- Email: [email protected]
Metal artefact reduction algorithm for correction of bone biopsy needle artefact in paediatric C-arm CT images: a qualitative and quantitative assessment
Clinical Radiology 71 (2016) 925e931
Contents lists available at ScienceDirect
Clinical Radiology journal homepage: www.clinicalradiologyonline.net
Technical report
Metal artefact reduction algorithm for correction of bone biopsy needle artefact in paediatric C-arm CT images: a qualitative and quantitative assessment S. Shellikeri a, *, E. Girard b, R. Setser c, J. Bao d, A.M. Cahill a a
The Children’s Hospital of Philadelphia, Department of Radiology, 34th and Civic Center Boulevard, Philadelphia, PA 10914, USA b Siemens Medical Solutions USA, Inc., Healthcare Technology Center, 755 College Road East, Princeton, NJ 08540, USA c Siemens Medical Solutions USA, Inc., Hoffman Estates, IL 60192, USA d Siemens Healthcare, Forchheim, Bayern 91301, Germany
art icl e i nformat ion Article history: Received 29 February 2016 Received in revised form 20 April 2016 Accepted 26 April 2016
Introduction C-arm angiographic systems are being used in the interventional radiology (IR) suite to generate intraprocedural C-arm computed tomography (CT) images, along with digital subtraction angiography and real-time two-dimensional (2D) fluoroscopic imaging.1e3 This information has facilitated treatment planning, procedure guidance, and outcome assessment of interventional procedures, while the patient is still on the IR table.1 C-arm CT technology has directly affected patient care by allowing additional procedures to be performed in the IR suite,2 and one such procedure is percutaneous bone biopsy. Paediatric
* Guarantor and correspondent: S. Shellikeri, The Children’s Hospital of Philadelphia, 34th and Civic Center Boulevard, Philadelphia, PA 10914, USA. Tel.: þ1 760 709 2272. E-mail address: [email protected] (S. Shellikeri).
percutaneous bone biopsies are typically performed with conventional CT-guidance, which is subject to some limitations compared to C-arm CT guidance, such as limited patient access by the operator, potential lack of real-time needle visualisation, unless the system has CT fluoroscopy, relocation of the IR personnel to the CT suite to perform the procedure, and interruption in workflow due to the need to review images on the CT image viewer.4 C-arm CT technology provides intra-procedural tomographic image acquisition and real-time fluoroscopic guidance in the same room without having to relocate the patient.4 Therefore, percutaneous bone biopsies are increasingly being performed in the IR suite using C-arm CT guidance. During percutaneous bone biopsy procedures in the IR suite, post-procedural C-arm CT images can be acquired to confirm the needle placement within the lesion, before obtaining the biopsy sample; however, C-arm CT image quality may be limited by the presence of metallic objects
http://dx.doi.org/10.1016/j.crad.2016.04.021 0009-9260/Ó 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.
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in the field of view. Because of its high X-ray attenuation, the presence of a metallic object, such as the biopsy needle, in the field of view can produce artefacts in C-arm CT images causing degradation in image quality and potentially compromising diagnostic decision-making. Nevertheless, metallic artefacts can be reduced using the iterative reconstruction algorithm.5 The algorithm compensates for beam-hardening and streak artefacts and potentially reveals anatomical details. The purpose of the present study was to evaluate the efficacy of a metal artefact reduction (MAR) prototype algorithm in reducing the biopsy needle artefacts in C-arm CT images that were acquired during percutaneous bone biopsy procedures in children.
Materials and methods C-arm CT image acquisition Percutaneous bone biopsy procedures in which C-arm CT images were acquired in the IR suite at a paediatric institution, as a standard clinical care, were identified and included
in this institutional review board-approved retrospective study (waiver of patient consent/child assent was obtained). Sixteen C-arm CT examinations were acquired during eight bone biopsy procedures, one at the beginning of each procedure for needle path planning, and one with the biopsy needle in place; therefore, each patient served as its own control. In addition, four C-arm CT examinations of an anthropomorphic torso phantom were acquired (two without the biopsy needle and two with the needle). All Carm CT images were acquired using a ceiling-mounted Artis Zee system (syngo DynaCT, VC14, Siemens Healthcare, Forchheim, Germany) with an institution-developed 8-second low-dose protocol (396 projection images per acquisition, 81e90 kV, 0.1/0.17 mGy/p entrance dose). Bonopty bone biopsy needle systems (AprioMed AB, Uppsala, Sweden) were used in all biopsy procedures. The 10 C-arm CT volumes (eight patient and two phantom volumes) that were acquired without the needle were classified as control images. The additional 10 C-arm CT acquisitions with the biopsy needle in place (eight patient and two phantom volumes) were reconstructed using the standard technique (uncorrected) and additionally using the MAR prototype algorithm (corrected).
