Lars Gjesteby

Metal Artifact Reduction in CT: Where Are We After Four Decades?

Methods to overcome metal artifacts in computed tomography (CT) images have been researched and developed for nearly 40 years. When X-rays pass through a metal object, depending on its size and density, different physical effects will negatively affect the measurements, most notably beam hardening, scatter, noise, and the non-linear partial volume effect. These phenomena severely degrade image quality and hinder the diagnostic power and treatment outcomes in many clinical applications. In this paper, we first review the fundamental causes of metal artifacts, categorize metal object types, and present recent trends in the CT metal artifact reduction (MAR) literature. To improve image quality and recover information about underlying structures, many methods and correction algorithms have been proposed and tested. We comprehensively review and categorize these methods into six different classes of MAR: metal implant optimization, improvements to the data acquisition process, data correction based on physics models, modifications to the reconstruction algorithm (projection completion and iterative reconstruction), and image-based post-processing. The primary goals of this paper are to identify the strengths and limitations of individual MAR methods and overall classes, and establish a relationship between types of metal objects and the classes that most effectively overcome their artifacts. The main challenges for the field of MAR continue to be cases with large, dense metal implants, as well as cases with multiple metal objects in the field of view. Severe photon starvation is difficult to compensate for with only software corrections. Hence, the future of MAR seems to be headed toward a combined approach of improving the acquisition process with dual-energy CT, higher energy X-rays, or photon-counting detectors, along with advanced reconstruction approaches. Additional outlooks are addressed, including the need for a standardized evaluation system to compare MAR methods.

Metal artifacts in computed tomography for radiation therapy planning: dosimetric effects and impact of metal artifact reduction

A significant and increasing number of patients receiving radiation therapy present with metal objects close to, or even within, the treatment area, resulting in artifacts in computed tomography (CT) imaging, which is the most commonly used imaging method for treatment planning in radiation therapy. In the presence of metal implants, such as dental fillings in treatment of head-and-neck tumors, spinal stabilization implants in spinal or paraspinal treatment or hip replacements in prostate cancer treatments, the extreme photon absorption by the metal object leads to prominent image artifacts. Although current CT scanners include a series of correction steps for beam hardening, scattered radiation and noisy measurements, when metal implants exist within or close to the treatment area, these corrections do not suffice. CT metal artifacts affect negatively the treatment planning of radiation therapy either by causing difficulties to delineate the target volume or by reducing the dose calculation accuracy. Various metal artifact reduction (MAR) methods have been explored in terms of improvement of organ delineation and dose calculation in radiation therapy treatment planning, depending on the type of radiation treatment and location of the metal implant and treatment site. Including a brief description of the available CT MAR methods that have been applied in radiation therapy, this article attempts to provide a comprehensive review on the dosimetric effect of the presence of CT metal artifacts in treatment planning, as reported in the literature, and the potential improvement suggested by different MAR approaches. The impact of artifacts on the treatment planning and delivery accuracy is discussed in the context of different modalities, such as photon external beam, brachytherapy and particle therapy, as well as by type and location of metal implants.

Deep learning methods to guide CT image reconstruction and reduce metal artifacts

The rapidly-rising field of machine learning, including deep learning, has inspired applications across many disciplines. In medical imaging, deep learning has been primarily used for image processing and analysis. In this paper, we integrate a convolutional neural network (CNN) into the computed tomography (CT) image reconstruction process. Our first task is to monitor the quality of CT images during iterative reconstruction and decide when to stop the process according to an intelligent numerical observer instead of using a traditional stopping rule, such as a fixed error threshold or a maximum number of iterations. After training on ground truth images, the CNN was successful in guiding an iterative reconstruction process to yield high-quality images. Our second task is to improve a sinogram to correct for artifacts caused by metal objects. A large number of interpolation and normalization-based schemes were introduced for metal artifact reduction (MAR) over the past four decades. The NMAR algorithm is considered a state-of-the-art method, although residual errors often remain in the reconstructed images, especially in cases of multiple metal objects. Here we merge NMAR with deep learning in the projection domain to achieve additional correction in critical image regions. Our results indicate that deep learning can be a viable tool to address CT reconstruction challenges.

