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выполняет предобработку изображений и Проведенные с помощью описанной ранжирование участков среды (выделение выше робототехнической платформы препятствий и проходимых участков, натурные эксперименты подтверждают определение коэффициентов эффективность предлагаемого подхода к проходимости). Сформированная модель реализации нейросетевой системы внешней среды загружается в навигации. Траектория движения робота управляющую нейросеть робота. благодаря использованию свойств поля Ранжирование целей, препятствий и потенциалов становится более гладкой, свободных участков производится по количество ошибок робота при обходе цветовому признаку. препятствий снижается.

Помимо набора камер, сенсорная подсистема разработанной платформы Список литературы может быть оснащена радиальным 1. Чернухин Ю.В. Нейропроцессорные сети. М.:

светочувствительным датчиком.

Изд-во ТРТУ, 1999. 439 с Совмещение свойств светочувствительного 2. С.Рассел, П. Норвиг. Искусственный интеллект.

датчика и камер позволяет строить Современный подход. Издательство Вильямс, 2007г.

биологически инспирированные сенсорные 3. Чернухин Ю.В., Сапрыкин Р.В., Бутов П.А.

системы [4]. Подходы к реализации нейросетевых систем управления интеллектуальными мобильными Для воспроизведения алгоритмов роботами. // Известия ЮФУ. Технические науки:

восприятия и навигации разработанная №1 (114), Таганрог, 2011.

робототехническая платформа содержит 4. Ю.В. Чернухин, Ю.С. Доленко, П.А. Бутов бортовой микрокомпьютер eBox-3300MX и Бионический подход к построению гибридной плату DE0-Nano с ПЛИС Altera Cyclone IV. системы технического зрения для интеллектуальных мобильных роботов. Информационные технологии, На базе ПЛИС реализована система системный анализ и управление. Сборник трудов. - управления сенсорами и эффекторами Таганрог: Изд-во ТТИ ЮФУ, 2011.

платформы. Ресурсы ПЛИС так же могут Материалы XVI Международной конференции по нейрокибернетике QUALITY ESTIMATION OF MOTION CORRECTION FOR PET BRAIN IMAGES S. Anishchenko 1, 2, R. Hui 2, 3, Y.K. Liang 3, Z.M. Tian 3, W.S. Lu 3, R. Comley 2, X. Gao A.B.Kogan Research Institute for Neurocybernetics, Southern Federal University, Rostov-on-Don, Russia, sergey.anishenko@gmail.com School of Engineering and Information Sciences, Middlesex University, London, NW4 4BT, UK, x.gao@mdx.ac.uk Chinese PLA Navy General Hospital, Beijing, China, huirui2002@163.com Patient’s movement during the acquisition of Positron therefore need different strategies for Emission Tomography (PET) can significantly degrade correction. This paper deals with the image quality. Hence, development of a motion spontaneous movement, i.e., the second type correction system is an essential task for preserving of movements.

image information and thereafter improving diagnosis accuracy. Performance evaluation is a required step in Correction can be performed in (1) listsuch a development. In this paper a simulation-based mode or (2) frame-mode. The advantage of technique for evaluation of motion correction of PET list-mode is that erroneous events can be brain images is described, whereas the sum of optical corrected before image reconstruction (thus flow technique has been developed to measure the the reconstruction algorithm does not correction.

accumulate errors). But it was shown that listIntroduction mode correction sometimes can yield image artifacts [1]. Also in most cases it is With the advent of modern imaging impossible to get access to list-mode raw data.

technology, PET has become a more accurate On the contrary, the frame-mode data is procedure and can reach 2mm spatial always available from a PET scanner. An resolutions [1, 3] for brain scanning. However, intra-frame movement cannot be taken into patient’s movement becomes a major factor account in frame-mode, but the frames can be that degrades the image quality. Thus it is reconstructed based on motion-free data, while important to take movements into account and the data corrupted by motion can be discarded.

respectively correct PET-data. As a result, it With this in mind, image correction will benefit PET-based diagnosis considerably algorithms can fall into three groups according and make research more reliable and accurate. to the data used for motion estimation:

In general during the procedure of PET 1. Emission data.

scanning, a few types of motion can occur: 2. Transmission data.