Figure 1 Graphical representation of the MAR prototype algorithm used to reduce biopsy needle artefact in C-arm CT images acquired during paediatric bone biopsy procedure. Initial C-arm CT image is reconstructed from raw data showing metal artefact. The metal (needle) is segmented, creating a binary volume of the needle, which is then forward-projected onto the first reconstruction to identify the data corrupted by artefacts. The corrupted data are replaced by data along the metallic region boundaries using a nonlinear interpolation procedure. The new MAR corrected volume is reconstructed and the segmented needle is then overlaid back onto the dataset for final reconstruction.
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Figure 2 Six ROIs drawn per dataset. (a) Five ROIs in the axial C-arm CT image, and (b) one ROI in the sagittal image. Four ROIs are placed in close proximity to the needle in the regions with most severe artefacts (i. axial needle front, ii. axial needle left (patient’s left), iii. axial needle right (patient’s right), iv. sagittal needle front). Two ROIs are placed in regions distant from the needle and artefact (v. in distant soft tissue and vi. air).
Figure 3 Uncorrected and corrected C-arm CT images of a 3-year-old male patient who underwent percutaneous bone biopsy of a lytic lesion of the right lateral mass of the L5 vertebra using the Bonopty needle system. (a, b) Uncorrected axial and sagittal C-arm CT images scoring 0 on the image-quality scale. (c, d) MAR-corrected axial and sagittal images scoring 1 on the image-quality scale, suggesting an improvement in image quality post-MAR correction. The corrected images have slightly better soft-tissue visualisation around the targeted lesion with sharper needle margin.
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Image reconstruction and MAR prototype algorithm
Image analysis
All images were transferred to a research workstation (syngo XWP VB21A, Siemens) for post-processing. The 10 images demonstrating needle placement were corrected using the MAR prototype algorithm with an iterative reconstruction and correction method.6 Using the MAR software, the initial step involves reconstructing an uncorrected volume from the raw data containing metal artefacts. The needle is then segmented manually, creating a binary volume of the needle. The binary volume is then forward-projected onto the first reconstruction to identify data corrupted by artefacts. The corrupted data are replaced by data along the metallic region boundaries using a nonlinear interpolation procedure. The new MAR corrected volume is reconstructed. The segmented needle is then overlaid back onto the dataset for final reconstruction. After a final optimisation process reduces residual streaks, the MAR prototype displays the uncorrected and the corrected volume in the Inspace task card, and sends them to the database, which can be seen in the patient browser (Fig 1).
Qualitative assessment Uncorrected and MAR corrected images were evaluated for image quality by two blinded readers in IR (2 years and over 15 years of experience) to reach a consensus opinion, using a three-point image-quality grading system. Imagequality scores from 0e2 were assigned to the images based on the following criteria: 2¼best image quality with absence/minimum artefact and very good visualisation of the needle and soft tissue; 1¼moderate image quality with moderate artefact, but can still visualise the needle and soft tissue; and 0¼poor image quality with massive artefact and inability to visualise the needle or surrounding soft tissues.
Quantitative assessment Six regions of interest (ROI) were created per dataset on the multiplanar reconstruction (MPR) views in identical locations on uncorrected, corrected, and control images: (i) four ROIs were placed in regions in close proximity to the needle with most severe artefacts, and (ii) two in regions distant from the needle and artefact (Fig 2). The mean
Figure 4 Uncorrected and corrected images of a 1.6-year-old male patient who underwent percutaneous biopsy of L4eL5 intervertebral lesion (suggestive of osteomyelitis/discitis) using the Bonopty needle system. (a, b) Uncorrected axial and sagittal C-arm CT images that scored 1 on the image-quality scale. (c, d) MAR-corrected axial and sagittal C-arm CT images scoring 2 on the image-quality scale. The post-correction images have the best image quality with minimum/no artefact and also a very good visualisation of the needle (sharp needle delineation). The femur cortex where the needle was aimed is better visualised in the MAR-corrected images.
S. Shellikeri et al. / Clinical Radiology 71 (2016) 925e931
image intensity values and image noise/error (defined as the standard deviation) in the ROIs were measured.
