In order to validate CT imaging as a biomarker, it is important to ascertain the variability and artifacts associated with
various forms of advanced visualization and quantification software. The purpose of the paper is to describe the
rationale behind the creation of a free, public resource that contains phantom datasets for CT designed to facilitate
testing, development and standardization of advanced visualization and quantification software. For our research, three
phantoms were scanned at multiple kVp and mAs settings utilizing a 64-channel MDCT scanner at a collimation of 0.75
mm. Images were reconstructed at a slice thickness of 0.75 mm and archived in DICOM format. The phantoms
consisted of precision spheres, balls of different materials and sizes, and slabs of Last-A-Foam(R) at varying densities.
The database of scans is stored in an archive utilizing software developed for the National Cancer Imaging Archive and
is publically available. The scans were completed successfully and the datasets are available for free and unrestricted
download. The CT images can be accessed in DICOM format via http or FTP or utilizing caGRID. A DICOM database
of phantom data was successfully created and made available to the public. We anticipate that this database will be
useful as a reference for physicists for quality control purposes, for developers of advanced visualization and
quantification software, and for others who need to test the performance of their systems against a known "gold"
standard. We plan to add more phantom images in the future and expand to other imaging modalities.
KEYWORDS: Digital photography, Photography, 3D image processing, 3D image reconstruction, Computed tomography, Diagnostics, Software, Statistical analysis, Radiology, Facial recognition systems
3D and multi-planar reconstruction of CT images have become indispensable in the routine practice of diagnostic
imaging. These tools cannot only enhance our ability to diagnose diseases, but can also assist in therapeutic planning as
well. The technology utilized to create these can also render surface reconstructions, which may have the undesired
potential of providing sufficient detail to allow recognition of facial features and consequently patient identity, leading
to violation of patient privacy rights as described in the HIPAA (Health Insurance Portability and Accountability Act)
legislation. The purpose of this study is to evaluate whether 3D reconstructed images of a patient's facial features can
indeed be used to reliably or confidently identify that specific patient. Surface reconstructed images of the study
participants were created used as candidates for matching with digital photographs of participants. Data analysis was
performed to determine the ability of observers to successfully match 3D surface reconstructed images of the face with
facial photographs. The amount of time required to perform the match was recorded as well. We also plan to
investigate the ability of digital masks or physical drapes to conceal patient identity. The recently expressed concerns
over the inability to truly "anonymize" CT (and MRI) studies of the head/face/brain are yet to be tested in a prospective
study. We believe that it is important to establish whether these reconstructed images are a "threat" to patient
privacy/security and if so, whether minimal interventions from a clinical perspective can substantially reduce this
possibility.
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