Signal Artifacts Research Articles

A New Fast Approach for an EEG-based Motor Imagery BCI Classification

Nowadays, Brain Computer Interface has an important role in the life quality of paralyzed people. However, this technique is mainly affected by the quality of the recorded signal in each trial. This problem could be solved by rejecting low-quality trials. But developing the processing based on the recorded signal from the brain, which is a mixture of the target signal plus noise and artifact, would not be favourable in situations that all trials have low quality. This paper solves this problem by presenting a new fast algorithm for separating recorded source signals. Results indicate the improvement in classification accuracy of the proposed method compared with the well-known state of the art works.

FASTQINS and ANUBIS: two bioinformatic tools to explore facts and artifacts in transposon sequencing and essentiality studies.

Transposon sequencing is commonly applied for identifying the minimal set of genes required for cellular life; a major challenge in fields such as evolutionary or synthetic biology. However, the scientific community has no standards at the level of processing, treatment, curation and analysis of this kind data. In addition, we lack knowledge about artifactual signals and the requirements a dataset has to satisfy to allow accurate prediction. Here, we have developed FASTQINS, a pipeline for the detection of transposon insertions, and ANUBIS, a library of functions to evaluate and correct deviating factors known and uncharacterized until now. ANUBIS implements previously defined essentiality estimate models in addition to new approaches with advantages like not requiring a training set of genes to predict general essentiality. To highlight the applicability of these tools, and provide a set of recommendations on how to analyze transposon sequencing data, we performed a comprehensive study on artifacts corrections and essentiality estimation at a 1.5-bp resolution, in the genome-reduced bacterium Mycoplasma pneumoniae. We envision FASTQINS and ANUBIS to aid in the analysis of Tn-seq procedures and lead to the development of accurate genome essentiality estimates to guide applications such as designing live vaccines or growth optimization.

Illumination variation-resistant video-based heart rate monitoring using LAB color space

The remote photoplethysmography technology based on consumer cameras has been demonstrated to be an effective method for heart rate monitoring. However, artificial signals caused by the ambient illumination variation and facial motion would severely distort the heart rate pulse signal and affect the measurement accuracy of the heart rate. In view of these issues, the conversion from RGB color space to LAB color space is performed to separate the luminance signal, and the smoothness prior approach is employed to remove the stationary artifacts in the raw signals of A channel and B channel. On this basis, the simple combined signal, the difference between A channel and B channel, is introduced to extract the heart rate pulse signal. Finally, the pure signal is decomposed to obtain a set of intrinsic mode functions using the ensemble empirical mode decomposition algorithm, and heart rate is estimated based on the one intrinsic mode function with the highest energy and peak ratio in the range of 0.7 Hz to 3 Hz. To assess the performance of the framework proposed in this paper, experiments in different scenarios are performed and the experimental results show that the method proposed in this paper can effectively estimate heart rate, where the mean absolute bias is 2.59 beats/min (bpm) and the 95% confidence interval is from -7.14 bpm to 3.40 bpm.

Implementation of Neural Network with ALE for the Removal of Artifacts in EEG Signals

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.

A deep learning method for real-time intraoperative US image segmentation in prostate brachytherapy.

This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoperative TRUS images is clinically important to have faster and reproducible interventions that can benefit both the clinical workflow and patient health. We present a multi-task deep learning method for an automatic prostate CTV boundary detection in intraoperative TRUS images by leveraging both the low-level and high-level (prior shape) information. Our method includes a channel-wise feature calibration strategy for low-level feature extraction and learning-based prior knowledge modeling for prostate CTV shape reconstruction. It employs CTV shape reconstruction from automatically sampled boundary surface coordinates (pseudo-landmarks) to detect the low-contrast and noisy regions across the prostate boundary, while being less biased from shadowing, inherent speckles, and artifact signals from the needle and implanted radioactive seeds. The proposed method was evaluated on a clinical database of 145 patients who underwent permanent prostate brachytherapy under TRUS guidance. Our method achieved a mean accuracy of [Formula: see text] and a mean surface distance error of [Formula: see text]. Extensive ablation and comparison studies show that our method outperformed previous deep learning-based methods by more than 7% for the Dice similarity coefficient and 6.9 mm reduced 3D Hausdorff distance error. Our study demonstrates the potential of shape model-based deep learning methods for an efficient and accurate CTV segmentation in an ultrasound-guided intervention. Moreover, learning both low-level features and prior shape knowledge with channel-wise feature calibration can significantly improve the performance of deep learning methods in medical image segmentation.