Statistical analysis Patient and phantom images were considered separately. Within each group, the difference in image noise/error between uncorrected and corrected image ROIs was measured and an average noise reduction was calculated in (i) the needle vicinity and (ii) regions distant from needle. In addition, in each group, mean image intensity values were averaged to get 1 value for each ROI location. The paired ttest was used to compare the mean image intensity values between uncorrected, corrected and control images in (i) the needle vicinity and (ii) regions distant from needle.
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delineation between needle position and high contrast bone with reduction of metal artefact (Figs 3c, 4c, 5b) when compared to the uncorrected images (Figs 3a, 4a, 5a). In several cases, metal artefacts obscured the soft tissue at the needle tip in axial (Figs 3a, 4a) and sagittal images (Figs 3b, 4b). The MAR prototype algorithm reduced this artefact, revealing the soft tissue at the needle tip in both orientations (Figs 3c, 3d, 4c, 4d).
Quantitative assessment A total of 180 ROIs (60 each in the uncorrected, corrected, and control images) were drawn on the MPR views of 30 datasets (10 uncorrected, 10 corrected, and 10 controls).
Patient group
Results
Image noise difference between uncorrected versus corrected images
Qualitative assessment
In the patient group, in regions surrounding the needle, there was a significant reduction in noise in the MARcorrected images (average reduction of 8960; p¼0.009), while in regions distant from the needle, noise reduction was not significant (average reduction of 512; p¼0.3).
In the uncorrected image group, 2/10 images scored 0, 7/ 10 images scored 1, and 1/10 image scored 2. In the corrected image group, no images scored 0, 3/10 images scored 1, and 7/10 images scored 2. Overall, 8/10 images corrected using the MAR algorithm showed an improvement in image quality. An improvement in image quality score from 0 to 1 was observed in two cases (Fig 3), and from 1 to 2 in six cases (Figs 4e5). No change was observed in the image quality of 2/10 patient images, which already had no/minimum artefacts (uncorrected images scored a 1 and 2 on image-quality scale). Metal artefacts degraded the conspicuity of needle edges and obscured the surrounding soft tissue in both the patient (Figs 3a, 4a) and phantom groups (Fig 5a). During imagequality assessment, the physician observed that MARcorrected images had crisp needle edges with clearer
Mean image intensity value comparison (uncorrected versus corrected versus control images) In the patient group, in all ROIs surrounding the needle, the averaged mean image intensity value for the corrected image was closer to the control value than the value for uncorrected image, except for the axial needle left ROI. In regions distant from the needle, no differences in average mean image intensity values were observed between the uncorrected, corrected, and control images, i.e., MAR did not affect the image intensity values or change the image quality of regions distant from the needle/artefact (p>0.05 for all; Fig 6).
Figure 5 Uncorrected and corrected C-arm CT axial images of a Bonopty biopsy needle placed in an anthropomorphic torso phantom. (a) Uncorrected axial C-arm CT image with moderate artefact from the biopsy needle to the right side (arrow). (b) MAR-corrected axial C-arm CT image with best image quality. The uncorrected image scored 1 on the image-quality scale and the corrected image scored 2 suggesting an improvement in image quality. An artefact has been removed from the right side in the corrected image. Crisp needle edges are seen postcorrection.
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Figure 6 Averaged mean image intensity values versus ROI location in patient group in the needle vicinity and in regions distant from needle. The mean image intensity values of eight patients were averaged to get one value for each ROI location.
Phantom group
Mean image intensity value comparison (uncorrected versus corrected versus control images)
Image noise difference between uncorrected versus corrected images
In the phantom group, at all regions surrounding the needle, the averaged mean image intensity value for the corrected image was closer to the control value than the uncorrected value. In regions distant from the needle, no differences in averaged mean image intensity values between the uncorrected, corrected, and control images were observed (p>0.05; Fig 7).
In the phantom group, in regions surrounding the needle, an average noise reduction of 136217 was observed (p¼0.3), while in regions distant from the needle, noise reduction was not significant (average reduction of 1.51.4; p¼0.3).
Figure 7 Averaged mean image intensity values versus ROI location in the phantom group in the needle vicinity and in regions distant from needle. The mean image intensity values of the two phantoms were averaged to get one value for each ROI location.
