Feasibility of Motion Compensation using Inertial Measurement in C-arm CT

Maier, Jennifer; Aichert, André; Mehringer, Wolfgang; Bier, Bastian; Eskofier, Bjoern M.; Levenston, Marc E.; Gold, Garry E.; Fahrig, Rebecca; Bonaretti, Serena; Maier, Andreas

doi:10.1109/nssmic.2018.8824463

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…However, there has been very limited use of inertial sensors for motion tracking in nuclear medicine and CT. The few reports include 3 and 6-axis mechanical gating of cardiac and respiratory motion for SPECT and PET (Jafari Tadi et al 2014Tadi et al , 2017 and monitoring of knee motion in standing patients imaged using C-arm CT (Maier et al 2018).…”

Section: Inertial Sensorsmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

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Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and x-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art deep learning methods may have a unique role to play in this context.

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Section: Inertial Sensorsmentioning

confidence: 99%

Motion estimation and correction in SPECT, PET and CT

Kyme¹,

Fulton²

2021

Phys. Med. Biol.

View full text Add to dashboard Cite

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Toward automatic C-arm positioning for standard projections in orthopedic surgery

et al. 2020

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Purpose Guidance and quality control in orthopedic surgery increasingly rely on intra-operative fluoroscopy using a mobile C-arm. The accurate acquisition of standardized and anatomy-specific projections is essential in this process. The corresponding iterative positioning of the C-arm is error prone and involves repeated manual acquisitions or even continuous fluoroscopy. To reduce time and radiation exposure for patients and clinical staff and to avoid errors in fracture reduction or implant placement, we aim at guiding—and in the long-run automating—this procedure. Methods In contrast to the state of the art, we tackle this inherently ill-posed problem without requiring patient-individual prior information like preoperative computed tomography (CT) scans, without the need of registration and without requiring additional technical equipment besides the projection images themselves. We propose learning the necessary anatomical hints for efficient C-arm positioning from in silico simulations, leveraging masses of 3D CTs. Specifically, we propose a convolutional neural network regression model that predicts 5 degrees of freedom pose updates directly from a first X-ray image. The method is generalizable to different anatomical regions and standard projections. Results Quantitative and qualitative validation was performed for two clinical applications involving two highly dissimilar anatomies, namely the lumbar spine and the proximal femur. Starting from one initial projection, the mean absolute pose error to the desired standard pose is iteratively reduced across different anatomy-specific standard projections. Acquisitions of both hip joints on 4 cadavers allowed for an evaluation on clinical data, demonstrating that the approach generalizes without retraining. Conclusion Overall, the results suggest the feasibility of an efficient deep learning-based automated positioning procedure, which is trained on simulations. Our proposed 2-stage approach for C-arm positioning significantly improves accuracy on synthetic images. In addition, we demonstrated that learning based on simulations translates to acceptable performance on real X-rays.

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Feasibility of Motion Compensation using Inertial Measurement in C-arm CT

Cited by 2 publications

References 7 publications

Motion estimation and correction in SPECT, PET and CT

Motion estimation and correction in SPECT, PET and CT

Toward automatic C-arm positioning for standard projections in orthopedic surgery

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