Signal Artifacts Research Articles

Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation.

Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Adaptive Algorithm for Motion Artifacts Removal in Wearable Biomedical Sensors During Physical Exercise

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Motion artifacts (MA) are significant sources of noise in the wearable devices that are used to collect biological information from the human body. Photoplethysmography (PPG) is an example of the sensors embedded in many of these devices. PPG signals are highly influenced by MA, especially when the motion involves the organ that the PPG sensor is attached to. The aim of this paper is to build an adaptive algorithm and multiresolution analysis technique to denoise the raw PPG signals, suppress the MAs and accurately estimate the HR as an example of MA suppression in the wearable devices. The challenge here is to find the right selection considering high accuracy and low-computational complexity. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> Discrete wavelet transform recursive inverse (DWT-RI) adaptive filter algorithm in addition to multiresolution analysis is implemented to suppress MAs and estimate the HR using the simultaneously recorded accelerations in the three dimensions as reference signals for the adaptive algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The proposed algorithm has a low computational cost and results in an absolute average error of 1.17 beats per minute (BPM) when tested on 12 subjects from a well-known database for this purpose. The performance of the proposed algorithm is compared to the other existing methods in literature used for noise removal and motion artifact suppression. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> DWT-RI adaptive algorithm can successfully suppress motion artifacts in PPG signals with a low computational complexity and outperforms several alternatives in terms of the average absolute error.

Searching for intralocus sexual conflicts in the three-spined stickleback (Gasterosteus aculeatus) genome.

Differences between sexes in trait fitness optima can generate intralocus sexual conflicts that have the potential to maintain genetic diversity through balancing selection. However, these differences are unlikely to be associated with strong selective coefficients and are challenging to detect. Additionally, recent studies have highlighted that duplications on sexual chromosomes can create artifactual signals of intralocus sexual conflicts. Thus, testing the relationship between intralocus sexual conflicts and balancing selection requires stringent filtering of duplicated regions, and dedicated methods to detect loci with low levels of intersex differentiation. In this study, we investigated intralocus sexual conflicts in the three-spined stickleback using whole-genome sequencing (mean coverage = 12×) of 50 females and 49 males from an anadromous population in the St. Lawrence River, Québec, Canada. After stringent filtering of duplications from the sex chromosomes, we compared three methods to detect intralocus sexual conflicts. We found only two genomic regions under potential intralocus sexual conflict that also showed signals of balancing selection. Overall, our results suggest that most intralocus sexual conflicts do not drive long-term balancing selection and are most likely transient.

Isolating sensory artifacts in the suprathreshold TMS-EEG signal over DLPFC

Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) is an effective way to evaluate neurophysiological processes at the level of the cortex. To further characterize the TMS-evoked potential (TEP) generated with TMS-EEG, beyond the motor cortex, we aimed to distinguish between cortical reactivity to TMS versus non-specific somatosensory and auditory co-activations using both single-pulse and paired-pulse protocols at suprathreshold stimulation intensities over the left dorsolateral prefrontal cortex (DLPFC). Fifteen right-handed healthy participants received six blocks of stimulation including single and paired TMS delivered as active-masked (i.e., TMS-EEG with auditory masking and foam spacing), active-unmasked (TMS-EEG without auditory masking and foam spacing) and sham (sham TMS coil). We evaluated cortical excitability following single-pulse TMS, and cortical inhibition following a paired-pulse paradigm (long-interval cortical inhibition (LICI)). Repeated measure ANOVAs revealed significant differences in mean cortical evoked activity (CEA) of active-masked, active-unmasked, and sham conditions for both the single-pulse (F(1.76, 24.63) = 21.88, p < 0.001, η2 = 0.61) and LICI (F(1.68, 23.49) = 10.09, p < 0.001, η2 = 0.42) protocols. Furthermore, global mean field amplitude (GMFA) differed significantly across the three conditions for both single-pulse (F(1.85, 25.89) = 24.68, p < 0.001, η2 = 0.64) and LICI (F(1.8, 25.16) = 14.29, p < 0.001, η2 = 0.5). Finally, only active LICI protocols but not sham stimulation ([active-masked (0.78 ± 0.16, P < 0.0001)], [active-unmasked (0.83 ± 0.25, P < 0.01)]) resulted in significant signal inhibition. While previous findings of a significant somatosensory and auditory contribution to the evoked EEG signal are replicated by our study, an artifact attenuated cortical reactivity can reliably be measured in the TMS-EEG signal with suprathreshold stimulation of DLPFC. Artifact attenuation can be accomplished using standard procedures, and even when masked, the level of cortical reactivity is still far above what is produced by sham stimulation. Our study illustrates that TMS-EEG of DLPFC remains a valid investigational tool.