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Discussion The presence of metallic objects in the imaging field can deteriorate the image quality of C-arm CT images and thus impact diagnosis and/or treatment. Metal artefacts, such as beam-hardening, scatter effects, and Poisson noise are caused by the metal itself, whereas streaks due to undersampling, motion, cone-beam, and windmill artefacts are caused by the metal edges.5 In neuroradiological interventions, artefacts from radio-dense cerebral endovascular and surgical implants, including clips, coils, and stents, have been reported to degrade the C-arm CT images preventing proper diagnosis of areas close to the implants (recognition of complications such as intracranial haemorrhage, residual filling of aneurysms, or thrombus within newly deployed stents).6,7 Previous studies have tested different algorithms to reduce artefacts in neuroradiological applications and have been successful.6e10 In 28 patients who underwent endovascular treatments and shunt drainage for aneurysms/carotid cavernous fistulas, cerebral AVMs, and hydrocephalus, Hung et al.8 demonstrated substantial reduction in streak artefacts around different metallic implants (coiling/stent, liquid embolisers, and ventricular shunting) and improvement in image quality on flat-detector CT. In another study, Prell et al.7 corrected Flat panel detector CT (FDCT) volume scans of seven patients who were treated with neuroradiological intracranial clips/ platinum coils for cerebral aneurysms using a dedicated MAR method. The study showed successful reduction of metal artefact in every case with 27% reduction in overall image noise.7 Psychogios et al.9 demonstrated successful MAR around metallic implants (from coiling, clipping/ stenting) in intravenous contrast agent application flatpanel CT angiograph images of patients treated for intracranial aneurysms and stenosis. The present study shows the efficacy of the MAR prototype algorithm in non-neuroradiology applications, i.e., in body (pelvic/lumbar) C-arm CT images acquired during paediatric bone biopsy procedures. In MAR-corrected images, crisp needle edges were observed with a clearer delineation between needle position and high contrast bone with the removal of metal artefact at the needle edges. The visualisation of this soft-tissue region surrounding the needle is critical as it can demonstrate valuable information about the presence of vessels close to the needle. Further, the removal of metal artefact and improved image quality at
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the needle tip may enhance needle confirmation within the lesion, especially in case of small lesions, and thus may enhance physician’s confidence in reaching the target lesion.11 The MAR algorithm used in the present study is currently a prototype that requires user input for postprocessing and takes approximately 8 minutes per dataset to remove/reduce metal artefact, which is a limitation of this study. The post-processing was performed on an offline research workstation, which limits the near real-time use of this algorithm; however, the product version of this software (syngo DynaCT SMART, Siemens) allows near-real time correction of metal artefacts on a clinical workstation with a click of a button. In conclusion, the MAR algorithm can be used to reduce needle artefacts in C-arm CT images, acquired during interventional paediatric bone biopsy procedures, for better visualisation of the needle and surrounding soft tissue.
References 1. Strobel N, Meissner O, Boese J, et al. Imaging with flat-detector C-arm systems. In: Multislice CT. 3rd ed. Berlin, Germany: Springer; 2009. p. 33e52. 2. Orth RC, Wallace MJ, Kuo MD. Technology Assessment Committee of the Society of Interventional R. C-arm cone-beam CT: general principles and technical considerations for use in interventional radiology. J Vasc Interv Radiol 2008;19(6):814e20. 3. Struffert T, Doerfler A. Flat-detector computed tomography in diagnostic and interventional neuroradiology. Radiologe 2009;49(9):820e9. 4. Racadio JM, Babic D, Homan R, et al. Live 3D guidance in the interventional radiology suite. AJR Am J Roentgenol 2007;189:W357e64. 5. Boas FE, Fleischmann D. CT artifacts: causes and reduction techniques. Imaging Med 2012;4(2):229e40. 6. Stidd DA, Theessen H, Deng Y, et al. Evaluation of a metal artifacts reduction algorithm applied to postinterventional flat panel detector CT imaging. AJNR Am J Neuroradiol 2014;35(11):2164e9. 7. Prell D, Kyriakou Y, Struffert T, et al. Metal artifact reduction for clipping and coiling in interventional C-arm CT. AJNR Am J Neuroradiol 2010;31(4):634e9. 8. Hung SC, Wu CC, Lin CJ, et al. Artifact reduction of different metallic implants in flat detector C-arm CT. AJNR Am J Neuroradiol 2014;35(7):1288e92. 9. Psychogios MN, Scholz B, Rohkohl C, et al. Impact of a new metal artefact reduction algorithm in the noninvasive follow-up of intracranial clips, coils, and stents with flat-panel angiographic CTA: initial results. Neuroradiology 2013;55(7):813e8. 10. van der Bom IMJ, Hou SY, Puri AS, et al. Reduction of coil mass artifacts in high-resolution flat detector conebeam CT of cerebral stent-assisted coiling. AJNR Am J Neuroradiol 2013;34(11):2163e70. 11. McWilliams SR, Murphy KP, Golestaneh S, et al. Reduction of guide needle streak artifact in CT-guided biopsy. J Vasc Interv Radiol 2014;25(12):1929e35.
