I.R.SNApp Image Reconstruction and Segmentation for Neurosurgery Applications

Transcription

1 SENIOR DESIGN PROJECT PLAN I.R.SNApp Image Reconstruction and Segmentation for Neurosurgery Applications May09 10 MEMBERS: William Lim Aaron Logan Dylan Reid Kyungchul Song Bill Zimmerman CLIENT AND FACULTY ADVISOR: Dr. Namrata Vaswani Page 1

2 Table of Contents Disclaimer... 3 Problem Statement... 4 Develop an algorithm for Compressed Sensing... 4 Develop an algorithm for Sequential Segmentation... 4 System Description... 4 Concept Sketch... 5 Block Diagrams... 6 Operating Environment... 8 User Interface... 8 Functional Requirements... 8 Nonfunctional Requirements... 9 Market/Literature Survey... 9 Deliverables Work Breakdown Structure Resource Requirements Project Schedule Risks Summary Page 2

3 Disclaimer This document was developed as part of the requirements of an electrical and computer engineering course at Iowa State University, Ames, Iowa. The document does not constitute a professional engineering design or a professional land surveying document. Although the information is intended to be accurate, the associated students, faculty, and Iowa State University make no claims, promises, or guarantees about the accuracy, completeness, quality, or adequacy of the information. Document users shall ensure that any such use does not violate any laws with regard to professional licensing and certification requirements. Such use includes any work resulting from this studentprepared document that is required to be under the responsible charge of a licensed engineer or surveyor. This document is copyrighted by the students who produced the document and the associated faculty advisors. No part may be reproduced without the written permission of the senior design course coordinator. Page 3

4 Problem Statement The direction of the medical industry necessitates an implementation of real time MR imaging for neurosurgery. To this end, our task as engineers is to implement algorithms that reduce the computational complexity involved in reconstructing raw MRI data into images and segmenting those images into regions of interest. Develop an algorithm for Compressed Sensing In the industry, most existing algorithms for MR image reconstruction have a large scan time. There are several ways to reduce scan time. Compressed Sensing for MRI uses fewer measurements than traditional MRIs. As a result, faster scan times are possible. Batch Compress Sensing for Dynamic MRI: this algorithm uses fewer measurements per frame and is capable of real time acquisition. However, reconstruction time is significantly longer. Develop an algorithm for Sequential Segmentation Currently, MRI data is segmented either by hand, or by slow algorithms. The algorithm must sequentially segment deforming objects, or regions of interest, (ROIs) from the filtered, compressed images. The algorithm must utilize prior knowledge about shape change dynamics to segment noisy/low contrast imagery. The process must be fast enough to run in real time, using only current and past images for segmentation. System Description The raw MRI data is passed to a program written in C++ for reconstruction, as shown in figure 3. Here the signal is passed to a Kalman filter for compressed signals, a least squares filter for compressed signals, and the existing filter for reconstruction, as shown in figure 4. The implementations are then compared, as shown in figure 3. The Kalman filter and least squares filter are shown in figure 6 and 7, respectively. The optimal filter will be chosen at a later date. The reconstructed MRI image is then passed to the segmentation block for processing. In the segmentation block, the areas of interest are highlighted either by hand, by algorithm, or by a combination of the two, as shown in figure 5. The processed image is then output. For testing purposes, simple interfaces may be used, such as the ones shown in figures 1 and 2. Page 4

5 Concept Sketch The following figures are concept sketches of very simple GUIs that might be used to interface with the algorithms. Our top priority in our project is the implementation of the algorithms, so these primitive sketches will suffice. As we draw closer to the end of the project (see the project schedule) and we have time to devote to GUI development, these designs will be given more detail and functional definition. Figure 1: Reconstruction GUI Figure 2: Segmentation GUI Page 5

6 Block Diagrams MRI Raw Data Reconstruction Reconstructed MRI Image Segmentation Processed Image Figure 3: General System Block Diagram MRI Raw Data LS-CS Filter Reconstruction Reconstruction KF-CS Filter Compare Reconstructed MRI Image Reconstruction Existing Filter Reconstruction Figure 4: Reconstruction Block Segmentation Reconstructed MRI Image MATLAB or C++ Code Additional Manual Tracing Via GUI Processed Image Figure 5: Segmentation Block Page 6

7 Figure 6: Overview of the Kalman Filtered CS Algorithm 1 Figure 7: Overview of the Least Squares CS Algorithm 1 1 Provided by Dr. Vaswani Page 7

8 Operating Environment Due to the computational complexity of MRI reconstruction, vast amounts of memory are required for reconstructing MRI images of nontrivial size. Also, due to the number of calculations involved in the reconstruction process and the requirement for real time or near real time reconstruction capability, our software will require a computational environment that can sustain high processing speeds. The additional, potential requirement of parallel processing necessitates that the computing environment be multiple-processor based. Linux computing clusters that meet these requirements are available within the ECpE Department. Thus, the C++ source code must be compiled with Linux as the target operating system. Any additional components, such as executables or GUIs, must also be compiled for Linux operation. All functions will be called from a command prompt, or, in the case that we make a single executable, all functions will be called through a GUI interface. In summary, these are the attributes of the operating environment: Linux based Fast, multiple core processing Lots of memory available Programs interfaced through command prompt program User Interface The preliminary user interface will consist of running the C++ program in a command prompt. After the code is developed into a working product, we will create an executable that has a graphical user interface (GUI). The GUI will allow the user to pick different options related to segmentation, and perhaps allow the user to assist the process if necessary. See figures 1 and 2 for general concept mockups. Functional Requirements FR 1: To translate a MATLAB algorithm written by Dr. Vaswani s graduate students to C++. FR 2: To run experiments with actual MRI data. These experiments will include testing of the reconstruction algorithm and the sequential segmentation algorithm. FR 3: To ensure the correctness of the programs output. This will be tested by comparing output data from the MATLAB code to the output of our C++ translation. Page 8