1. Motion caused by the respiratory cycle. 3. External motion tracker.

2. Motion due to patient’s uncontrollable Algorithms developed for the first group medical conditions. process data in frame-mode. One frame should 3. Motion due to the subject’s accidental be chosen as a reference whereas all the others movement. can be realigned with reference to the chosen Each type needs its own specific approach frame. Intra-frame movement cannot be taken to correct images. This research focuses on the into consideration in this case.

third type of movement during the brain The second type of algorithms uses imaging procedure which in turn can fall into transmission data as a reference to realign PET two groups: data in the frame-mode. The main 1. Drifting. Slow changing of head disadvantage of this method is that there is no position. exact metric to compare emission and 2. Spontaneous movement. Fast head transmission data due to their different pose changing. nature [2].

These two types of movement affect the An external motion tracker can be used resulting brain images in different ways and for PET-data correction in both frame- or list4-Й МЕЖДУНАРОДНЫЙ СИМПОЗИУМ «НЕЙРОИНФОРМАТИКА И НЕЙРОКОМПЬЮТЕРЫ» mode. It is an attractive technique and will become the constituent part of high-resolution PET brain scanners in the near future. It is particularly important for dedicated brain scanners. Comparing with multipurpose scanners, it can use smaller detectors and, therefore, produce higher-resolution images [3].

A simulation-based evaluation technique for motion correction of PET brain images is described below. It is assumed that an external motion tracker is used to detect a patient’s head pose.

Experimental setup In this work real PET data was used to perform computer simulation. Data was collected in the PET-CT centre of the Chinese PLA Navy General Hospital during real scan procedures on a GE Discovery ST PET/CT system.

Fig. 1. (1) experimental setup: PET-scanner equipped To capture video clips monitoring a with one camera and one stereo rig to monitor patient’s patient’s head during PET scanning, extra head during scanning; (2) patient’s photo taken from left camera of stereo rig, (3) right camera photo.

equipment (one stereo rig and one photo camera) was installed behind the gantry (Fig.

In this work a set of reconstructed motion1.1).

free PET frames was used to simulate images For the stereo rig, two Genius 1.3 Mpx in the presence of head motion during web cameras were used. A SONY DSC-Hscanning. Furthermore, the simulated images 7.2 Mpx camera was used for the photo rig.

were realigned based on the information from In total, video data was collected for a simulated head tracker taking into account its patients. Each patient at first was scanned precision. The quality of image realigning was using CT (whole body scanning, duration ~ estimated using metrics based on Optical minutes). After that the patient was scanned Flow [4]. Below the computer simulation is using PET (brain scanning only, duration ~ described in detail.

minutes). Such a short period of PET scanning was used because the purpose was to check for Input data metastasis in the brain and 5 min is enough for From the recorded dataset, as described this task. After scanning, the PET data for above, the part (3 minutes scanning of one patients was reconstructed in 2 seconds patient) of motion-free PET data reconstructed frames.

in 2 seconds per frame was extracted. So, Computer Simulation 90 frames were extracted for each slice (n=47).

Consequently, the total number of brain The evaluation of a motion correction images was 4230. Those images were system for PET brain imaging is a challenging converted from DICOM format to bitmap task due to many reasons and one of them is image files (bmp) and saved for further the absence of a “gold standard”. In other processing.

words to check correction precision we need to know how brain images look in both cases Processing with and without motion. It is impossible in a To simulate head rotation, the input real PET scanning procedure but can be done images were rotated to a predefined angle.

using computer simulation.

Материалы XVI Международной конференции по нейрокибернетике Thus, to simulate movement correction using Assumptions the external tracker, the obtained images were There are several assumptions in the realigned using angle which is a random simulation framework described here. The angle generated in the interval from - to + head rotation happens only in one plane and, where is the head tracker precision. The relative to one axis. In real conditions, head parameter corresponds to the head pose movement can happen in any direction and in estimation system precision described in [5] the future it must be taken into account in the and equal to 5 in this research. framework.

Output data Results After processing three sets of PET brain images are available: Two experiments were conducted. In the 1. Initial motionless images. first one the simulated head rotation (=10o) 2. Images obtained by simulation of head was bigger than the motion tracker precision rotation. (=5o). The number of head movements during 3. Images obtained by realigning (2) the scan was eight (top plot in Fig. 3.). The taking into account the performance of SOF for corrected frames was 177244, for the head movement tracking system. uncorrected – 550142 (bottom plot in Fig. 3).