Pallidal Recordings in Chronically Implanted Dystonic Patients: Mitigation of Tremor-Related Artifacts.

Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the PerceptTM PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.

Deep Learning-Based ECG Arrhythmia Classification: A Systematic Review

Deep learning (DL) has been introduced in automatic heart-abnormality classification using ECG signals, while its application in practical medical procedures is limited. A systematic review is performed from perspectives of the ECG database, preprocessing, DL methodology, evaluation paradigm, performance metric, and code availability to identify research trends, challenges, and opportunities for DL-based ECG arrhythmia classification. Specifically, 368 studies meeting the eligibility criteria are included. A total of 223 (61%) studies use MIT-BIH Arrhythmia Database to design DL models. A total of 138 (38%) studies considered removing noise or artifacts in ECG signals, and 102 (28%) studies performed data augmentation to extend the minority arrhythmia categories. Convolutional neural networks are the dominant models (58.7%, 216) used in the reviewed studies while growing studies have integrated multiple DL structures in recent years. A total of 319 (86.7%) and 38 (10.3%) studies explicitly mention their evaluation paradigms, i.e., intra- and inter-patient paradigms, respectively, where notable performance degradation is observed in the inter-patient paradigm. Compared to the overall accuracy, the average F1 score, sensitivity, and precision are significantly lower in the selected studies. To implement the DL-based ECG classification in real clinical scenarios, leveraging diverse ECG databases, designing advanced denoising and data augmentation techniques, integrating novel DL models, and deeper investigation in the inter-patient paradigm could be future research opportunities.

Appearance and modeling of bubble artifacts in intracranial magnetic resonance-guided laser interstitial thermal therapy (MRg-LITT) temperature images

PurposeTo understand if unexplained signal artifacts in MRg-LITT proton resonance frequency- (PRF-) shift thermometry images are caused by air bubbles or hemorrhages, and to characterize their effects on temperature measurements. MethodsRetrospective image data from an IRB-approved clinical trial of intracranial MRg-LITT were inspected for asymmetric distortions observed in phase data during ablations, which have been previously reported as likely hemorrhages. A total of eight patient cases were selected: seven with artifact occurrence and one without. Mathematical image models for air bubbles or hemorrhages were implemented to estimate the size of the air bubble or hemorrhage needed to explain the clinically observed phase artifacts. Correlations and Bland-Altman analyses were used to determine if an air bubble model or a hemorrhage model was better correlated to the clinical data. The model was used to inject bubbles into clean PRF phase data without artifacts to examine how temperature profile distortions change with slice orientation. The simulated air-bubble injected data were compared to clinical data containing artifacts to examine the bubbles' effects on temperature and thermal damage estimates. ResultsThe model demonstrated that air bubbles up to approximately 1 cm in diameter could explain the clinically observed phase artifacts. The bubble model predicts that a hemorrhage would have to be 2.2 times as large as an air bubble in order to explain the same extent of phase distortion observed in clinical data. Air bubbles had 16% percent higher correlations to the clinical PRF phase data than hemorrhages, even after rescaling the hemorrhage phases to better match the data. The air bubble model also explains how the phase artifacts lead to both large positive and large negative temperature errors, up to ±100 °C, which could cascade to damage estimate errors of several millimeters. ConclusionResults showed that the artifacts are likely caused by air bubbles rather than hemorrhages, which may be introduced before heating or appear during heating. Manufacturers and users of devices that rely upon PRF-shift thermometry should be aware these phase distortions from bubble artifacts can result in large temperature errors.