Contents lists available at ScienceDirect
Clinical Radiology journal homepage: www.clinicalradiologyonline.net
Technical report
Metal artefact reduction algorithm for correction of bone biopsy needle artefact in paediatric C-arm CT images: a qualitative and quantitative assessment S. Shellikeri a, *, E. Girard b, R. Setser c, J. Bao d, A.M. Cahill a a
The Children’s Hospital of Philadelphia, Department of Radiology, 34th and Civic Center Boulevard, Philadelphia, PA 10914, USA b Siemens Medical Solutions USA, Inc., Healthcare Technology Center, 755 College Road East, Princeton, NJ 08540, USA c Siemens Medical Solutions USA, Inc., Hoffman Estates, IL 60192, USA d Siemens Healthcare, Forchheim, Bayern 91301, Germany
art icl e i nformat ion Article history: Received 29 February 2016 Received in revised form 20 April 2016 Accepted 26 April 2016
Introduction C-arm angiographic systems are being used in the interventional radiology (IR) suite to generate intraprocedural C-arm computed tomography (CT) images, along with digital subtraction angiography and real-time two-dimensional (2D) fluoroscopic imaging.1e3 This information has facilitated treatment planning, procedure guidance, and outcome assessment of interventional procedures, while the patient is still on the IR table.1 C-arm CT technology has directly affected patient care by allowing additional procedures to be performed in the IR suite,2 and one such procedure is percutaneous bone biopsy. Paediatric
* Guarantor and correspondent: S. Shellikeri, The Children’s Hospital of Philadelphia, 34th and Civic Center Boulevard, Philadelphia, PA 10914, USA. Tel.: þ1 760 709 2272. E-mail address: [email protected] (S. Shellikeri).
percutaneous bone biopsies are typically performed with conventional CT-guidance, which is subject to some limitations compared to C-arm CT guidance, such as limited patient access by the operator, potential lack of real-time needle visualisation, unless the system has CT fluoroscopy, relocation of the IR personnel to the CT suite to perform the procedure, and interruption in workflow due to the need to review images on the CT image viewer.4 C-arm CT technology provides intra-procedural tomographic image acquisition and real-time fluoroscopic guidance in the same room without having to relocate the patient.4 Therefore, percutaneous bone biopsies are increasingly being performed in the IR suite using C-arm CT guidance. During percutaneous bone biopsy procedures in the IR suite, post-procedural C-arm CT images can be acquired to confirm the needle placement within the lesion, before obtaining the biopsy sample; however, C-arm CT image quality may be limited by the presence of metallic objects
http://dx.doi.org/10.1016/j.crad.2016.04.021 0009-9260/Ó 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.
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in the field of view. Because of its high X-ray attenuation, the presence of a metallic object, such as the biopsy needle, in the field of view can produce artefacts in C-arm CT images causing degradation in image quality and potentially compromising diagnostic decision-making. Nevertheless, metallic artefacts can be reduced using the iterative reconstruction algorithm.5 The algorithm compensates for beam-hardening and streak artefacts and potentially reveals anatomical details. The purpose of the present study was to evaluate the efficacy of a metal artefact reduction (MAR) prototype algorithm in reducing the biopsy needle artefacts in C-arm CT images that were acquired during percutaneous bone biopsy procedures in children.
Materials and methods C-arm CT image acquisition Percutaneous bone biopsy procedures in which C-arm CT images were acquired in the IR suite at a paediatric institution, as a standard clinical care, were identified and included
in this institutional review board-approved retrospective study (waiver of patient consent/child assent was obtained). Sixteen C-arm CT examinations were acquired during eight bone biopsy procedures, one at the beginning of each procedure for needle path planning, and one with the biopsy needle in place; therefore, each patient served as its own control. In addition, four C-arm CT examinations of an anthropomorphic torso phantom were acquired (two without the biopsy needle and two with the needle). All Carm CT images were acquired using a ceiling-mounted Artis Zee system (syngo DynaCT, VC14, Siemens Healthcare, Forchheim, Germany) with an institution-developed 8-second low-dose protocol (396 projection images per acquisition, 81e90 kV, 0.1/0.17 mGy/p entrance dose). Bonopty bone biopsy needle systems (AprioMed AB, Uppsala, Sweden) were used in all biopsy procedures. The 10 C-arm CT volumes (eight patient and two phantom volumes) that were acquired without the needle were classified as control images. The additional 10 C-arm CT acquisitions with the biopsy needle in place (eight patient and two phantom volumes) were reconstructed using the standard technique (uncorrected) and additionally using the MAR prototype algorithm (corrected).
