Minor roject

            My research question is the new iterative reconstruction technique suppresses the metal streak artifact in computed tomography images. It should improve image quality and diagnostic confidence of the metallic artifact region in the routine clinical application. Metal streak artifacts are major problem in computed tomography because they result in dark and bright shading between metal objects.

            Many researchers who have looked at this subject are Boas F Edward and Joemai Raoul M S for instance. They argue that metal streak artifacts can also suppress by using iterative reconstruction techniques, resulting in more accurate diagnosis.

            Boas et al (2012) argues the different types of computed tomography artifacts including noise, beam hardening, scatter, motion and metal artifacts. They review the causes and methods to suppress the artifact. They conclude that iterative reconstruction technique can suppress metal streak artifact and improve image quality.

            Joemai et al (2012) argues that metal streak artifact were suppressed by applying correction in Radon transformation of the computed tomography images and forward projection with applying corrections in the scanner’s original raw data.

            Debate centers on the basic issue of metal artifact suppression are importance because it can improve image quality, accuracy and confidence diagnostic in computed tomography images.

            There is still work to be done on the techniques that can be suppressed metal streak artifact because current techniques and methods for suppression metal artifacts in computed tomography images have not achieved widespread clinic use and some techniques can produce new artifacts in the computed tomography images.

            My research is closer to Boas’s research because I will be used the iterative reconstruction technique to suppress metal streak artifact in computed tomography. However, I will add function and develop algorithm to suppress metal artifact to obtain the better image quality and more suppress metal artifact than Boas’s method.

            Hopefully my contribution will be improved diagnostic confidence and image quality in term of metal artifact suppression in computed tomography images. I will be implemented the new iterative reconstruction algorithm at the Department of Radiology, King Chulalongkorn Memorial Hospital for suppression of metal streak artifact in the routine clinic application.

Reference list

Boas, E. F. & Fleischmann D. (2012). CT artifacts: causes and reduction techniques. Imaging in Medicine, 4(2), 229-240.

Joemai R.M.S., Bruin P.W.D., Veldkamp W.J.H. & Geleijns J.  (2012). Metal artifact reduction for CT: development, implementation, and clinical comparison of a generic and a scanner-specific technique. Medical Physics, 39(2), 1125-1132

Writing an introduction

Introduction

            Metal streak artifact is the major problem in the computed tomography. It appears as bright and dark streaks throughout the cross section images and around the metal. It is caused by multiple mechanisms, some of which are related to the metal itself, and some of which are related to metal edges. The metal itself causes beam hardening, scatter effects and Poisson noise. The metal edges causes streaks due to under sampling, motion, cone beam, and windmill artifacts.  Beam hardening and scatter result in dark streak between metal. Various techniques for metal artifact reduction have been introduced in the literature for improved computed tomography image quality. Boas (2011) reported that metal artifacts due to photon starvation, beam hardening, and motion can suppress by his metal artifact reduction technique (p.894-902). Raoul (2012) noted that quantitative assessment of clinical images demonstrated improved image quality for Radon transformation and forward projection by using scanner’s original raw data techniques of metal artifact reduction but the technique that using scanner’s original raw data showed better image quality than the other technique (p.1125-1132). Koehler (2012) presented a new technique for suppress metal artifact, which is based on a sinogram interpolation technique. All of the current techniques and methods for reducing metal artifacts in computed tomography images have not achieved widespread in clinical use and some techniques can produce new artifacts in the computed tomography images.

            The aim of this study is to develop the computer software to suppress the metal artifact in the computed tomography images by iterative reconstruction technique. It should be improve image quality and diagnostic confidence of the metallic artifact region in the routine clinical application. The new iterative reconstruction algorithm will be implemented to Department of Radiology, King Chulalongkorn Memorial Hospital for reduction the metal artifact in the patients.

Assignment 1: Citation

Evaluation of Two Iterative Techniques for Reducing Metal Artifacts in Computed Tomography

F. Edward Boas , MD , PhD, Dominik Fleischmann , MD

Purpose: To evaluate two methods for reducing metal artifacts in computed tomography (CT) the metal deletion technique (MDT) and the selective algebraic reconstruction technique (SART) and compare these methods with filtered back projection (FBP) and linear interpolation (LI).

Materials and Methods: The institutional review board approved this retrospective HIPAA compliant study; informed patient consent was waived. Simulated projection data were calculated for a phantom that contained water, soft tissue, bone, and iron. Clinical  projection data were obtained retrospectively from 11 consecutively identified CT scans with metal streak artifacts, with a total of 178 sections containing metal. Each scan was reconstructed using FBP, LI, SART, and MDT. The simulated scans were evaluated quantitatively by calculating the average error in Hounsfield units for each pixel compared with the original phantom. Two radiologists who were blinded to the reconstruction algorithms used qualitatively evaluated the clinical scans, ranking the overall severity of artifacts for each algorithm. P values for comparisons of the image quality ranks were calculated from the binomial distribution.

Results: The simulations showed that MDT reduces artifacts due to photon starvation, beam hardening, and motion and does not introduce new streaks between metal and bone. MDT had the lowest average error (76% less than FBP, 42% less than LI, 17% less than SART). Blinded comparison of the clinical scans revealed that MDT had the best image quality 100% of the time (95% confidence interval: 72%, 100%). LI had the second best image quality, and SART and FBP had the worst image quality. On images from two CT scans, as compared with images generated by the scanner, MDT revealed information of potential clinical importance.

Conclusion: For a wide range of scans, MDT yields reduced metal streak artifacts and better-quality images than does FBP, LI, or SART.

Reference: Boas EF. and Fleischmann D.(2011). Evaluation of Two Iterative Techniques for Reducing Metal Artifacts in Computed Tomography. Radiology, 259(3), 894-902.

Citation

(1) Boas EF and Fleischmann D (2011) report that "MDT reduces artifacts due to photon starvation, beam hardening, and motion and does not introduce new streaks between metal and bone" (p.894-902).

(2) Boas EF and Fleischmann D (2011) note that metal artifacts due to photon starvation, beam hardening, and motion was suppressed by metal artifact reduction (MDT) technique (p.894-902).