Physical evaluation of an ultra-high-resolution CT scanner

Oostveen, Luuk J.; Boedeker, Kirsten; Brink, Monique; Prokop, Mathias; Lange, Frank de; Sechopoulos, Ioannis

doi:10.1007/s00330-019-06635-5

Cited by 56 publications

(46 citation statements)

References 20 publications

(23 reference statements)

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“…One limitation of using the CTP404 module for SSP estimations is that the wire ramps are too thick for accurate estimation using a slice thickness <1 mm as indicated by the manufacturer. This results in a commensurate broadening of the measured slice thickness (0.95 mm; Table II) compared to previously reported measurements of 0.79 mm using a thin (0.025 mm) tungsten foil 1 . Given that the focus of this study is on the relative behavior of the longitudinal resolution between the NR sim and actual NR images this systematic bias was considered acceptable.…”

Section: Discussionsupporting

confidence: 49%

“…This results in a commensurate broadening of the measured slice thickness (0.95 mm; Table II) compared to previously reported measurements of 0.79 mm using a thin (0.025 mm) tungsten foil. 1 Given that the focus of this study is on the relative behavior of the longitudinal resolution between the NR sim and actual NR images this systematic bias was considered acceptable.…”

Section: Discussionmentioning

confidence: 99%

“…These include the estimation of technical measures such as the modulation transfer function (MTF) and noise power spectrum (NPS), dose relationships, scan time, etc. [1][2][3][4][5][6] Other key components in the development of a scanner include the evaluation of its impact on specific diagnostic tasks. [7][8][9] While the technical evaluations are typically phantom based, these clinical evaluations require patient imaging, comparing the new system with some existing, standard-of-care technology.…”

Section: Introductionmentioning

confidence: 99%

“…In the initial stages, engineering properties of the new scanner are established. These include the estimation of technical measures such as the modulation transfer function (MTF) and noise power spectrum (NPS), dose relationships, scan time, etc 1–6 . Other key components in the development of a scanner include the evaluation of its impact on specific diagnostic tasks 7–9 .…”

Section: Introductionmentioning

confidence: 99%

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Validation of synthesized normal‐resolution image data generated from high‐resolution acquisitions on a commercial CT scanner

et al. 2020

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To validate a normal-resolution (NR) simulation (NRsim) algorithm that uses high-resolution (HR) or super-high resolution (SHR) acquisitions on a commercial HR computed tomography (CT) scanner by comparing image quality between NRsim-generated images and actual NR images. NRsim is intended to allow direct comparison between normal-resolution CT and HR/SHR reconstructions in clinical investigations, without repeating exams. Methods: The Aquilion Precision CT (Canon Medical Systems Corporation) HR CT scanner has three resolution modes resulting from detector binning in the channel (x-y) and row (z) directions. For NR, each detector element is 0.5 mm × 0.5 mm along the channel and row directions, 0.25 mm × 0.5 mm for HR, and 0.25 mm × 0.25 mm for SHR. The NRsim algorithm simulates NR acquisitions from HR or SHR acquisitions (termed NR HR and NR SHR , respectively) by downsampling the pre-log raw data in the channel direction for the HR acquisitions and in the channel and row direction for the SHR acquisition. The downsampled data are then reconstructed using the same process as NR. The axial modulation transfer function (MTF), slice sensitivity profile (SSP), and CT number accuracy were measured using the Catphan 600 phantom, and the three-dimensional noise power spectrum (NPS) was measured in water-equivalent phantoms for standard protocols across a range of size-specific dose estimates (SSDE): head (6.2-29.8 mGy), lung (2.2-18.2 mGy), and body (5.6-19.4 mGy). The MTF and NPS measurements were combined to estimate low-contrast detectability (LCD) using a non-prewhitening model observer with an eye filter for a 5-mm disk with 10 HU contrast. All metrics were compared for NR, NR HR , and NR SHR images reconstructed using filtered back projection (FBP) and an iterative reconstruction algorithm (AIDR3D). We chose a 15% error threshold as a reasonable definition of success for NRsim when compared against actual NR based on published studies showing that a just-noticeable difference in image noise level for human observers is typically <15%. Results: The axial MTF and SSPs for NRsim were in good agreement with NR demonstrated by a maximum difference of 5.1% for the MTF at 10% and 50% across materials (air, Teflon, LDPE, and polystyrene) and a maximum SSP difference of 2.2%. Noise magnitude differences were within 15% across the SSDE levels with the exception of below 4.5 mGy for the lung protocol with FBP. The relative RMSE of normalized NPS comparisons were all <15%. Differences in CT numbers for NRsim reconstructions were within 2 HU of NR. LCD for NRsim was within 15% of NR with the exception of NR SHR for the lung protocol SSDE levels below 3.7 mGy with FBP. Conclusions: NRsim, an algorithm for simulating NR acquisitions using HR and SHR raw data, was introduced and shown to generate images with spatial resolution, noise, HU accuracy, and LCD

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Section: Discussionsupporting

confidence: 49%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validation of synthesized normal‐resolution image data generated from high‐resolution acquisitions on a commercial CT scanner

et al. 2020

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“…Ultra-high-resolution CT images with MI-RT have better spatial resolution due to smaller detector elements and fine x-ray tube focal spot. Oostveen et al found the modulation transfer function of a novel ultra-high-resolution CT to be twice as high as a multidetector CT [105]. Thus, the high resolution and ultra-high-resolution scanning modes can increase the spatial resolution by 200% with only 23% increase in radiation dose for same detectability.…”

Section: Evidence For Dl-based Image Reconstructionmentioning

confidence: 99%

Artificial intelligence in image reconstruction: The change is here

Singh

Wang

et al. 2020

Physica Medica

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Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.

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Three‐dimensional printing of patient‐specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures

et al. 2021

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Purpose: Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures. Methods: PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung. Results: For the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from −800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans. Conclusion:The present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles.PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

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Physical evaluation of an ultra-high-resolution CT scanner

Cited by 56 publications

References 20 publications

Validation of synthesized normal‐resolution image data generated from high‐resolution acquisitions on a commercial CT scanner

Validation of synthesized normal‐resolution image data generated from high‐resolution acquisitions on a commercial CT scanner

Artificial intelligence in image reconstruction: The change is here

Three‐dimensional printing of patient‐specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures

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