9 Nonfunctional Requirements NFR 1: The program shall be written in C++. NFR 2: The program shall run faster than the MATLAB implementation. The following are tentative nonfunctional requirements, in that they are goals rather than requirements. If it becomes apparent that they are unattainable NFR 3: The program shall be capable of running in real time. NFR 4: The program shall be parallel processed. Market/Literature Survey Magnetic resonance imaging (MRI) is an imaging technique used in medical that has a strong market in medical imaging industry. Area such as Radiology uses MRI to visualize structure of body. Images from MRI can be utilized for guiding invasive surgery. This is due to the ability to image soft tissue and organs. The market for this program includes all companies that produce MR imaging systems for neurosurgery, and the surgeons performing the operation. This is because, The determination of the optimal limits during the surgical resection of intrinsic brain tumors is particularly challenging due to the difficulty of distinguishing tumor from essential brain by visual inspection. 2 The current time needed to capture a complete MRI brain scan is approximately 35 minutes 3. With the development of a real time system, surgeons will be able to perform more accurate operations. The market for this technology consists of companies that are currently manufacturing MRI systems that are used in hospitals today. Here is a list of current MRI companies 4 : 2http://portal.acm.org/citation.cfm?id= &coll=GUIDE&dl=GUIDE&CFID= &CFTOK EN= &ret=1#Fulltext 3 medicine.org/medicine news 1/Use of intraoperative MRI adds time but carechanging information to neurosurgery study shows / 4 mri.org/resources/links/manufacturers/systems.htm Page 9

10 Esaote Biomedica Fonar GE Medical Systems Hitachi Medical Systems Millennium Technology Inc. Odin Medical Technologies Oni Corporation Neusoft Philips Medical Systems Shimadzu Siemens Medical Solutions Toshiba Medical Systems Deliverables The deliverables of our project include: A complete system that takes raw MRI data, reconstructs the image, and performs image segmentation. The system will output the ROI contours. According to the project requirements, attributes of the system are: C++ code, well documented and working An executable of the compiled C++ code A basic GUI that allows the user to assist the segmentation process, and also displays segmentation options Comprehensive test results Work Breakdown Structure The two main portions of the MRI software, Compressive Sensing Image Reconstruction and Image Segmentation, require teams who will specialize in understanding the theory and implementations of the respective algorithms. Thus, we have divided our group into two teams: Team 1: Compressive Sensing Image Reconstruction William Lim Aaron Logan Bill Zimmerman Team 2: Image Segmentation Dylan Reid Kyungchul Song We have divided into these groups according to our interests in the topics and our inclinations toward attending graduate school. Consideration was also given to the complexity of the two portions. Accordingly, three team members have been assigned to the more complex, Compressive Sensing portion. Page 10

11 At the outset, it shall be the responsibility of each team member to learn and understand the theory of his topic in addition to the IT aspects required for implementation (C++, MATLAB, etc.). Understanding of the tools and algorithm development will be undertaken simultaneously, in that code snippets in C++ will be written to test and understand theoretical concepts. A review of the current MATLAB implementation will aid understanding of MATLAB programming and the algorithm. As learning progresses and we move closer to algorithm implementation, we will further specialize amongst our respective teams. Specific responsibilities of these specializations are to be determined, pending a working understanding of the theory and implementation strategy. Resource Requirements Each team member will need, either on his own computer, or through access to an ECpE department lab computer: A C++ integrated development environment (IDE) and compiler. Possible IDEs include Eclipse and Bloodshed Dev C++, which are both open source software, available for free download from the internet. MATLAB and Image Processing Toolbox. Both are available to all team members for free as part of the College of Engineering s Mathworks student license. Additional resource requirements are: Test Data. Initial framework testing will require a small dataset, but extensive testing for algorithm robustness and correctness will require a large dataset with enough variation between MRI images to simulate the gamut of real world scenarios. GUI development environment. Possibilities include Microsoft Visual Studio (available to all team members as free, educational licensed version from Microsoft) or Eclipse with appropriate add on(s). Additional research into GUI development tools will be required when GUI development begins. Page 11

12 Project Schedule Below is our tentative project schedule. We have scheduled our work for both this semester and next semester. Note that we plan to wrap up with learning by Thanksgiving break; by that time we should have enough working knowledge to be moving into algorithm development. We have scheduled algorithm integration (completion of a single framework of integrated modules) and testing of this framework to begin after winter break, during EE 492. Finally, note that all tasks end the last week before dead week of next semester. Figure 8: Tentative Project Schedule for EE 491 and EE 492 Risks Risks with this project mainly relate to the segmentation portion. Segmentation is very data dependent, so poor test data may result in inaccurate contour extraction. Also, good segmentation is difficult to define objectively, so implementing a robust segmentation algorithm will be challenging. Also, running the KFCS algorithm on a computer with insufficient resources could cause it to crash, resulting in loss of data. This risk is minimized if we test only very small images (such as 64x64 images) on PCs and reserve our large scale testing for the highspeed Linux clusters. Page 12

13 Summary Our project is the implementation of MRI image processing and has two components: Kalman Filtered Compressive Sensing image reconstruction, and image segmentation. We have subdivided our group into two teams to specialize on these parts. The algorithms will be implemented in C++ for operation on Linux clusters. We have several project requirements that not only dictate how we do our work but also give us work goals. We hope to come as close to real time operation as possible. This may require using parallel processing. Currently, we are in the learning phase of the project, and we will be moving into the initial stages of implementation by the end of November. Page 13