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Physics & Computing Research

Carbon nanotube field emission based irradiation technology program

CNT Image 1
Carbon nanotube (CNT) field emission
illustration.  Electrons emitted from the
tip of a CNT under an applied
electric field.

The new research program is headed by Dr. Sha Chang and involves development of novel radiation research and clinical devices based on carbon nanotube (CNT) field emission technology. The program is a close research collaboration with Dr. Otto Zhou’s group in the department of Physics & Astronomy. The multidisciplinary research team includes medical physicists, material scientists, and cancer biologists.  Field emission is a quantum effect that where electrons are emitted from cathode under the control of a surface electronic field.  CNTs are excellent field emitters and CNT field emission technology has been used in Zhou’s group to develop medical imaging devices such as micro-CT for animal model research and tomosynthesis for clinical application.  Our program is focused on the development of CNT field emission technology applications in radiation oncology. We currently have several research projects funded by NIH grants to develop CNT field emission based single cell Irradiation and micro-RT systems.

Publication list

  • "Development of a carbon nanotube based low -LET multi-pixel microbeam array", S. Chang, J. Zhang, D. Bordelon, E. Schreiber, A. Cox, O. Zhou. Rad. Res 166, 658-659 (2006).
  • "A stationary scanning X-ay imaging system based on carbon nanotube field emitters", J. Zhang, G. Yang, R. Rajaram, E. Guan, Y. Lee, D. LaLush, S. Chang, J.P. Lu, O. Zhou, Medical Physics 33 (6): 2159, JUN 2006
  • "A carbon nanotube based low LET multi-microbeam array singel cell irradiation system", S. Chang, J. Zhang, D. Bordelon, E. Schrieber, A. Cox, O. Zhou,Medical Physics (6): 2271, JUN 2006
  • "Multiplexing radiography using a carbon nanotube based x-ray tube", J. Zhang, G. Yang, Y.Z. Lee, S. Chang, J.P. Lu, O. Zhou, Applied Physics Letters 89, 1 (2006)
  • "A multi-beam x-ray imaging system based on carbon nanotube field emitters", J. Zhang, G. Yang, Y. Z. Lee, Y. Cheng, B. Gao, Q. Qiu, J. P. Lu, and O. Zhou, Proc. SPIE Vol. 6142, 614204 (2006)
  • "Development of a carbon nanotube based microfocus x-ray tube with single focusing electrode", Z. Liu, J. Zhang, G. Yang, Y. Cheng, O. Zhou, and J.P. Lu, Rev. Sci. Instrum. 77, 054302 (2006)
  • “Field Emission of Electrons from Single LaB6 Nanowires”, Han Zhang, Qi Zhang, Guang Yang, Jian Zhang, Jie Tang, Otto Zhou and Lu-Chang Qin, Adv. Mater. Vol 18, Issue 1, 87-91(2006)
  • "Tomosynthesis reconstruction from multi-beam x-ray sources", David S. Lalush, Enzhuo Quan, Ramya Rajaram, Jian Zhang, Jianping Lu, and Otto Zhou, Proceeding of 2006 IEEE International Symposium on Biomedical Imaging
  • “Multiplexing Radiography for Ultra-Fast Computed Tomography: A Feasibility Study”, J Zhang, G Yang, Y Lee, S Chang, J Lu, O Zhou, Med. Phys. 34, 2527 (2007)
  • “Feasibility Study of a Carbon Nanotube Field Emission Microbeam System for Cellular Irradiation” S Chang, J Zhang, D Bordelon, S Graboski, E Schreiber, A Cox, and O Zhou, Med. Phys. 34, 2455 (2007)
  • “Monte Carlo Simulation and Design of a Carbon Nanotube Small-Animal Micro-RT System”. E Schreiber and S Chang. Med. Phys. 34, 2332 (2007)

Carbon nanotube field emission single cell irradiation

Sha Chang (PI), Jian Zhang, Eric Schreiber, Adrienne Cox, David Borldelon*, Otto Zhou*
*Department of Physics & Astronomy, University of North Carolina at Chapel Hill

Goal: To develop a novel multi-pixel single cell microbeam irradiation system that can irradiate many individually selected single cells or cellular regions simultaneously or in a time sequence under microscope observation.

We use carbon nanotube field emission technology to develop new irradiation devices to meet the needs of high spatial and temporal resolution yet compact cellular irradiation devices suitable for average biology laboratories.  The proposed system promises simultaneous irradiation of many individually selected cells or cellular regions under microscope observation.  Recently, we have successfully developed the first prototype device and calibrated its 30keV electron beam Radiation dosimetry.  We have validated the prototype system by cell irradiation and observation of the expected DNA damage.