Figure 1 Graphical representation of the MAR prototype algorithm used to reduce biopsy needle artefact in C-arm CT images acquired during paediatric bone biopsy procedure. Initial C-arm CT image is reconstructed from raw data showing metal artefact. The metal (needle) is segmented, creating a binary volume of the needle, which is then forward-projected onto the first reconstruction to identify the data corrupted by artefacts. The corrupted data are replaced by data along the metallic region boundaries using a nonlinear interpolation procedure. The new MAR corrected volume is reconstructed and the segmented needle is then overlaid back onto the dataset for final reconstruction.
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Figure 2 Six ROIs drawn per dataset. (a) Five ROIs in the axial C-arm CT image, and (b) one ROI in the sagittal image. Four ROIs are placed in close proximity to the needle in the regions with most severe artefacts (i. axial needle front, ii. axial needle left (patient’s left), iii. axial needle right (patient’s right), iv. sagittal needle front). Two ROIs are placed in regions distant from the needle and artefact (v. in distant soft tissue and vi. air).
Figure 3 Uncorrected and corrected C-arm CT images of a 3-year-old male patient who underwent percutaneous bone biopsy of a lytic lesion of the right lateral mass of the L5 vertebra using the Bonopty needle system. (a, b) Uncorrected axial and sagittal C-arm CT images scoring 0 on the image-quality scale. (c, d) MAR-corrected axial and sagittal images scoring 1 on the image-quality scale, suggesting an improvement in image quality post-MAR correction. The corrected images have slightly better soft-tissue visualisation around the targeted lesion with sharper needle margin.
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Image reconstruction and MAR prototype algorithm
Image analysis
All images were transferred to a research workstation (syngo XWP VB21A, Siemens) for post-processing. The 10 images demonstrating needle placement were corrected using the MAR prototype algorithm with an iterative reconstruction and correction method.6 Using the MAR software, the initial step involves reconstructing an uncorrected volume from the raw data containing metal artefacts. The needle is then segmented manually, creating a binary volume of the needle. The binary volume is then forward-projected onto the first reconstruction to identify data corrupted by artefacts. The corrupted data are replaced by data along the metallic region boundaries using a nonlinear interpolation procedure. The new MAR corrected volume is reconstructed. The segmented needle is then overlaid back onto the dataset for final reconstruction. After a final optimisation process reduces residual streaks, the MAR prototype displays the uncorrected and the corrected volume in the Inspace task card, and sends them to the database, which can be seen in the patient browser (Fig 1).
Qualitative assessment Uncorrected and MAR corrected images were evaluated for image quality by two blinded readers in IR (2 years and over 15 years of experience) to reach a consensus opinion, using a three-point image-quality grading system. Imagequality scores from 0e2 were assigned to the images based on the following criteria: 2¼best image quality with absence/minimum artefact and very good visualisation of the needle and soft tissue; 1¼moderate image quality with moderate artefact, but can still visualise the needle and soft tissue; and 0¼poor image quality with massive artefact and inability to visualise the needle or surrounding soft tissues.
Quantitative assessment Six regions of interest (ROI) were created per dataset on the multiplanar reconstruction (MPR) views in identical locations on uncorrected, corrected, and control images: (i) four ROIs were placed in regions in close proximity to the needle with most severe artefacts, and (ii) two in regions distant from the needle and artefact (Fig 2). The mean
Figure 4 Uncorrected and corrected images of a 1.6-year-old male patient who underwent percutaneous biopsy of L4eL5 intervertebral lesion (suggestive of osteomyelitis/discitis) using the Bonopty needle system. (a, b) Uncorrected axial and sagittal C-arm CT images that scored 1 on the image-quality scale. (c, d) MAR-corrected axial and sagittal C-arm CT images scoring 2 on the image-quality scale. The post-correction images have the best image quality with minimum/no artefact and also a very good visualisation of the needle (sharp needle delineation). The femur cortex where the needle was aimed is better visualised in the MAR-corrected images.
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image intensity values and image noise/error (defined as the standard deviation) in the ROIs were measured.
Statistical analysis Patient and phantom images were considered separately. Within each group, the difference in image noise/error between uncorrected and corrected image ROIs was measured and an average noise reduction was calculated in (i) the needle vicinity and (ii) regions distant from needle. In addition, in each group, mean image intensity values were averaged to get 1 value for each ROI location. The paired ttest was used to compare the mean image intensity values between uncorrected, corrected and control images in (i) the needle vicinity and (ii) regions distant from needle.
