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Assessing MRI susceptibility artefact through an indicator of image distortion

  • Alfredo Illanes EMAIL logo , Johannes W. Krug and Michael Friebe
Published/Copyright: September 30, 2016
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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Susceptibility artefacts in magnetic resonance imaging (MRI) caused by medical devices can result in a severe degradation of the MR image quality. The quantification of susceptibility artefacts is regulated by the ASTM standard which defines a manual method to assess the size of an artefact. This means that the estimated artefact size can be user dependent. To cope with this problem, we propose an algorithm to automatically quantify the size of such susceptibility artefacts. The algorithm is based on the analysis of a 3D surface generated from the 2D MR images. The results obtained by the automatic algorithm were compared to the manual measurements performed by study participants. The results show that the automatic and manual measurements follow the same trend. The clear advantage of the automated algorithm is the absence of the inter- and intra-observer variability. In addition, the algorithm also detects the slice containing the largest artefact which was not the case for the manual measurements.

Keywords: artefact assessment; MRI; susceptibility

1 Introduction

When magnetic resonance imaging (MRI) is used as a modality for guiding a minimally invasive procedures, materials used to fabricate catheters or biopsy needles have to be chosen such that their magnetic susceptibility does not distort the acquired MR images. Such susceptibility artefacts can make MRI unusable or harm its use as a guiding modality in minimally invasive interventions such as liver or breast biopsies. It can affect the image quality if the area of interest is close to the position of the passive instrument [1], [2].

To not significantly affect the quality of the diagnostic information of the MR images, testing of susceptibility artifacts arising from passive instruments is important before introducing them into the MRI environment [3], [4]. Therefore, the precise measurement of the artefact size is important during the development stage of medical tools to be used in MRI. The quantification of artefacts caused by such devices is regulated by the ASTM F2119 standard [5]. The ASTM defines a quantitative but manual method to assess the artefact, where the definition of the artefact is clearly defined but gives only an approximative method for its quantification [6]. Hence, it can be assumed that observed artefact dimensions are user dependent despite a standard exists for reducing subjectivity.

Some authors have proposed different methods to assess artifacts using mainly software measurement tools [7], [8], while other works proposed to automatize the standard measurement objective and reproducible measurement that comply with this standard [6], [9], [10].

This work investigates whether an automatic algorithm can be used as an alternative for measuring susceptibility artefacts of passive instruments used in MRI. The algorithm is based on the computation of a quantitative indicator related to the amount of distortion produced by an instrument on a three-dimensional (3D) surface obtained from the 2D MR image.

For the evaluation of the proposed algorithm, six MRI compatible biopsy needles were used for artefact assessment. The results show that an indicator can be computed automatically from all the slices of a given MRI sequence and that a value computed from the indicator is correlated with the average of the manual measurements.

2 Method

2.1 Data acquisition and manual measurements setup

Six different MR compatible biopsy needles with outer diameters of 1 mm to 1.3 mm (ITP GmbH, Somatex GmbH, Germany) were imaged in a 3T MRI scanner (Magnetom Skyra, Siemens) using a FLASH sequence (see Table 1). A Plexiglas phantom filled with a solution of water and copper sulphate was used for the experiments [5].

Table 1

Parameters of the MRI sequence used to acquire the images of the different biopsy needles.

TR [ms]TE [ms]Flip angle [°]Slice

Thk [mm]		Resolution

[mm×mm]
521011

Artefacts were manually analysed by three MRI or image analysis experts. For each needle configuration, the participants had to select one slice for measuring the artefact size. Each artefact was measured twice by each participant (with a pause of one day between the measurements) following [5] using a DICOM viewer (RadiAnt DICOM). In summary, every participant made six artefact measurements in each of the two measurement series and therefore six measurements are available for each needle.

2.2 Image distortion indicator computation

The standard 2D MR image depicted in Figure 1A shows the artefact of a biopsy needle. The artefact can be represented as a 3D surface version of the MR image by representing the values of each pixel (being located in the x-y plane) along the z-axis (see Figure 1B). In this 3D surface version the susceptibility artefact can be viewed as a distortion of the 3D surface image background. This distortion appears as a downward deflection of the background. It seems that the artefact observed in a 3D surface point of view has a more complicated nature than the traditional 2D image, therefore the main idea is to quantify this deflection of the background using a quantitative indicator of background distortion. For that we propose to calculate an indicator related to the volume resulting from the background deflection produced by the artefact in the 3D surface image.

Figure 1 (A) Standard 2D MR image of a needle, (B) 3D surface representation of the 2D MRI, (C) obtained 3D surface after attenuation of low frequency trend and removal of surface offset.
Figure 1

(A) Standard 2D MR image of a needle, (B) 3D surface representation of the 2D MRI, (C) obtained 3D surface after attenuation of low frequency trend and removal of surface offset.

Before doing this, it is important to observe in Figure 1B that the surface baseline is located above zero and that it has non-homogeneous characteristics with low frequency trends. This last feature can be explained by the location of the Plexiglas phantom with respect to the RF coil position and its inhomogeneous field distribution, which causes a variation of the signal intensity.

Therefore it is required first to attenuate the low frequency trend of the background surface and to remove the surface offset. Thus, the deflection volume, where no artefact is present, will tend to be zero, while the volume where the artefact is present will tend to be negative: the more negative the indicator value is, the more pronounced is the artefact level.