CNT Image 2
Dose distribution of the single-pixel CNT cellular irradiation system.  The insert is the Si3Ni4 vacuum and electron window.
CNT image 3
Prototype single-pixel CNT field emission cellular irradiation system design.

Carbon nanotube field emission micro-RT irradiation

Sha Chang (PI), Sigen Wang, Eric Schreiber, Otto Zhou*
*Department of Physics 7 Astronomy, University of North Carolina at Chapel Hill

Goal: To develop a high spatial and temporal resolution micro-RT system for small animal model radiation research using nanotube field emission technology.

We propose to develop a novel x-ray pixel beam array micro-RT system using carbon nanotube field emission.  The radiation field shape, intensity modulation, and their variation with time are defined electronically by activating a selected set of CNT field emission x-ray pixel beams.  Once developed the micro-RT will be integrated with the CNT micro-CT already developed by our collaborator Dr. Zhou’s group to form an integrated micro-CT-RT system.  The micro-CT-RT system is expected to deliver image-guided (IGRT) and intensity-modulated (IMRT) irradiation analogous to the state of the art radiotherapy many of our patients receive today but at the scale of a mouse.

CNT Image 4
Simulated treatment planning of mouse irradiation by the CNT x-ray pixel beam array micro-RT system under development by our group.
CNT image 5
CNT field emission x-ray pixel beam micro-RT system for small animal irradiation.

 

Other related CNT field emission research projects

http://www.physics.unc.edu/project/zhou/index.php

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Radiotherapy treatment & planning

Clinical significance based IMRT QA approach

Jun Lian, Tim Cullip, Kathy Deschesne and Sha Chang

Goal: reconstruct the 3D dose distribution from the 2D intensity maps collected during IMRT QA.

The patient-specific IMRT treatment quality assurance procedure often consists of the measurement of 2-D intensity map and absolute dose. The data is compared with the computed results from TPS using a standard pass-or-fail criterion. However, the same dose discrepancy may not have the same clinical significance depending on its location. For instance, the consequence of a hot-spot in PTV is different than in spinal cord. In this project, we reconstruct the 3D dose distribution in patient planning CT from the intensity maps obtained from the 2D IMRT QA measurement. A QA statistics method is proposed to include the location of dose discrepancy points. This approach promises a new IMRT QA standard that considers the clinical significance of dose discrepancy measured in IMRT QA.

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Image deformation and image-guided radiotherapy

Automatic segmentation of the male pelvis for image-guided and adaptive radiation therapy (IGRT, ART)

Critical treatment planning and delivery decisions in radiation therapy include aiming and shaping radiation beams to hit the tumor target and spare nearby normal organs. CT images provide a geometric model of the patient that is used to make these decisions with the aid of computer-based planning systems, sometimes on a daily basis over several weeks. Current methods to extract the patient model from CT images in clinical practice require expert human interaction and thus are extremely time consuming and expensive. Moreover humans, even experts with similar training, are known to exhibit inter- and intra-user variabilities that can adversely affect clinical decisions. Automatic methods are being developed based on a class of so-called statistically trainable deformable-shape models (SDSMs) called m-reps (medial representations). An m-rep is an explicit mathematical representation (a model) of an anatomical object that can be automatically imbedded in a target image, rigidly registered with the corresponding target object, and then deformed to closely match the shape of the specific target object. To accomplish all this, m-reps must be trained against models built by expert humans to generate statistical probability distributions that define the range of object shapes (e.g., prostates, bladders and rectums) and the range of each object’s intensity patterns in medical images across the human population of interest. more >>

Finite Element Modeling of mechanical deformations of the prostate caused by endorectal imaging probes (IGRT, ART)

M-reps provide a framework for automatically building 3D hexahedral meshes to compute physically correct mechanical deformations via the finite element method (FEM). This approach is being used in clinical studies involving CT and MR images for brachytherapy dosimetry by collaborators at Memorial Sloan Kettering Cancer Center.

Selected Publications

  • Crouch JR, Pizer SM, Chaney EL, Zaider M: Medially Based Meshing with Finite Element Analysis of Prostate Deformation.: Medical Image Computing and Computer-Assisted Intervention, Proceedings, Part I, Randy E. Ellis, Terry M. Peters (Eds.) 108-115 (2003) Springer, NY
  • Crouch JR, Pizer SM, Chaney EL, Hui Y-C, Mageras G, and Zaider M: Automated Finite Element Analysis for Deformable Registration of Prostate Images. Trans Med Imaging (2007) (in press)

 

Radiation treatment under respiratory motion

Researchers is being conducted on how to improve treatment of tumors in the lung and in the liver while these organs undergo motion and shape change due to breathing during treatment. M-reps will be investigated to segment the target organ and nearby organs at risk in CT images acquired at multiple intervals over one ore more respiratory cycles. A 4D model will be built from these segmentations to predict changes across the patient’s breathing cycle and to account for these changes during planning and treatment delivery. External collaborators include Memorial Sloan Kettering Cancer Center.