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delineation between needle position and high contrast bone with reduction of metal artefact (Figs 3c, 4c, 5b) when compared to the uncorrected images (Figs 3a, 4a, 5a). In several cases, metal artefacts obscured the soft tissue at the needle tip in axial (Figs 3a, 4a) and sagittal images (Figs 3b, 4b). The MAR prototype algorithm reduced this artefact, revealing the soft tissue at the needle tip in both orientations (Figs 3c, 3d, 4c, 4d).
Quantitative assessment A total of 180 ROIs (60 each in the uncorrected, corrected, and control images) were drawn on the MPR views of 30 datasets (10 uncorrected, 10 corrected, and 10 controls).
Patient group
Results
Image noise difference between uncorrected versus corrected images
Qualitative assessment
In the patient group, in regions surrounding the needle, there was a significant reduction in noise in the MARcorrected images (average reduction of 8960; p¼0.009), while in regions distant from the needle, noise reduction was not significant (average reduction of 512; p¼0.3).
In the uncorrected image group, 2/10 images scored 0, 7/ 10 images scored 1, and 1/10 image scored 2. In the corrected image group, no images scored 0, 3/10 images scored 1, and 7/10 images scored 2. Overall, 8/10 images corrected using the MAR algorithm showed an improvement in image quality. An improvement in image quality score from 0 to 1 was observed in two cases (Fig 3), and from 1 to 2 in six cases (Figs 4e5). No change was observed in the image quality of 2/10 patient images, which already had no/minimum artefacts (uncorrected images scored a 1 and 2 on image-quality scale). Metal artefacts degraded the conspicuity of needle edges and obscured the surrounding soft tissue in both the patient (Figs 3a, 4a) and phantom groups (Fig 5a). During imagequality assessment, the physician observed that MARcorrected images had crisp needle edges with clearer
Mean image intensity value comparison (uncorrected versus corrected versus control images) In the patient group, in all ROIs surrounding the needle, the averaged mean image intensity value for the corrected image was closer to the control value than the value for uncorrected image, except for the axial needle left ROI. In regions distant from the needle, no differences in average mean image intensity values were observed between the uncorrected, corrected, and control images, i.e., MAR did not affect the image intensity values or change the image quality of regions distant from the needle/artefact (p>0.05 for all; Fig 6).
Figure 5 Uncorrected and corrected C-arm CT axial images of a Bonopty biopsy needle placed in an anthropomorphic torso phantom. (a) Uncorrected axial C-arm CT image with moderate artefact from the biopsy needle to the right side (arrow). (b) MAR-corrected axial C-arm CT image with best image quality. The uncorrected image scored 1 on the image-quality scale and the corrected image scored 2 suggesting an improvement in image quality. An artefact has been removed from the right side in the corrected image. Crisp needle edges are seen postcorrection.
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Figure 6 Averaged mean image intensity values versus ROI location in patient group in the needle vicinity and in regions distant from needle. The mean image intensity values of eight patients were averaged to get one value for each ROI location.
Phantom group
Mean image intensity value comparison (uncorrected versus corrected versus control images)
Image noise difference between uncorrected versus corrected images
In the phantom group, at all regions surrounding the needle, the averaged mean image intensity value for the corrected image was closer to the control value than the uncorrected value. In regions distant from the needle, no differences in averaged mean image intensity values between the uncorrected, corrected, and control images were observed (p>0.05; Fig 7).
In the phantom group, in regions surrounding the needle, an average noise reduction of 136217 was observed (p¼0.3), while in regions distant from the needle, noise reduction was not significant (average reduction of 1.51.4; p¼0.3).
Figure 7 Averaged mean image intensity values versus ROI location in the phantom group in the needle vicinity and in regions distant from needle. The mean image intensity values of the two phantoms were averaged to get one value for each ROI location.