Hybrid Imaging System for Simultaneous Spiral MR and X-ray (MRX) Scans

The needs and the feasibility of simultaneous computed tomography (CT) and magnetic resonance imaging (MRI) were recently reported. In this paper, a spiral magnetic resonance X-ray CT (MRX) imaging system is proposed for head and extremities imaging, which serves as a simple, cost-effective solution on the path to a full-scale CT-MRI fusion. While MRI and X-ray radiography were integrated before, we propose novel designs to acquire simultaneous CT and MR views for synchronized radiographic imaging or joint tomographic reconstruction. Our preliminary permanent magnet configurations achieve a magnetic field strength between 0.1 and 0.2 T while keeping weight low enough for portability. We have also shown that a field strength up to 0.35 T is achievable with permanent magnets that maintain a compact profile, though increased weight would hinder ease of transportation. Simulation results of a joint tomographic reconstruction scheme show the advantage of simultaneously acquired images. The proposed MRX system performs double helical scans in CT and MRI mechanisms, and has multiple niche applications, such as medical imaging on disaster sites, in battle fields, and for under-developed regions.

Deep learning methods for CT image-domain metal artifact reduction

Artifacts resulting from metal objects have been a persistent problem in CT images over the last four decades. A common approach to overcome their effects is to replace corrupt projection data with values synthesized from an interpolation scheme or by reprojection of a prior image. State-of-the-art correction methods, such as the interpolation- and normalization-based algorithm NMAR, often do not produce clinically satisfactory results. Residual image artifacts remain in challenging cases and even new artifacts can be introduced by the interpolation scheme. Metal artifacts continue to be a major impediment, particularly in radiation and proton therapy planning as well as orthopedic imaging. A new solution to the long-standing metal artifact reduction (MAR) problem is deep learning, which has been successfully applied to medical image processing and analysis tasks. In this study, we combine a convolutional neural network (CNN) with the state-of-the-art NMAR algorithm to reduce metal streaks in critical image regions. Training data was synthesized from CT simulation scans of a phantom derived from real patient images. The CNN is able to map metal-corrupted images to artifact-free monoenergetic images to achieve additional correction on top of NMAR for improved image quality. Our results indicate that deep learning is a novel tool to address CT reconstruction challenges, and may enable more accurate tumor volume estimation for radiation therapy planning.

MO-FG-CAMPUS-IeP2-05: Feasibility Demonstration of High-Voltage Clinical CT and Impact On X-Ray Penetration Through Metal Objects

PURPOSE To demonstrate the possibility and quantify the impact of operating a clinical CT scanner at exceptionally high x-ray tube voltage for better penetration through metal objects and facilitating metal artifact reduction. METHODS We categorize metal objects according to the data corruption severeness (level of distortion and complete photon starvation fraction). To demonstrate feasibility and investigate the impact of high voltage scanning we modified a commercial GE LightSpeed VCT scanner (generator and software) to enable CT scans with x-ray tube voltages as high as 175 kVp. A 20 cm diameter water phantom with two metal rods (10 mm stainless and 25 mm titanium) and a water phantom with realistic metal object (spine cage) were used to evaluate the data corruption and image artifacts in the absence of any algorithm correction. We also performed simulations to confirm our understanding of the transmitted photon levels through metal objects with different size and composition. RESULTS The reconstructed images at 175 kVp still have significant dark shading artifacts, as expected since no special scatter correction or beam hardening was performed but show substantially lower noise and photon starvation than at lower kVp due to better beam penetration. Analysis of the raw data shows that the photon starved data is reduced from over 4% at 140 kVp to below 0.2% at 175 kVp. The simulations indicate that for clinically relevant titanium and stainless objects a 175 kVp tube voltage effectively avoids photon starvation. CONCLUSION The use of exceptionally high tube voltage on a clinical CT system is a practical and effective solution to avoid photon starvation caused by certain metal implants. Sparse and hybrid high-voltage protocols are being considered to maintain low patient dose. This opens the door to algorithmic physics-based corrections rather than treating the data as missing and relying on missing data algorithms. Some of the authors are employees of General Electric.