Multi-window spectrum analysis of Búzios oil field PSDM seismic data applied to frequency attenuation study

The intrinsic attenuation is often quantified using the inverse quality factor Q. Methods to estimate Q are mainly based on time domain or frequency domain and sensitive to amplitude and/or frequency band variations. In this sense, noise, signal artifacts or processing steps can alter the amplitude, frequency content and the waveform, what may lead to inaccurate calculations of Q. A way to better understand the frequency band variation in seismic data and verify patterns or problems occurring in the signal frequency band is performing layered or window-by-window spectrum analysis. This work describes a multi-window spectrum analysis method used to better understand the frequency attenuation process as the seismic wave travels through the sediments from the sea bottom down to the reservoir depths in a 3D Kirchhoff Pre-Stack Depth Migrated (PSDM) seismic data from Búzios oil field. To perform the spectral analysis in frequency domain, the PSDM volume was converted to time domain using the same interval velocity volume used to depth-migrate the data. The variation of frequency band as the signal propagates through the sediment package was analyzed dividing the studied crossline sections in 13 layers of 200 ms each, from sea bottom down to the base of pre-salt reservoir formations. The results suggest that most part of higher frequency loss has been occurred in post-salt package. This may be an indication of significant viscoelastic behavior or strong stratigraphic filtering of this interval. Sequentially, higher frequency content is lost slowly from salt top down to the last layer located inside pre-salt formation. Very slight frequency spectrum variation below salt top indicates elastic behavior of the salt formation and underling pre-salt formation in this interval. The spectrum analysis method proposed here has shown to be useful and efficient to identify intervals of different attenuation patterns in seismic data. Additionally, the frequency band study produced by our spectrum analysis can be used to quality-control Q-factor estimation computed by different methods.

Hitting the Target of Image-Guided Psychiatry?

Hitting the Target of Image-Guided Psychiatry?

Monaural cardiopulmonary sound separation via complex-valued deep autoencoder and cyclostationarity

Objective. Cardiopulmonary auscultation is promising to get smart due to the emerging of electronic stethoscopes. Cardiac and lung sounds often appear mixed at both time and frequency domain, hence deteriorating the auscultation quality and the further diagnosis performance. The conventional cardiopulmonary sound separation methods may be challenged by the diversity in cardiac/lung sounds. In this study, the data-driven feature learning advantage of deep autoencoder and the common quasi-cyclostationarity characteristic are exploited for monaural separation. Approach. Different from most of the existing separation methods that only handle the amplitude of short-time Fourier transform (STFT) spectrum, a complex-valued U-net (CUnet) with deep autoencoder structure, is built to fully exploit both the amplitude and phase information. As a common characteristic of cardiopulmonary sounds, quasi-cyclostationarity of cardiac sound is involved in the loss function for training. Main results. In experiments to separate cardiac/lung sounds for heart valve disorder auscultation, the averaged achieved signal distortion ratio (SDR), signal interference ratio (SIR), and signal artifact ratio (SAR) in cardiac sounds are 7.84 dB, 21.72 dB, and 8.06 dB, respectively. The detection accuracy of aortic stenosis can be raised from 92.21% to 97.90%. Significance. The proposed method can promote the cardiopulmonary sound separation performance, and may improve the detection accuracy for cardiopulmonary diseases.

From the Editors

From the Editors

Controlling jaw-related motion artifacts in functional near-infrared spectroscopy

BackgroundFunctional near-infrared spectroscopy (fNIRS) as a non-invasive optical neuroimaging technique has demonstrated great potential in monitoring cerebral activity. Due to its portability and compatibility with medical implants, fNIRS has seen increasing applications in studying the hearing, language and cognitive functions. However, fNIRS is susceptible to artifacts related to jaw movements, such as teeth clenching, swallowing and speaking, which affect recordings over the temporal, parietal and frontal/prefrontal cortices. New methodWe investigated two new approaches to control the jaw-related motion artifacts, an individually customized bite bar apparatus and a denoising algorithm namely PCA-GLM based on multi-channel fNIRS recordings from long-separation and short-separation montage. We first recorded data while subjects performed a clenching task, then an auditory task and a resting-state task with and without the bite bar. ResultsOur results have shown that jaw clenching can introduce spurious, task-evoked-like responses in fNIRS signals. A bite bar customized for each participant effectively suppressed the movement-related activities in fNIRS, at both task and resting-state conditions. Moreover, the bite bar and the PCA-GLM denoising method are shown to improve auditory responses, by significantly reducing the within-subject standard deviation, increasing the task-related contrast-to-noise ratio, and yielding stronger activations to the auditory stimuli. Comparison with existing method(s)The current study has demonstrated a novel method to control the jaw-related motion artifacts in fNIRS signals. ConclusionsOur method will benefit the study of the hearing, language and cognitive functions in normal healthy subjects and patients.

Comparison and Integration of Voltage and Charge Amplifiers for Capacitive ECG Measurements.