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Discussion The presence of metallic objects in the imaging field can deteriorate the image quality of C-arm CT images and thus impact diagnosis and/or treatment. Metal artefacts, such as beam-hardening, scatter effects, and Poisson noise are caused by the metal itself, whereas streaks due to undersampling, motion, cone-beam, and windmill artefacts are caused by the metal edges.5 In neuroradiological interventions, artefacts from radio-dense cerebral endovascular and surgical implants, including clips, coils, and stents, have been reported to degrade the C-arm CT images preventing proper diagnosis of areas close to the implants (recognition of complications such as intracranial haemorrhage, residual filling of aneurysms, or thrombus within newly deployed stents).6,7 Previous studies have tested different algorithms to reduce artefacts in neuroradiological applications and have been successful.6e10 In 28 patients who underwent endovascular treatments and shunt drainage for aneurysms/carotid cavernous fistulas, cerebral AVMs, and hydrocephalus, Hung et al.8 demonstrated substantial reduction in streak artefacts around different metallic implants (coiling/stent, liquid embolisers, and ventricular shunting) and improvement in image quality on flat-detector CT. In another study, Prell et al.7 corrected Flat panel detector CT (FDCT) volume scans of seven patients who were treated with neuroradiological intracranial clips/ platinum coils for cerebral aneurysms using a dedicated MAR method. The study showed successful reduction of metal artefact in every case with 27% reduction in overall image noise.7 Psychogios et al.9 demonstrated successful MAR around metallic implants (from coiling, clipping/ stenting) in intravenous contrast agent application flatpanel CT angiograph images of patients treated for intracranial aneurysms and stenosis. The present study shows the efficacy of the MAR prototype algorithm in non-neuroradiology applications, i.e., in body (pelvic/lumbar) C-arm CT images acquired during paediatric bone biopsy procedures. In MAR-corrected images, crisp needle edges were observed with a clearer delineation between needle position and high contrast bone with the removal of metal artefact at the needle edges. The visualisation of this soft-tissue region surrounding the needle is critical as it can demonstrate valuable information about the presence of vessels close to the needle. Further, the removal of metal artefact and improved image quality at
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the needle tip may enhance needle confirmation within the lesion, especially in case of small lesions, and thus may enhance physician’s confidence in reaching the target lesion.11 The MAR algorithm used in the present study is currently a prototype that requires user input for postprocessing and takes approximately 8 minutes per dataset to remove/reduce metal artefact, which is a limitation of this study. The post-processing was performed on an offline research workstation, which limits the near real-time use of this algorithm; however, the product version of this software (syngo DynaCT SMART, Siemens) allows near-real time correction of metal artefacts on a clinical workstation with a click of a button. In conclusion, the MAR algorithm can be used to reduce needle artefacts in C-arm CT images, acquired during interventional paediatric bone biopsy procedures, for better visualisation of the needle and surrounding soft tissue.
References 1. Strobel N, Meissner O, Boese J, et al. Imaging with flat-detector C-arm systems. In: Multislice CT. 3rd ed. Berlin, Germany: Springer; 2009. p. 33e52. 2. Orth RC, Wallace MJ, Kuo MD. Technology Assessment Committee of the Society of Interventional R. C-arm cone-beam CT: general principles and technical considerations for use in interventional radiology. J Vasc Interv Radiol 2008;19(6):814e20. 3. Struffert T, Doerfler A. Flat-detector computed tomography in diagnostic and interventional neuroradiology. Radiologe 2009;49(9):820e9. 4. Racadio JM, Babic D, Homan R, et al. Live 3D guidance in the interventional radiology suite. AJR Am J Roentgenol 2007;189:W357e64. 5. Boas FE, Fleischmann D. CT artifacts: causes and reduction techniques. Imaging Med 2012;4(2):229e40. 6. Stidd DA, Theessen H, Deng Y, et al. Evaluation of a metal artifacts reduction algorithm applied to postinterventional flat panel detector CT imaging. AJNR Am J Neuroradiol 2014;35(11):2164e9. 7. Prell D, Kyriakou Y, Struffert T, et al. Metal artifact reduction for clipping and coiling in interventional C-arm CT. AJNR Am J Neuroradiol 2010;31(4):634e9. 8. Hung SC, Wu CC, Lin CJ, et al. Artifact reduction of different metallic implants in flat detector C-arm CT. AJNR Am J Neuroradiol 2014;35(7):1288e92. 9. Psychogios MN, Scholz B, Rohkohl C, et al. Impact of a new metal artefact reduction algorithm in the noninvasive follow-up of intracranial clips, coils, and stents with flat-panel angiographic CTA: initial results. Neuroradiology 2013;55(7):813e8. 10. van der Bom IMJ, Hou SY, Puri AS, et al. Reduction of coil mass artifacts in high-resolution flat detector conebeam CT of cerebral stent-assisted coiling. AJNR Am J Neuroradiol 2013;34(11):2163e70. 11. McWilliams SR, Murphy KP, Golestaneh S, et al. Reduction of guide needle streak artifact in CT-guided biopsy. J Vasc Interv Radiol 2014;25(12):1929e35.