Low-field designs for interior MRI and CT coupling

The combination of CT and MRI for simultaneous image acquisition has not yet been successfully prototyped. To overcome the geometric conflict and electromagnetic interference between CT and MRI hardware, low-field magnet arrays are utilized. Using 3D magnetic field modeling software, several permanent magnet array designs are proposed yielding homogeneous field magnitudes of ~0.3-0.35 T. The results demonstrate potential configurations to enable interior MRI while working synergistically with x-ray hardware for simultaneous data acquisition.

Metal artifact reduction for radiation therapy: a simulation study

Metal artifacts have been a challenge in computed tomography (CT) for nearly four decades. Despite intensive research in this area, challenges still exist in commercial metal artifact reduction (MAR) solutions. MAR is particularly important for radiation therapy and proton therapy treatment planning because metal artifacts not only degrade the outline of tumors and sensitive organs, but also introduce errors in stopping power estimation, compromising dose prediction accuracy. In this study, we developed a MAR approach that combines hardware and algorithmic innovations to systematically tackle the challenge of metal artifacts in radiation therapy. We propose to operate the X-ray tube at exceptionally high voltage and the detector DAS with adaptive triggering rate to prevent photon starvation in the CT raw data, followed by physics-based sinogram domain precorrection and model-based iterative reconstruction to correct the metal artifacts. We performed an end-to-end simulation of the integrated MAR approach with advanced hardware and algorithmic solutions. We simulated 700mAs/140 kVp and 550mAs/180 kVp CT scans, 984 views, with and without adaptive triggering, of an image volume based on the Visible Human Project CT data set, and after inserting two Titanium hip prostheses. The results demonstrated that the proposed MAR scheme can effectively eliminate metal artifacts and improve the accuracy of proton therapy planning. The dosimetric evaluation showed that with the proposed MAR solution, the error in range calculation was reduced from 7 mm to <1 mm.

Numerical study on simultaneous emission and transmission tomography in the MRI framework

Multi-modality imaging methods are instrumental for advanced diagnosis and therapy. Specifically, a hybrid system that combines computed tomography (CT), nuclear imaging, and magnetic resonance imaging (MRI) will be a Holy Grail of medical imaging, delivering complementary structural/morphological, functional, and molecular information for precision medicine. A novel imaging method was recently demonstrated that takes advantage of radiotracer polarization to combine MRI principles with nuclear imaging. This approach allows the concentration of a polarized Υ-ray emitting radioisotope to be imaged with MRI resolution potentially outperforming the standard nuclear imaging mode at a sensitivity significantly higher than that of MRI. In our work, we propose to acquire MRI-modulated nuclear data for simultaneous image reconstruction of both emission and transmission parameters, suggesting the potential for simultaneous CT-SPECT-MRI. The synchronized diverse datasets allow excellent spatiotemporal registration and unique insight into physiological and pathological features. Here we describe the methodology involving the system design with emphasis on the formulation for tomographic images, even when significant radiotracer signals are limited to a region of interest (ROI). Initial numerical results demonstrate the feasibility of our approach for reconstructing concentration and attenuation images through a head phantom with various radio-labeled ROIs. Additional considerations regarding the radioisotope characteristics are also discussed.

Investigation into multiphysics coupling via semiconducting nanophosphors

In an attempt to strengthen the case for the design and manufacture of multi-modal imaging machines capable of simultaneous and synergistic computed tomography (CT) and magnetic resonance imaging (MRI) as proposed by Ge Wang as early as 2012, a series of experiments using various combinations of nanophosphors, agar gels, a fiber optic coupled UV LED, and micro-MRI have been proposed and performed. The aim of these experiments is to elucidate the ability, if any, of UV-excited nanophosphors to modulate MRI parameters (T1, T2, and T2*). The majority of our findings have been difficult to interpret, although some hints of the coupling were seen in previously reported studies. This paper briefly describes the next set experiments and discusses the improvements that need to be made to achieve statistical confidence in any phenomena that may be recorded. This work is an important step in the coupling of seemingly unrelated imaging modalities and could serve as a stepping-stone for further insight into the future of imaging.