Sensing with capacitive electrodes is of interest for long-term, comfortable bio-potential measurements (e.g., ECG). However, due to the small body-to-electrode capacitance (Ce), the design of the associated front-end amplifier remains a challenge. Both voltage amplifiers (VA) and charge amplifiers (CA) can be employed. While basic comparisons of both typologies were done before, this paper extends the comparison to their responses to artifacts (caused by motion or interference). Further, a VA-CA-switchable amplifier is proposed, allowing to adapt the amplifier type to different situations, and enabling to estimate the body-to-electrode capacitance Ce in a passive way. A VA-CA switchable amplifier was implemented in a 180 nm CMOS process. The responses to artifacts for VA and CA were studied by modelling, simulations and experiments using the custom IC. The proposed Ce estimation method was validated by electrical tests and in-vivo tests. VAs are less affected by Ce variation artifacts, while CAs recover faster from triboelectricity artifacts. In a VA, these two artifacts are multiplicative and get modulated if they occur simultaneously, but in a CA they remain independent. The combined VA-CA amplifier has the potential for optimal amplifier selection according to the properties of the recorded signal, the value of Ce and the actual presence of artifacts. Moreover, it can estimate Ce without extra hardware. The proposed VA-CA switchable structure is superior to an individual VA or CA, thanks its adaptability to signal quality and artifacts, and it provides extra information on the body-to-electrode interface quality (Ce).

Circulant Singular Spectrum Analysis and Discrete Wavelet Transform for Automated Removal of EOG Artifacts from EEG Signals.

Background: Portable electroencephalogram (EEG) systems are often used in health care applications to record brain signals because their ease of use. An electrooculogram (EOG) is a common, low frequency, high amplitude artifact of the eye blink signal that might confuse disease diagnosis. As a result, artifact removal approaches in single EEG portable devices are in high demand. Materials: Dataset 2a from the BCI Competition IV was employed. It contains the EEG data from nine subjects. To determine the EOG effect, each session starts with 5 min of EEG data. This recording lasted for two minutes with the eyes open, one minute with the eyes closed, and one minute with eye movements. Methodology: This article presents the automated removal of EOG artifacts from EEG signals. Circulant Singular Spectrum Analysis (CiSSA) was used to decompose the EOG contaminated EEG signals into intrinsic mode functions (IMFs). Next, we identified the artifact signal components using kurtosis and energy values and removed them using 4-level discrete wavelet transform (DWT). Results: The proposed approach was evaluated on synthetic and real EEG data and found to be effective in eliminating EOG artifacts while maintaining low frequency EEG information. CiSSA-DWT achieved the best signal to artifact ratio (SAR), mean absolute error (MAE), relative root mean square error (RRMSE), and correlation coefficient (CC) of 1.4525, 0.0801, 18.274, and 0.9883, respectively. Comparison: The developed technique outperforms existing artifact suppression techniques according to performance measures. Conclusions: This advancement is important for brain science and can contribute as an initial pre-processing step for research related to EEG signals.

Undiscovered Spin Trapping Artifacts in Persulfate Oxidation Processes: Implications for Identification of Hydroxyl or Sulfate Radicals in Water

Electron paramagnetic resonance (EPR) coupled with the spin trapping technique is widely used to identify radicals and has become a tool that is being heavily relied on to probe the reaction mechanism in advanced oxidation processes (AOPs). However, the unexpected spin trapping artifacts in a highly oxidizing environment may mislead the mechanistic interpretations of AOPs. Here, we report previously unrevealed reactions between peroxydisulfate (PDS) and 5,5-dimethyl-1-pyrroline N-oxide (DMPO) or N-tert-butyl-α-phenylnitrone (PBN). The mainly detectable adducts are attributed to DMPO-OH and PBN-OH. Through radical quenching experiments, the nonradical generation pathways of DMPO-OH and PBN-OH are validated, and the 17O incorporation experiment demonstrates that H2O plays a critical role in DMPO-OH production. The formation routes of the primary artifact signals (DMPO-OH, DMPO-SO4, PBN-OH, and PBN-SO4) are further proposed by applying diverse solvents. PDS may directly oxidize DMPO to generate DMPO-SO4 in acetonitrile or acetone solvent, while DMPO-OH is yielded using H2O as the solvent. The generation of PBN-OH or PBN-SO4 follows the inverted spin trapping mechanism (a combination of one-electron oxidation and nucleophilic addition). These findings manifest the necessity of selecting the proper spin traps and performing careful control tests during exploration of the catalytic oxidation mechanism under highly oxidizing conditions.