B0 field homogeneity recommendations, specifications, and measurement units for MRI in radiation therapy.

The purpose is: (a) Relate magnetic resonance imaging (MRI) quality recommendations for radiation therapy (RT) to B0 field homogeneity; (b) Evaluate manufacturer specifications of B0 homogeneity for 34 commercial whole-body MRI systems based on the MRI quality recommendations and RT application; (c) Measure field homogeneity in five commercial MRI systems and one commercial MRI-Linac used in RT and compare the results with their B0 homogeneity specifications. Magnetic resonance imaging quality recommendations for spatial integrity, image blurring, fat saturation, and null banding in RT were developed based on the literature. Guaranteed (maximum) and typical B0 field homogeneity specifications for various diameter spherical volumes (DSVs) were provided by GE, Philips, Siemens, and Canon. For each system, the DSV that conforms to each MRI quality recommendation and anatomical RT application was estimated based on the manufacturer specifications. B0 field homogeneity was measured on six MRI systems including Philips (1.5T), Siemens (1.5 and 3T), and ViewRay MRI (0.35T) systems using 24 and 35cm DSV spherical phantoms. Two measurement techniques were used: (a) MRI using phase contrast field mapping to measure peak-to-peak (pk-pk), volume root mean square (VRMS), and standard deviation (SD); and (b) Magnetic resonance (MR) spectroscopy by acquiring a volumetric free induction decay (FID) to measure full width at half maximum (FWHM). The measurements were used to assess: (a) conformance with the manufacturer specifications; and (b) the relationship between the various field homogeneity measurement units. Measurements were made with and without gradient shimming (gradshim) or second-order active shimming. Multiple comparisons, analysis of variance (ANOVA), and Pearson correlations were performed to assess the dependence of pk-pk, VRMS, SD, and FWHM measurements of field homogeneity on shim volume, level of shim, and MRI system. For a 40cm DSV, the B0 homogeneity specifications ranged from 0.35 to 5ppm (median=0.75ppm) VRMS for 1.5T systems and 0.2 to 1.4ppm (median=0.5ppm) VRMS for 3T systems. The usable DSVs ranged from 16 to 49cm (median=35cm) based on the image quality recommendations and the manufacturer specifications. There was general compliance between the six measured field homogeneities and manufacturer specifications although signal dephasing was observed in two systems at<35cm DSV. The relationships between pk-pk, VRMS, SD, and FWHM varied based on MRI system, shim volume, and quality of shim. However, VRMS and SD measurements were highly correlated. The delineation of the diseased lesion from organs at risk is the main priority for RT. Therefore, field homogeneity performance for RT must minimize image blurring and image artifacts (null bands and signal dephasing) while optimizing spatial integrity and fat saturation. Based on the specifications and recommendations for field homogeneity, some MRI systems are not well suited to meet the strict demands of RT particularly for the large imaging volumes used in body MRI. VRMS and SD measurements of B0 field homogeneity tend to be more stable and sensitive to field inhomogeneities in RT applications than pk-pk and FWHM.

