Magnetic resonance imaging and the reduction of motion artifacts: review of the principles

Walle, Rik Van de; Lemahieu, Ignace; Achten, Erik

doi:10.3233/thc-1997-5602

Cited by 30 publications

(19 citation statements)

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“…MRI on the other hand has superior contrast resolution for evaluation of wrist injuries and other conditions, therefore is the most commonly used imaging modality for that purpose, and does not involve ionizing radiation. However it suffers from motion (due to longer acquisition times), bias field, non-standardized image values, and partial voluming [44]. A recent method by Włodarczyk et al [46], an expansion on their earlier work [45], first found seed locations using the multi-Otsu thresholding algorithm on a user defined region of interest of the carpal bone area.…”

Section: Discussionmentioning

confidence: 99%

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

Foster

Joshi

Borgese

et al. 2018

Computerized Medical Imaging and Graphics

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Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI. In our method, the boundary of the bones were iteratively found using prior known anatomical constraints and a shape-detection level set. The parameters of the method were optimized using a training dataset of 48 manually segmented carpal bones and evaluated on 112 carpal bones which included both healthy participants without known wrist conditions and participants with thumb basilar osteoarthritis (OA). Manual segmentation by two expert human observers was considered as a reference. On the healthy subject dataset we obtained a Dice overlap of 93.0 ± 3.8, Jaccard Index of 87.3 ± 6.2, and a Hausdorff distance of 2.7 ± 3.4 mm, while on the OA dataset we obtained a Dice overlap of 90.7 ± 8.6, Jaccard Index of 83.0 ± 10.6, and a Hausdorff distance of 4.0 ± 4.4 mm. The short computational time of 20.8 s per bone (or 5.1 s per bone in the parallelized version) and the high agreement with the expert observers gives WRIST the potential to be utilized in musculoskeletal research.

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Section: Discussionmentioning

confidence: 99%

WRIST: A WRist Image Segmentation Toolkit for carpal bone delineation from MRI

Foster

Joshi

Borgese

et al. 2018

Computerized Medical Imaging and Graphics

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“…In an MRI, moving tissue can be imaged with cardiac gating, where several short image phase acquisitions are triggered by the subject's heartbeat (1 cycle). Only objects with a periodic or quasi-periodic motion can be imaged in this manner, 20 which, in this study, referred to a foot being moved by the loading platen. Instead of using a heartbeat, phase synchronization was accomplished with a 2-ms peripheral pulse unit (PPU) signal sent to the MRI scanner from a custom LabVIEW VI via a serial-to-fiber-optic signal converter (Versalink; Electro Standards Laboratory, Cranston, RI).…”

Section: System Designmentioning

confidence: 99%

The design and validation of a magnetic resonance imaging–compatible device for obtaining mechanical properties of plantar soft tissue via gated acquisition

Williams

Stebbins

Cavanagh

et al. 2015

Proc Inst Mech Eng H

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Changes in the mechanical properties of the plantar soft tissue in people with diabetes may contribute to the formation of plantar ulcers. Such ulcers have been shown to be in the causal pathway for lower extremity amputation. The hydraulic plantar soft tissue reducer (HyPSTER) was designed to measure in vivo, rate-dependent plantar soft tissue compressive force and three-dimensional deformations to help understand, predict, and prevent ulcer formation. These patient-specific values can then be used in an inverse finite element analysis to determine tissue moduli, and subsequently used in a foot model to show regions of high stress under a wide variety of loading conditions. The HyPSTER uses an actuator to drive a magnetic resonance imaging-compatible hydraulic loading platform. Pressure and actuator position were synchronized with gated magnetic resonance imaging acquisition. Achievable loading rates were slower than those found in normal walking because of a water-hammer effect (pressure wave ringing) in the hydraulic system when the actuator direction was changed rapidly. The subsequent verification tests were, therefore, performed at 0.2 Hz. The unloaded displacement accuracy of the system was within 0.31%. Compliance, presumably in the system's plastic components, caused a displacement loss of 5.7 mm during a 20-mm actuator test at 1354 N. This was accounted for with a target to actual calibration curve. The positional accuracy of the HyPSTER during loaded displacement verification tests from 3 to 9 mm against a silicone backstop was 95.9% with a precision of 98.7%. The HyPSTER generated minimal artifact in the magnetic resonance imaging scanner. Careful analysis of the synchronization of the HyPSTER and the magnetic resonance imaging scanner was performed. With some limitations, the HyPSTER provided key functionality in measuring dynamic, patient-specific plantar soft tissue mechanical properties.

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“…In practical application, MR image acquisition collects data in the frequency domain (kspace). Lustig et al in [4] proposed a reconstruction model for MR Image. Let be the undersampled Fourier transform, the reconstruction is given by:…”

Section: Cs With Total Variation Minimizationmentioning

confidence: 99%

“…Breath holding during acquisition is one of the methods to reduce image artifact. An overview of reducing motion artifacts principles such as retrospective gating or projection reconstruction were discussed in [4]. Hardware based approach to reduce imaging time using parallel data acquisition (P-MRI) method is proposed in ISSN: 1693-6930 TELKOMNIKA Vol.…”

Section: Introductionmentioning

confidence: 99%