An Adaptive Spatial Filtering Method for Multi-channel EMG Artifact Removal during Functional Electrical Stimulation with Time-variant Parameters.

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts.

Identifying and Removing Interference and Artifacts in Multifractal Signals With Application to EEG Signals

Identifying and Removing Interference and Artifacts in Multifractal Signals With Application to EEG Signals

Multimodal single-cell and whole-genome sequencing of small, frozen clinical specimens.

Single-cell genomics enables dissection of tumor heterogeneity and molecular underpinnings of drug response at an unprecedented resolution1-11. However, broad clinical application of these methods remains challenging, due to several practical and preanalytical challenges that are incompatible with typical clinical care workflows, namely the need for relatively large, fresh tissue inputs. In the present study, we show that multimodal, single-nucleus (sn)RNA/T cell receptor (TCR) sequencing, spatial transcriptomics and whole-genome sequencing (WGS) are feasible from small, frozen tissues that approximate routinely collected clinical specimens (for example, core needle biopsies). Compared with data from sample-matched fresh tissue, we find a similar quality in the biological outputs of snRNA/TCR-seq data, while reducing artifactual signals and compositional biases introduced by fresh tissue processing. Profiling sequentially collected melanoma samples from a patient treated in the KEYNOTE-001 trial12, we resolved cellular, genomic, spatial and clonotype dynamics that represent molecular patterns of heterogeneous intralesional evolution during anti-programmed cell death protein 1 therapy. To demonstrate applicability to banked biospecimens of rare diseases13, we generated a single-cell atlas of uveal melanoma liver metastasis with matched WGS data. These results show that single-cell genomics from archival, clinical specimens is feasible and provides a framework for translating these methods more broadly to the clinical arena.

DNA Modification Patterns Filtering and Analysis Using DNAModAnnot.

Mapping DNA modifications at the base resolution is now possible at the genome level thanks to advances in sequencing technologies. Long-read sequencing data can be used to identify modified base patterns. However, the downstream analysis of Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data requires the integration of genomic annotation and comprehensive filtering to prevent the accumulation of artifact signals. We present in this chapter, a linear workflow to fully analyze modified base patterns using the DNA Modification Annotation (DNAModAnnot) package. This workflow includes a thorough filtering based on sequencing quality and false discovery rate estimation and provides tools for a global analysis of DNA modifications. Here, we provide an applicationexample of this workflow with PacBio data and guide the user by explaining expected outputs via a fully integrated Rmarkdown script. This protocol is presented with tips showing how to adapt the provided code for annotating epigenomes of any organism according to the user needs.

Pitfalls of Using T2-contrast Enhancement Techniques in 3D-FLAIR to Detect Endolymphatic Hydrops.

To determine whether T2-contrast enhancement techniques can be used to diagnose endolymphatic hydrops, we compared fluid signal artifacts with and without T2-contrast enhancement techniques in 3D fluid-attenuated inversion recovery (3D-FLAIR). We prepared a custom-made phantom consisting of eight tubes half-filled with saline. Images were obtained using four 3D-FLAIR: without T2-contrast enhancement (Normal), with non-selective T2-inversion recovery (T2-IR), and two with non-selective T2 preparation IR (T2-prep). Scans were performed with and without rice covering the phantom to simulate minimal and severe B0-inhomogeneity conditions. The average signal intensity (SI) values of eight saline tubes were compared between the four sequences and between each other. Comparisons were performed for all measurement slices and the central 10 slices. The images using T2-contrast enhancement technique were obtained from a volunteer and a patient suspected of Meniere's disease. The Normal sequence SI for all slices was significantly lower than that for the other sequences, with smaller standard deviation (SD) and no outliers. Several outliers were detected in the other sequences. The SDs and outliers were larger without rice than with rice. When the central 10 slices with rice, the T2-IR had a significantly higher SI with more outliers compared with the Normal sequence. The T2-prep had no outliers and SIs that were comparable to those of the Normal sequence. However, without rice, the T2-IR and T2-prep sequences had significantly higher SIs with outliers and larger SDs compared to the Normal sequence. In the corresponding images, the Normal sequence achieved excellent fluid suppression, whereas the T2-IR and T2-prep sequences showed high-signal artifacts. Imperfect fluid suppressions were observed in the volunteer image and the endolymphatic hydrops on the post-gadolinium image differed in size and shape in the non-injected T2-IR in the patient image. T2-contrast enhancement techniques should be used with caution in 3D-FLAIR for diagnosing endolymphatic hydrops.