Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Range of motion in anterior femoroacetabular impingement

The range of motion of normal hips and hips with femoroacetabular impingement relative to some specific anatomic reference landmarks is unknown. We therefore described: (1) the range of motion pattern relative to landmarks; (2) the location of the impingement zones in normal and impinging hips; and (3) the influence of surgical débridement on the range of motion. We used a previously developed and validated noninvasive 3-D CT-based method for kinematic hip analysis to compare the range of motion pattern, the location of impingement, and the effect of virtual surgical reconstruction in 28 hips with anterior femoroacetabular impingement and a control group of 33 normal hips. Hips with femoroacetabular impingement had decreased flexion, internal rotation, and abduction. Internal rotation decreased with increasing flexion and adduction. The calculated impingement zones were localized in the anterosuperior quadrant of the acetabulum and were similar in the two groups and in impingement subgroups. The average improvement of internal rotation was 5.4 degrees for pincer hips, 8.5 degrees for cam hips, and 15.7 degrees for mixed impingement. This method helps the surgeon quantify the severity of impingement and choose the appropriate treatment option; it provides a basis for future image-guided surgical reconstruction in femoroacetabular impingement with less invasive techniques.

Application and assessment of a robust elastic motion correction algorithm to dynamic MRI

The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).

An integral conservative gridding--algorithm using Hermitian curve interpolation

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

Altered perception of apparent motion in schizophrenia spectrum disorder

Apparent motion (AM), the Gestalt perception of motion in the absence of physical motion, was used to study perceptual organization and neurocognitive binding in schizophrenia. Associations between AM perception and psychopathology as well as meaningful subgroups were sought. Circular and stroboscopic AM stimuli were presented to 68 schizophrenia spectrum patients and healthy participants. Psychopathology was measured using the Positive and Negative Syndrome Scale (PANSS). Psychopathology was related to AM perception differentially: Positive and disorganization symptoms were linked to reduced gestalt stability; negative symptoms, excitement and depression had opposite regression weights. Dimensions of psychopathology thus have opposing effects on gestalt perception. It was generally found that AM perception was closely associated with psychopathology. No difference existed between patients and controls, but two latent classes were found. Class A members who had low levels of AM stability made up the majority of inpatients and control subjects; such participants were generally young and male, with short reaction times. Class B typically contained outpatients and some control subjects; participants in class B were older and showed longer reaction times. Hence AM perceptual dysfunctions are not specific for schizophrenia, yet AM may be a promising stage marker.