer and patient data with simulated misalignments, as well as on clinical multibreath-hold patient data. For the simulated data, subpixel accuracy could be obtained using translational correction. The possibilities and limitations of rotational correction were investigated and discussed. For the clinical multibreath-hold patient data sets, the median discrepancy between manual SA and LA contours was reduced from 2.83 to 1.33 mm using the proposed correction method. We have also shown the usefulness of the correction method for functional analysis on clinical image data. The same clinical multibreath-hold data sets were resegmented after positional correction, taking newly available complementary information of intersecting slices into account, further reducing the median discrepancy to 0.43 mm. This is due to the integration of the 2-D slice information into 3-D space.