Creation of an anthropomorphic CT head phantom for verification of image segmentation

Purpose Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast. Methods To investigate these issues we have created a three‐dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom. Results Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9–35.8 phantom, 29.9–34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other. Conclusion The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

[1]  M. Poletti,et al.  Development of an anthropomorphic head phantom using dolomite and polymethyl methacrylate for dosimetry in computed tomography , 2015 .

[2]  Karl J. Friston,et al.  Voxel-based morphometry of the human brain: Methods and applications , 2005 .

[3]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[4]  L. Rodney Long,et al.  Window Classification of Brain CT Images in Biomedical Articles , 2012, AMIA.

[5]  J. Zhong,et al.  Gray matter atrophy in Parkinson’s disease with dementia: evidence from meta-analysis of voxel-based morphometry studies , 2013, Neurological Sciences.

[6]  F. Rybicki,et al.  Medical 3D Printing for the Radiologist. , 2015, Radiographics : a review publication of the Radiological Society of North America, Inc.

[7]  Chris Rorden,et al.  Age-specific CT and MRI templates for spatial normalization , 2012, NeuroImage.

[8]  Eric Westman,et al.  Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis , 2018, European Radiology.

[9]  Darryl H. Hwang,et al.  Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions , 2019, Front. Neuroinform..

[10]  Kota Yokoyama,et al.  Computed-tomography-guided anatomic standardization for quantitative assessment of dopamine transporter SPECT , 2017, European Journal of Nuclear Medicine and Molecular Imaging.

[11]  Rui Huang,et al.  A meta-analysis of voxel-based morphometry studies of white matter volume alterations in Alzheimer's disease , 2012, Neuroscience & Biobehavioral Reviews.

[12]  Rebecca M. Howell,et al.  Preparation and fabrication of a full‐scale, sagittal‐sliced, 3D‐printed, patient‐specific radiotherapy phantom , 2017, Journal of applied clinical medical physics.

[13]  Hajime Monzen,et al.  Three-dimensional printer-generated patient-specific phantom for artificial in vivo dosimetry in radiotherapy quality assurance. , 2017, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[14]  Nick C Fox,et al.  The Alzheimer's disease neuroimaging initiative (ADNI): MRI methods , 2008, Journal of magnetic resonance imaging : JMRI.

[15]  S. Goldberg,et al.  Significance of enhanced cerebral gray–white matter contrast at 80kVp compared to conventional 120kVp CT scan in the evaluation of acute stroke , 2014, Journal of Clinical Neuroscience.

[16]  S. Fielden,et al.  Automated Segmentation of Head Computed Tomography Images Using FSL , 2017, Journal of computer assisted tomography.

[17]  Maureen van Eijnatten,et al.  Using 3D printing techniques to create an anthropomorphic thorax phantom for medical imaging purposes , 2018, Medical physics.

[18]  Tilo Kircher,et al.  Accuracy and Reliability of Automated Gray Matter Segmentation Pathways on Real and Simulated Structural Magnetic Resonance Images of the Human Brain , 2012, PloS one.

[19]  L. R. Dice Measures of the Amount of Ecologic Association Between Species , 1945 .

[20]  A. Mäkitie,et al.  Inaccuracies in additive manufactured medical skull models caused by the DICOM to STL conversion process. , 2014, Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery.

[21]  Ron Kikinis,et al.  Statistical validation of image segmentation quality based on a spatial overlap index. , 2004, Academic radiology.

[22]  Chien-Han Lai Gray matter volume in major depressive disorder: A meta-analysis of voxel-based morphometry studies , 2013, Psychiatry Research: Neuroimaging.

[23]  Mirza Faisal Beg,et al.  Multiscale deep neural network based analysis of FDG‐PET images for the early diagnosis of Alzheimer's disease , 2018, Medical Image Anal..

[24]  H. Wiendl,et al.  Clinical Relevance of Brain Volume Measures in Multiple Sclerosis , 2014, CNS Drugs.

[25]  Clifford R Jack,et al.  Common MRI acquisition non-idealities significantly impact the output of the boundary shift integral method of measuring brain atrophy on serial MRI , 2006, NeuroImage.

[26]  L. Schad,et al.  Comparison of automated brain segmentation using a brain phantom and patients with early Alzheimer's dementia or mild cognitive impairment , 2015, Psychiatry Research: Neuroimaging.

[27]  Kengo Ito,et al.  A comparison of three brain atlases for MCI prediction , 2014, Journal of Neuroscience Methods.

[28]  Lei Wang,et al.  Development and validation of a novel dementia of Alzheimer's type (DAT) score based on metabolism FDG-PET imaging , 2017, NeuroImage: Clinical.

[29]  W. M. van der Flier,et al.  Diagnostic imaging of patients in a memory clinic: comparison of MR imaging and 64-detector row CT. , 2009, Radiology.

[30]  L. Pantoni,et al.  The use of CT in dementia , 2011, International Psychogeriatrics.

[31]  Ehsan Samei,et al.  Quantum noise properties of CT images with anatomical textured backgrounds across reconstruction algorithms: FBP and SAFIRE. , 2014, Medical physics.