Robust De-Noising Technique for Accurate Heart Rate Estimation Using Wrist-Type PPG Signals

Heart rate (HR) estimated using the photoplethysmography (PPG) signals during intense physical exercise is highly onerous owing to the existence of noise components like motion artifacts (MA’s) in the PPG signal. In this work, a robust de-noising technique for accurate HR estimation from the corrupted PPG signal is reported. The de-noising technique reported employs three pairs of Recursive Least Squares (RLS) as well as Normalized Least Mean Squares (NLMS) adaptive filters. The de-noised PPG signal obtained at the output of RLS and NLMS adaptive filters are combined using the sigmoid function. The three MA reduced PPG signals obtained from each adaptive filter pair are again combined using softmax activation function to form a MA reduced PPG signal from which the HR is estimated. Fast Fourier Transform (FFT) is used to estimate the HR and phase vocoder is used to refine the estimated HR. The proposed method is tested on the publically available 23 PPG datasets and it resulted in an HR estimation error of 1.89 beat per minute (BPM). The HR estimated using the proposed technique is found to be accurate and the HR estimation error is 1.89 BPM which is less when compared to other existing HR estimation mechanisms that reported an error of 1.97 BPM and 2.09 BPM.

Performance Evaluation of Fixed Sample Entropy for Lung Sound Intensity Estimation.

Lung sound (LS) signals are often contaminated by impulsive artifacts that complicate the estimation of lung sound intensity (LSI) using conventional amplitude estimators. Fixed sample entropy (fSampEn) has proven to be robust to cardiac artifacts in myographic respiratory signals. Similarly, fSampEn is expected to be robust to artifacts in LS signals, thus providing accurate LSI estimates. However, the choice of fSampEn parameters depends on the application and fSampEn has not previously been applied to LS signals. This study aimed to perform an evaluation of the performance of the most relevant fSampEn parameters on LS signals, and to propose optimal fSampEn parameters for LSI estimation. Different combinations of fSampEn parameters were analyzed in LS signals recorded in a heterogeneous population of healthy subjects and chronic obstructive pulmonary disease patients during loaded breathing. The performance of fSampEn was assessed by means of its cross-covariance with flow signals, and optimal fSampEn parameters for LSI estimation were proposed.

Modeling and Reconstructing Textile Sensor Noise: Implications for Wearable Technology.

Wearable sensors enable the simultaneous recording of several electrophysiological signals from the human body in a non-invasive and continuous manner. Textile sensors are garnering substantial interest in the wearable technology because they can be knitted directly into the daily-used objects like underwear, bra, dress, etc. However, accurate processing of signals recorded by textile sensors is extremely challenging due to the very low signal-to-noise ratio (SNR). Systematic classification of textile sensor noise (TSN) is necessary to: (i) identify different types of noise and their statistical characteristics, (ii) explore how each type of noise influences the electrophysiological signal, (iii) develop optimal textile-specific electronics that suppress TSN, and (iv) reproduce TSN and create large dataset of textile sensors to validate various machine learning and signal processing algorithms. In this paper, we develop a novel technique to classify textile sensor artifacts in ECG signals. By simultaneously recording signals from the waist (textile sensors) and chest (gel electrode), we extract TSN by removing the chest ECG signal from the recorded textile data. We classify TSN based on its morphological and statistical features in two main categories, namely, slow and fast. Linear prediction coding (LPC) is utilized to model each class of TSN by auto-regression coefficients and residues. The residual signal can be approximated by Gaussian distribution which enables reproducing slow and fast artifacts that not only preserve the similar morphological features but also fulfill the statistical properties of TSN. By reproducing TSN and adding them to clean ECG signals, we create a textile-like ECG signal which can be used to develop and validate different signal processing algorithms.

Artefacts Removal from ECG Signal: Dragonfly Optimization-based Learning Algorithm for Neural Network-enhanced Adaptive Filtering

Electrocardiogram (ECG) artefact removal is the major research topic as the pure ECG signals are an essential part of diagnosing heart-related problems. ECG signals are highly prominent to the interaction with the other signals like the Electromyography (EMG), Electroencephalography (EEG), and Electrooculography (EOG) signals and the interference mainly occurs at the time of recording. The removal of the artefacts from the ECG signal is a hectic challenge, for which, a novel algorithm is proposed in this work. The proposed method utilizes the adaptive filter termed as the (Dragonfly optimization + Levenberg Marqueret learning algorithm) DLM-based Nonlinear Autoregressive with eXogenous input (NARX) neural network for the removal of the artefacts from the ECG signals. Once the artefact signal is identified using the adaptive filter, the identified signal is subtracted from the primary signal that is composed of the ECG signal and the artefacts through an adaptive subtraction procedure. The clean signal thus obtained is used for effective diagnosis purposes, and the experimentation performed to prove the effectiveness of the proposed method proves that the proposed method obtained a maximum Signal-to-noise ratio (SNR) of 52.8789 dB, a minimum error of 0.1832, and minimum error of 0.428.

