- author = "Matthew Witten and Owen Clancey",
- title = "Feasibility of Genetic Programming for the Optimization of Tissue-Type-Segmented Maps in the Generation of Synthetic {CT} in Radiation Therapy Treatment Planning",
- booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC 2020",
- year = "2020",
- editor = "Yaochu Jin",
- pages = "paper id24048",
- address = "internet",
- month = "19-24 " # jul,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, human medicine thaerpy, synthetic CT, Matlab",
- isbn13 = "978-1-7281-6929-3",
-
URL = "
http://www.human-competitive.org/sites/default/files/wittenclanceyhumiesubmission.txt",
-
URL = "
http://www.human-competitive.org/sites/default/files/cec_2020_witten_clancey_post_acceptance_draft1.pdf",
-
DOI = "
doi:10.1109/CEC48606.2020.9185768",
-
video_url = "http://www.human-competitive.org/sites/default/files/witten_clancey_2021_humie_video.mp4",
- size = "6 pages",
- abstract = "Modern radiation therapy treatment planning has traditionally relied upon computed tomography (CT) in modeling the interaction of megavoltage (MV) photons with the various tissues and organs of the body. CT image data provide both detailed information about individual patient anatomy, as well as a voxel-by-voxel three-dimensional grid of Hounsfield units (HU), which specifies, at all points within the patient, essentially the difference between the attenuation coefficient of the tissue within that voxel from the attenuation coefficient of water, normalized to that of water. From the HU value, the relative electron density can be inferred, and as the relative electron density is the major determinant of the interaction of the tissue with MV photons, the radiation dose distribution can then be calculated. Recently, there has been interest in using magnetic resonance imaging (MR) in lieu of CT, as MR provides superior soft-tissue contrast; however, MR does not provide any electron density information. Various approaches have been essayed to create a synthetic CT (synCT) from the MR data. In the present study, genetic programming was used to construct mappings of MR data to HU data for seven tissue types: bladder, cancellous bone, cortical bone, fat, muscle, prostate, and rectum. These maps were then applied to randomly chosen points in five patient data sets to calculate the synCT HU values, which were then compared with the actual HU values from CT images of those same patients. The method produced mean absolute errors (MAE) of 9.28 HU, 33.24 HU, 75.32 HU, 18.64 HU, 17.12 HU, 11.76 HU, and 18.40 HU for the respective tissue types, and these MAE values are less than those of previous approaches, indicating superior performance. Although the method of the present study does require more manual input, the superior performance is compensatory. Further study is necessary to confirm accuracy on entire MR data sets, and to ensure there is no sample variance effect on the current results.",
-
notes = "Entered 2021 HUMIES,
GPlab v4, pop=100,100=gens lexitour, Rank85 selection, no elitism pc=0.5, 0.5 point mutation, Sara Silva dynamic tree depth for anti-bloat. GP better than ANN. GP and deep ANN better than human atlas based methods. Protate cancer. male pelvis.
Video mins:sec Feasibility of GP for the optimisation of tissue-type-segmented maps in the generation of synthetic CT in radiation therapy treatment planning 0:55 Radiation million electron volt photons, Compton scattering, killing tumour cells, electron density of human tissues. Langone Health. 2:04 MRI v CT 2:21 dataset registration Use GP to quickly get CT like dataset from patient's MRI data 3:49. No geometric error. Some of expert human knowledge plus machine. Electron density 5:44 GP, human, body atlas Deep Convolutional neural network ANN, DCNN. GP only available approach for 4 tissues: Canc Bone, types, muscle, fat, prostate 6:06. GP better than all published results. Practical for any hospital => better treatment => better outcomes.
https://wcci2020.org/
NYU Winthrop Hospital, Long Island, United States of America.
Also known as \cite{9185768}",