Output data analysis To analyze the quality of the frame realignment an appropriate metric must be used which allows the comparison of two images of the same object in different poses.

Using such a metric, the initial image can be compared with both simulated and realigned images to estimate the difference.

Fig. 3. The top plot shows simulated head rotation over frames (the case when rotation is bigger than head pose Fig. 2. SOF over PET frame rotation degree. SOF grows tracker precision). The bottom plot shows the value of until the rotation reaches 15 degrees. Therefore, it is the metric over frames for both cases corrected (lighter) incorrect to use metric if the rotation more than and uncorrected (darker) frames. It is obvious that degrees.

uncorrected images contain more shifted pixels.

Since the metric should reflect the In the second experiment the simulated head magnitude of each pixel offset, it should grow rotation (=1o) was smaller than the motion if rotation is growing. It was shown that Sum tracker precision (=5o). The number of head of Optical Flow (SOF) can be used as such a movements during the scan was eight (top metric if the rotation of an image is less than graph in Fig. 4.). The SOF for corrected 15 degrees (for image resolution 128xframes was 93510, for uncorrected – pixels). In Fig. 2. the SOF over rotation is (bottom plot in Fig. 4). It is obvious that frame shown. The SOF can be thought of as a total realignment should not be applied when amount of pixel offset on the two compared motion tracker precision is less than head images.

rotation angle.

4-Й МЕЖДУНАРОДНЫЙ СИМПОЗИУМ «НЕЙРОИНФОРМАТИКА И НЕЙРОКОМПЬЮТЕРЫ» Conclusion The developed simulation framework allows a qualitative and quantitative estimation of the quality of a system for head motion correction for PET imaging. The real scanning data in frame-mode is used. It was shown that motion can introduce blurriness and artifacts which can be reduced using a head pose estimation system. The metric for quantitative estimation of the changes in brain images introduced by motion is described. It is based on computing the Sum of Optic Flow.

Fig. 4. The top plot shows simulated head rotation over The results show that correction should frames (the case when rotation is less than head pose tracker precision). The bottom graph shows the value of not be applied if head movement is less than the metric over frames for both cases corrected (lighter) head tracker precision, otherwise new and uncorrected (darker). It is obvious that correction additional blurriness will be introduced.

should not be applied if head movement is less than Therefore if a tracker has a movement head pose tracker precision.

detection module it should not detect movements that are less than the tracker precision.

Acknowledgments: The work is supported by the Russian Foundation for Basic Research, grant 11-01-00750a.

References 1. Arman Rahmim, Olivier Rousset, Habib Zaidi, “Strategies for Motion tracking and Correction in PET”.

PET Clinics, vol. 2, 2007: pp. 251-266.

2. Costes N, Dagher A, Larcher K, Evans AC, Collins DL, Reilhac A., “Motion correction of multi-frame PET data in neuroreceptor mapping: simulation based validation”. Neuroimage, vol. 47(4), 2009: pp. 1496505.

3. Habib Zaidi and Marie-Louise Montandon, “The New Challenges of Brain PET Imaging Technology”.

Fig. 5. PET brain images: (1) initial motion free PET Current Medical Imaging Reviews, vol. 2, 2006: pp. 3brain image, (2) created using simulation of movement 13.

correction, (3) created using head rotation simulation 4. Gunnar Farnebck “Two-Frame Motion Estimation without correction. It is obvious that the uncorrected Based on Polynomial Expansion”. Lecture Notes in image has more artifacts and is more blurred.

Computer Science, vol. 2749, 2003: pp. 363-370.

Output PET data of the first experiment 5. S. I. Anishchenko, B. A. Osinov, and D. G.

Shaposhnikov. Head Pose Estimation in Solving also was visualized to qualitatively estimate Human–Computer Interaction Problems // Pattern the influence of rotation and correction. The Recognition and Image Analysis, 2011, Volume 21, obtained brain image for one slice is shown in Number 3, pp. 446-449.

Fig. 5. It is obvious that the uncorrected image has more artifacts and is more blurred.

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