Reduction of Artifacts Caused by Deep Brain Stimulating Electrodes in Cranial Computed Tomography Imaging by Means of Virtual Monoenergetic Images, Metal Artifact Reduction Algorithms, and Their Combination

Hokamp, Nils Große; Hellerbach, Alexandra; Gierich, Andreas; Jordan, David W.; Visser‐Vandewalle, Veerle; Maintz, David; Haneder, Stefan

doi:10.1097/rli.0000000000000460

Cited by 34 publications

(33 citation statements)

References 37 publications

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“…Preliminary studies showed similar artifact patterns with different scanners (Philips iQon and iCT256, Siemens Somatom Force, Toshiba Aquilion/LB). In addition, the observed artifact patterns of the orientation marker and of the segmented electrodes are still clearly visible even when using different reconstruction methods (e.g., iterative metal artifact reduction algorithm [O-MAR] or virtual monoenergetic images) [21]. Due to the small size of the lead’s anisotropic structures, however, only CT scan protocols with slice thickness < 1 mm appear reasonable.…”

Section: Discussionmentioning

confidence: 99%

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

Hellerbach

Dembek

Hoevels

et al. 2018

Stereotact Funct Neurosurg

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Background: Directional deep brain stimulation (DBS) allows steering the stimulation in an axial direction which offers greater flexibility in programming. However, accurate anatomical visualization of the lead orientation is required for interpreting the observed stimulation effects and to guide programming. Objectives: In this study we aimed to develop and test an accurate and robust algorithm for determining the orientation of segmented electrodes based on standard postoperative CT imaging used in DBS. Methods: Orientation angles of directional leads (Cartesia^TM; Boston Scientific, Marlborough, MA, USA) were determined using CT imaging. Therefore, a sequential algorithm was developed that quantitatively compares the similarity of the observed CT artifacts with calculated artifact patterns based on the lead’s orientation marker and a geometric model of the segmented electrodes. Measurements of seven ground truth phantoms and three leads with 60 different configurations of lead implantation and orientation angles were analyzed for validation. Results: The accuracy of the determined electrode orientation angles was –0.6 ± 1.5° (range: –5.4 to 4.2°). This accuracy proved to be sufficiently high to resolve even subtle differences between individual leads. Conclusions: The presented algorithm is user independent and provides highly accurate results for the orientation of the segmented electrodes for all angular constellations that typically occur in clinical cases.

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Section: Discussionmentioning

confidence: 99%

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

Hellerbach

Dembek

Hoevels

et al. 2018

Stereotact Funct Neurosurg

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“…In all the cases described above, the segmentation process using VMI data was qualitatively reported as easier, faster, and increased the operator’s confidence in obtaining accurate anatomy. Case 4 also demonstrated how imaging artifacts could be reduced on SDCT enabling segmentation and 3D printing from otherwise unsalvageable imaging sources [13–15]. The single segmentation operator commented that without the VMI data in each of the four detailed cases, the requested segmentation may not have been possible and would have resulted in either time-prohibiting manual segmentation or rescanning the desired patient anatomy.…”

Section: Discussionmentioning

confidence: 99%

“…Since the k-edge of iodine lies at 33 keV, photons with 40 keV are more likely to be absorbed resulting in increased attenuation [11]. High keV VMI can be used to reduce image artifacts due to photon starvation [13–15]. These characteristics are highlighted in Additional file 1: Figure S1.…”

Section: Introductionmentioning

confidence: 99%

Utility of virtual monoenergetic images from spectral detector computed tomography in improving image segmentation for purposes of 3D printing and modeling

et al. 2019

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Background One of the key steps in generating three-dimensional (3D) printed models in medicine is segmentation of radiologic imaging. The software tools used for segmentation may be automated, semi-automated, or manual which rely on differences in material density, attenuation characteristics, and/or advanced software algorithms. Spectral Detector Computed Tomography (SDCT) is a form of dual energy computed tomography that works at the detector level to generate virtual monoenergetic images (VMI) at different energies/ kilo-electron volts (keV). These VMI have varying contrast and attenuation characteristics relative to material density. The purpose of this pilot project is to explore the use of VMI in segmentation for medical 3D printing in four separate clinical scenarios. Cases were retrospectively selected based on varying complexity, value of spectral data, and across multiple clinical disciplines (Vascular, Cardiology, Oncology, and Orthopedic). Results In all four clinical cases presented, the segmentation process was qualitatively reported as easier, faster, and increased the operator’s confidence in obtaining accurate anatomy. All cases demonstrated a significant difference in the calculated Hounsfield Units between conventional and VMI data at the level of targeted segmentation anatomy. Two cases would not have been feasible for segmentation and 3D printing using conventional images only. VMI data significantly reduced conventional CT artifacts in one of the cases. Conclusion Utilization of VMI from SDCT can improve and assist the segmentation of target anatomy for medical 3D printing by enhancing material contrast and decreasing CT artifact. Electronic supplementary material The online version of this article (10.1186/s41205-019-0038-y) contains supplementary material, which is available to authorized users.

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“…This result is likely due to advantages regarding image noise enabled by the detector-based approach. 24,31 While we compared VMI 65keV with a state-ofthe-art hybrid iterative reconstruction algorithm, whether most recent model-based image reconstructions can outperform noise reduction enabled by means of VMI remains elusive. 32 So far, only a few studies have investigated dose reduction in head imaging by means of VMI compared with polychromatic CT using a kilovolt-switching dual-energy CT system.…”

Section: Discussionmentioning

confidence: 99%

Virtual Monoenergetic Images from Spectral Detector CT Enable Radiation Dose Reduction in Unenhanced Cranial CT

Reimer

Flatten

Lichtenstein

et al. 2019

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Our aim was to evaluate whether improved gray-white matter differentiation in cranial CT by means of 65-keV virtual monoenergetic images enables a radiation dose reduction compared to conventional images. MATERIALS AND METHODS: One hundred forty consecutive patients undergoing 171 spectral detector CTs of the head between February and November 2017 (56 6 19 years of age; male/female ratio, 56%/44%) were retrospectively included. The tube currenttime product was reduced during the study period, resulting in 61, 55, and 55 patients being examined with 320, 290, and 260 mAs, respectively. All other scanning parameters were kept identical. The volume CT dose index was recorded. ROIs were placed in gray and white matter on conventional images and copied to identical positions in 65-keV virtual monoenergetic images. The contrastto-noise ratio was calculated. Two radiologists blinded to the reconstruction technique evaluated image quality on a 5-point Likert-scale. Statistical assessment was performed using ANOVA and Wilcoxon test adjusted for multiple comparisons.

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Reduction of Artifacts Caused by Deep Brain Stimulating Electrodes in Cranial Computed Tomography Imaging by Means of Virtual Monoenergetic Images, Metal Artifact Reduction Algorithms, and Their Combination

Abstract: The investigated O-MAR algorithm reduces artifacts from DBS electrodes and should be used in the assessment of postoperative patients; however, combination with VMI does not provide an additional benefit.

Cited by 34 publications

References 37 publications

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

DiODe: Directional Orientation Detection of Segmented Deep Brain Stimulation Leads: A Sequential Algorithm Based on CT Imaging

Utility of virtual monoenergetic images from spectral detector computed tomography in improving image segmentation for purposes of 3D printing and modeling

Virtual Monoenergetic Images from Spectral Detector CT Enable Radiation Dose Reduction in Unenhanced Cranial CT

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