To remove the low frequency trend and the offset, a preprocessing step is performed over each horizontal signal line of the surface image and then the 3D surface is reconstructed using the preprocessed signal lines. In a first step the horizontal signal line passes through a moving average (MA) smoother. Then the deflection waveform (if it is present) is removed from the signal line and an hermite interpolation is used to fill the removed signal segment. A Kalman smoother [11] is then used for the low frequency trend extraction, which is finally subtracted to the MA smoothed signal. In Figure 1C the obtained 3D surface image is shown after preprocessing of every signal line and surface reconstruction.

The resulting preprocessed 3D surface of Figure 1C is only the result for one MRI slice. The main algorithm processes every signal line of every slice for calculating a final distortion indicator. A summary of this algorithm is presented in the following:

  • For each slice the preprocessing algorithm described above is applied.

  • For each line of the preprocessed 3D surface image of each image slice, an indicator of artefact level is computed using a integration operation over the preprocessed signal line.

  • All the values of the indicator of every signal line and slice are stored in the same vector.

  • The elements of the vector are sorted in descending order.

Figure 2 shows an example of the distortion indicator per slice when the algorithm was applied to 13 slices of a needle MRI image, where it is possible to observe how the indicator changes according to the visibility of the artefact. At the right of this figure, the descending order sorted vector is shown, which correspond to the final distortion indicator. In this way the artefact assessment results in a monotonically decreasing curve (MDC) indicator, which goes from zero values representing signal lines, where no artefact is present, passing through middle values and arriving to lower negative values representing the maximal artefact levels assessed in different signal lines. These lower values are the ones which are significant for assessing the artefact created by the tested instrument.

Figure 2 Assesment of artifact caused by a biopsy needle using the distortion indicator in 13 slices and the resulting MDC indicator.
Figure 2

Assesment of artifact caused by a biopsy needle using the distortion indicator in 13 slices and the resulting MDC indicator.

3 Results

First of all in Figure 3 the MDC indicator is shown for every tested biopsy needle (named N1, …, N6 in the sequel). Since the MDC curve is the result of a descending sorted signal, only the last part of this indicator is significant (the maximal value of artefact assessment is in this section of the indicator). That is why only the last 150 values of the MDC signal are shown. Despite that, due to the nature of the indicator the MDC, it has no distance unit but it shows clearly the different artefact sizes produced by each needle.

Figure 3 Last 150 values of the MDC indicator obtained for each one of the six tested needles.
Figure 3

Last 150 values of the MDC indicator obtained for each one of the six tested needles.

For comparison between manual and automatic artefact assessment, the measured manual artefact were evaluated for each needle using the average and standard deviation of all the manual measurements performed per needle. This is shown at the left of Figure 4, where the average and standard deviation of the six measures per needle (2 per each participant) result in different artefacts levels. It is also possible to observe significant variability between observers with sometimes more than 1 mm of measurements differences between them. At the right of the same figure the automatic measurements are shown, where the displayed single values per needle were calculated as the average of the last 100 values of the obtained MDC for each needle. With the exception of the differences in artefact between needles N1 and N6 the curves have an acceptable correlation.

Figure 4 Average and standard deviation of the manual measurements versus the automatic artefact assessment performed from the MDC indicator (absolute values were taken for the right graph).
Figure 4

Average and standard deviation of the manual measurements versus the automatic artefact assessment performed from the MDC indicator (absolute values were taken for the right graph).

The variability between participants shown in the left of Figure 4 is not only caused by different interpretations on how to measure an artefact using the ASTM standard, but also because the observers selected different slices for performing their measurements. This is revealed by Table 2, where the selected slices for each needle by each participant are shown. It is evident that not always the same slice is selected by the participants and that moreover sometimes not the same slice is selected by the same observer in two different measurements of the same artefact. The last row of the table shows the slice where the automatic algorithm finds the largest artefact. It can be noticed that this maximal artefact measurement is in almost all cases located in the slice where there is a manual selection agreement of at least 50% of all the measurements performed in a needle.

Table 2

Selection of slices by the different participants and also by the automatic algorithm for each tested needle.

ParticipantMeas.N1N2N3N4N5N6
11111012323132
12111112313032
21111111323131
22111111313131
31111011303233
32111011303232
Automatic− 111011313132

4 Conclusion

The presented work had the objective of comparing artefact assessment performed by manual measurements with an automatic algorithm where the algorithms measurement principle is based on a different point of view than the proposed standards. Assessing the artefact through the computation of a 3D surface distortion indicator shows a correlation with the manually obtained results.

One question to answer in the future is if the 3D representation of the artefact can reveal more complex structures than the 2D-images can and if this can improve the accuracy in artefact assessment. Also, further work is needed to use the indicator for quantitative information about the artefact in terms of an accepted physical unity.

Author’s Statement

Research funding: This research was funded by the German BMBF (grant 03IPT7100X). Conflict of interest: Authors state no conflict of interest. Material and methods: Informed consent: Informed consent is not applicable. Ethical approval: The conducted research is not related to either human or animal use.

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Alfredo Illanes et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0095
Keywords for this article
artefact assessment; MRI; susceptibility
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  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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