[32]  Frank P. Leo,et al.  Use of computed tomography in skeletal structure research , 1986 .

[33]  W. Kalender,et al.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions , 2000 .

[34]  J. Park,et al.  Inter‐scanner variability in Hounsfield unit measured by CT of the brain and effect on gray‐to‐white matter ratio , 2018, The American journal of emergency medicine.

[35]  John Muschelli,et al.  Recommendations for Processing Head CT Data , 2019, Front. Neuroinform..

[36]  Sung Kyu Kim,et al.  Feasibility of a 3D-printed anthropomorphic patient-specific head phantom for patient-specific quality assurance of intensity-modulated radiotherapy , 2017, PloS one.

[37]  John J Sidtis,et al.  A six-month longitudinal evaluation significantly improves accuracy of predicting incipient Alzheimer's disease in mild cognitive impairment. , 2017, Journal of neuroradiology. Journal de neuroradiologie.

[38]  Takeshi Iwatsubo,et al.  Comparison between brain CT and MRI for voxel-based morphometry of Alzheimer's disease , 2013, Brain and behavior.

[39]  Stefan Klöppel,et al.  Applying Automated MR-Based Diagnostic Methods to the Memory Clinic: A Prospective Study , 2015, Journal of Alzheimer's disease : JAD.

[40]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[41]  Dong Soo Lee,et al.  Differential features of metabolic abnormalities between medial and lateral temporal lobe epilepsy: quantitative analysis of (18)F-FDG PET using SPM. , 2003, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[42]  H. Rusinek,et al.  Accelerated Brain Atrophy on Serial Computed Tomography: Potential Marker of the Progression of Alzheimer Disease , 2016, Journal of computer assisted tomography.

[43]  Elizabeth Gourd UK radiologist staffing crisis reaches critical levels. , 2017, The Lancet. Oncology.

[44]  A. Scarsbrook,et al.  Clinical impact and diagnostic accuracy of 2-[18F]-fluoro-2-deoxy-d-glucose positron-emission tomography/computed tomography (PET/CT) brain imaging in patients with cognitive impairment: a tertiary centre experience in the UK. , 2017, Clinical radiology.

[45]  P. Scheltens,et al.  Does MRI Increase the Diagnostic Confidence of Physicians in an Outpatient Memory Clinic , 2016, Dementia and Geriatric Cognitive Disorders Extra.

[46]  Glyn W. Humphreys,et al.  NeuroImage: Clinical Automated delineation of stroke lesions using brain CT images , 2022 .

[47]  Milan Sonka,et al.  3D Slicer as an image computing platform for the Quantitative Imaging Network. , 2012, Magnetic resonance imaging.

[48]  Xiaohong W. Gao,et al.  Classification of CT brain images based on deep learning networks , 2017, Comput. Methods Programs Biomed..

[49]  F. Barkhof,et al.  Agreement of MSmetrix with established methods for measuring cross-sectional and longitudinal brain atrophy , 2017, NeuroImage: Clinical.

[50]  Bram Platel,et al.  White Matter and Gray Matter Segmentation in 4D Computed Tomography , 2017, Scientific Reports.

[51]  H. Matsuda,et al.  Structural Neuroimaging in Alzheimer’s Disease , 2017 .

[52]  John E. Richards,et al.  Age-specific MRI brain and head templates for healthy adults from 20 through 89 years of age , 2015, Front. Aging Neurosci..

[53]  Patrick D Higgins,et al.  Patient specific 3D printed phantom for IMRT quality assurance , 2014, Physics in medicine and biology.

[54]  E. Bigler Structural Image Analysis of the Brain in Neuropsychology Using Magnetic Resonance Imaging (MRI) Techniques , 2015, Neuropsychology Review.

[55]  J. Raduà,et al.  Localized grey matter atrophy in multiple sclerosis: A meta-analysis of voxel-based morphometry studies and associations with functional disability , 2013, Neuroscience & Biobehavioral Reviews.

[56]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[57]  Soo Borson,et al.  Neuroimaging in the Clinical Diagnosis of Dementia: Observations from a Memory Disorders Clinic , 2010, Journal of the American Geriatrics Society.

[58]  Tao Liu,et al.  Identification of Early-Stage Alzheimer's Disease Using Sulcal Morphology and Other Common Neuroimaging Indices , 2017, PloS one.

[59]  Jae Min Kim,et al.  The frontal skull Hounsfield unit value can predict ventricular enlargement in patients with subarachnoid haemorrhage , 2018, Scientific Reports.

[60]  Hamid Abrishami Moghaddam,et al.  Design and construction of a brain phantom to simulate neonatal MR images , 2011, Comput. Medical Imaging Graph..

[61]  Dane Rayment,et al.  Neuroimaging in dementia: an update for the general clinician , 2016 .

[62]  J. Thiran,et al.  Basic MR sequence parameters systematically bias automated brain volume estimation , 2016, Neuroradiology.

[63]  Jason W Sohn,et al.  Characterization of 3D printing techniques: Toward patient specific quality assurance spine-shaped phantom for stereotactic body radiation therapy , 2017, PloS one.

[64]  Nick C Fox,et al.  MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases , 2016, Brain : a journal of neurology.