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      Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities : PET/MRI, quantification, attenuation map, attenuation correction, tracer uptake

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      Medical Physics
      Wiley-Blackwell

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          Metal-induced artifacts in MRI.

          The purpose of this article is to review some of the basic principles of imaging and how metal-induced susceptibility artifacts originate in MR images. We will describe common ways to reduce or modify artifacts using readily available imaging techniques, and we will discuss some advanced methods to correct readout-direction and slice-direction artifacts. The presence of metallic implants in MRI can cause substantial image artifacts, including signal loss, failure of fat suppression, geometric distortion, and bright pile-up artifacts. These cause large resonant frequency changes and failure of many MRI mechanisms. Careful parameter and pulse sequence selections can avoid or reduce artifacts, although more advanced imaging methods offer further imaging improvements.
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            MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences.

            One of the challenges in PET/MRI is the derivation of an attenuation map to correct the PET image for attenuation. Different methods have been suggested for deriving the attenuation map from an MR image. Because the low signal intensity of cortical bone on images acquired with conventional MRI sequences makes it difficult to detect this tissue type, these methods rely on some sort of anatomic precondition to predict the attenuation map, raising the question of whether these methods will be usable in the clinic when patients may exhibit anatomic abnormalities. We propose the use of the transverse relaxation rate, derived from images acquired with an ultrashort echo time sequence to classify the voxels into 1 of 3 tissue classes (bone, soft tissue, or air), without making any assumptions on patient anatomy. Each voxel is assigned a linear attenuation coefficient corresponding to its tissue class. A reference CT scan is used to determine the voxel-by-voxel accuracy of the proposed method. The overall accuracy of the MRI-based attenuation correction is evaluated using a method that takes into account the nonlocal effects of attenuation correction. As a proof of concept, the head of a pig was used as a phantom for imaging. The new method yielded a correct tissue classification in 90% of the voxels. Five human brain PET/CT and MRI datasets were also processed, yielding slightly worse voxel-by-voxel performance, compared to a CT-derived attenuation map. The PET datasets were reconstructed using the segmented MRI attenuation map derived with the new method, and the resulting images were compared with segmented CT-based attenuation correction. An average error of around 5% was found in the brain. The feasibility of using the transverse relaxation rate map derived from ultrashort echo time MR images for the estimation of the attenuation map was shown on phantom and clinical brain data. The results indicate that the new method, compared with CT-based attenuation correction, yields clinically acceptable errors. The proposed method does not make any assumptions about patient anatomy and could therefore also be used in cases in which anatomic abnormalities are present.
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              Overcoming artifacts from metallic orthopedic implants at high-field-strength MR imaging and multi-detector CT.

              At magnetic resonance (MR) imaging and multidetector computed tomography (CT), artifacts arising from metallic orthopedic hardware are an obstacle to obtaining optimal images. Although various techniques for reducing such artifacts have been developed and corroborated by previous researchers, a new era of more powerful MR imaging and multidetector CT modalities has renewed the importance of a systematic consideration of methods for artifact reduction. Knowledge of the factors that contribute to artifacts, of related theories, and of artifact reduction techniques has become mandatory for radiologists. Factors that affect artifacts on MR images include the composition of the metallic hardware, the orientation of the hardware in relation to the direction of the main magnetic field, the strength of the magnetic field, the pulse sequence type, and other MR imaging parameters (mainly voxel size, which is determined by the field of view, image matrix, section thickness, and echo train length). At multidetector CT, the factors that affect artifacts include the composition of the hardware, orientation of the hardware, acquisition parameters (peak voltage, tube charge, collimation, and acquired section thickness), and reconstruction parameters (reconstructed section thickness, reconstruction algorithm used, and whether an extended CT scale was used). A comparison of images obtained with different hardware and different acquisition and reconstruction parameters facilitates an understanding of methods for reducing or overcoming artifacts related to metallic implants. (c) RSNA, 2007.
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                Author and article information

                Journal
                Medical Physics
                Med. Phys.
                Wiley-Blackwell
                00942405
                March 2016
                February 2016
                : 43
                : 3
                : 1130-1155
                Article
                10.1118/1.4941014
                26936700
                8957245b-83d2-4b73-a0fb-9ab35ce7524a
                © 2016

                http://doi.wiley.com/10.1002/tdm_license_1.1

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