 

 

 

TRUS augmented by MRI/MRSI for prostate interventions

Prostate interventions such as biopsy, brachytherapy, and therapeutic tissue ablation are visually guided by trans-rectal ultrasound images (TRUS). TRUS shows the overall shape of the prostate but does not distinguish cancer from normal prostate tissue. The inability to actually see where cancer is located during a TRUS procedure severely limits the ability of the surgeon to accurately aim his/her instrument at suspected cancer growths. Due to this limitation current biopsy procedures lead to a relatively high number of undetected cases (false negatives). Moreover brachytherapy and ablation procedures must target volumes of tissue larger than necessary to assure inclusion of the cancer growths in the target region, an approach that can lead to unwanted side effects. Recent research has shown that the combination of magnetic resonance imaging (MRI) and magnetic resonance spectroscopic imaging (MRSI) can pinpoint regions likely to harbor cancer cells inside the prostate. M-reps are being investigated to accurately map potential cancer-bearing regions found on pre-intervention MR/MRS images to TRUS images, and display those regions as targets for interventional instruments such as biopsy needles, radioactive seeds, and ablation instruments.

 

Develop resources to support external users of PLanUNC

PlanUNC is a radiation therapy treatment planning software system that has been under development in the Department of Radiation Oncology at the University of North Carolina (UNC) for more than fifteen years. PLanUNC has an established record as an exportable research platform but resources to support external users are lacking. The overall aim of this project is to provide PlanUNC, enhanced with a suite of integrated software tools from the rich image analysis research environment at UNC, as a freely available, fully documented and supported, open and extensible treatment planning research platform for investigating new techniques and methods for image-guided radiotherapy treatment simulation and delivery.

Selected Publications

  • Sailer SL, Chaney EL, Rosenman JG, Chang SX, Miller EP, Experience with 3D Conformal Treatment Planning, in Teletherapy: Present and Future, J Palta and TR Mackie, eds., Advanced Medical Publishing, Madison, WI: 525-555 (1996)

 

Software toolkit for multi-image registration and segmentation for IGRT and ART

ConStruct leverages prior knowledge to simplify IGRT/ART seg's
ConStruct leverages prior knowledge
to simplify IGRT/ART seg's

Extracting information from images for IGRT and ART usually involves segmentation and/or registration with a reference image. The purpose of this project is to develop and make freely available a software toolkit for research use that combines several registration and segmentation methods and is particularly applicable to problems involving multiple sequential images. Two automatic approaches will be integrated with standard interactive methods. One automatic approach solves PDEs describing visco-elastic flow to register a set of target images with a reference image. Segmentations can then be transferred from the reference image to target images via corresponding deformation fields. The second method segments images via Bayesian posterior optimization of m-reps. User efficiency will be improved by the ability to queue, view, work with, and compare multiple images and segmentations during a single session. Registered images and segmentations will be output in DICOM RT format.

 

 

Prostate IGRT using Deformable Image Registration (PI: Mark Foskey)

ConStruct leverages prior knowledge to simplify IGRT/ART seg's
Green is Prostate after deformable
registration with this planning image.

Mark Foskey, Bradley C. Davis, Lav Goyal, Sha Chang, Ed Chaney, Nathalie Strehl, Sandrine Tomei, Julian Rosenman, Sarang Joshi

We are studying the use of deformable image registration as a tool in image-guided radiation therapy of the prostate. As modern techniques for conformal therapy attempt to fit the high-dose region as closely as possible to the target volume, it becomes more essential to ensure that the tumor is in the expected location, and to assess the dose actually delivered to each organ.

We use deformable image registration in two ways. First, we use it for automatic segmentation of images acquired at treatment time, by deforming manual segmentations of the planning day image. Second, we use it to estimate the dose actually delivered, by deforming calculated dose distributions from each treatment day. In each case, the deformations we apply are computed by registering a treatment image to a planning image.

We perform our registration in two stages. We first perform a translation based on bony landmarks, and then a deformable registration in a region of interest. The images to the right illustrate the effects of the stages. Only the conturs differ; the images are all the same slice from the planning CT of a patient. The red contour is the location of the prostate in the planning CT, and the yellow contour is the rectum. The green contours indicate the locations of the prostate in the daily images. The top image shows the prostate locations as treated. The middle images shows the prostate locations after translation to align bony landmarks. And the bottom image shows the locations of the prostate after deformable registration is performed. In the latter case, the image deformation was applied to 3D prostate models derived from the manual segmentations, which were then resliced to get the deformed contours.

ConStruct leverages prior knowledge to simplify IGRT/ART seg's
Average radial distance between
deformed planning segmentation
and 2 rators.

Selected Publications

 

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