Introduction to the Special Issue on Intelligence on Scalable computing for Recent Applications

Introduction to the Special Issue on Intelligence on Scalable computing for Recent Applications

Data Fusion of Multiview Ultrasonic Imaging for Characterization of Large Defects.

The multiview total focusing method (TFM) enables a region of interest within a specimen to be imaged using different ray paths and wave-mode combinations. For defects larger than the ultrasonic wavelength, different portions of the same defect may manifest in a number of views. For a crack, the tip diffraction response may be evident in certain views and the specular reflection in others. Accurate characterization of large defects requires the information in multiple views to be combined. In this work, three data fusion methodologies are presented: a simple sum over all views, a sum weighted according to the inverse of the noise in each view, and a matched filter approach. Four large defects are examined; one stress corrosion crack (SCC), two weld cracks, and a pair of slagline defects in a weld. The matched filter (matched to a small circular void) provided significant improvement over the best individual view. The data fusion process incorporates artifact removal, where nondefect artifact signals within each image view are identified and masked, using a single defect-free data set for training. The matched filter was able to accurately visualize the full 3-D extent of the four defects, allowing characterization via the decibel drop method. When compared to X-ray computed tomography and micrograph data in the case of the SCC, the matched filter fusion provided excellent agreement. Its performance was also superior to any individual view while providing a single fused image that is easier for an operator to interpret than a set of multiview images.

VolcanoFinder: Genomic scans for adaptive introgression.

Recent research shows that introgression between closely-related species is an important source of adaptive alleles for a wide range of taxa. Typically, detection of adaptive introgression from genomic data relies on comparative analyses that require sequence data from both the recipient and the donor species. However, in many cases, the donor is unknown or the data is not currently available. Here, we introduce a genome-scan method-VolcanoFinder-to detect recent events of adaptive introgression using polymorphism data from the recipient species only. VolcanoFinder detects adaptive introgression sweeps from the pattern of excess intermediate-frequency polymorphism they produce in the flanking region of the genome, a pattern which appears as a volcano-shape in pairwise genetic diversity. Using coalescent theory, we derive analytical predictions for these patterns. Based on these results, we develop a composite-likelihood test to detect signatures of adaptive introgression relative to the genomic background. Simulation results show that VolcanoFinder has high statistical power to detect these signatures, even for older sweeps and for soft sweeps initiated by multiple migrant haplotypes. Finally, we implement VolcanoFinder to detect archaic introgression in European and sub-Saharan African human populations, and uncovered interesting candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder.

Multicenter intracranial EEG dataset for classification of graphoelements and artifactual signals

EEG signal processing is a fundamental method for neurophysiology research and clinical neurology practice. Historically the classification of EEG into physiological, pathological, or artifacts has been performed by expert visual review of the recordings. However, the size of EEG data recordings is rapidly increasing with a trend for higher channel counts, greater sampling frequency, and longer recording duration and complete reliance on visual data review is not sustainable. In this study, we publicly share annotated intracranial EEG data clips from two institutions: Mayo Clinic, MN, USA and St. Anne’s University Hospital Brno, Czech Republic. The dataset contains intracranial EEG that are labeled into three groups: physiological activity, pathological/epileptic activity, and artifactual signals. The dataset published here should support and facilitate training of generalized machine learning and digital signal processing methods for intracranial EEG and promote research reproducibility. Along with the data, we also propose a statistical method that is recommended for comparison of candidate classifier performance utilizing out-of-institution/out-of-patient testing.

Combined Denoising and Suppression of Transient Artifacts in Arterial Spin Labeling MRI Using Deep Learning.

Arterial spin labeling (ASL) is a useful tool for measuring cerebral blood flow (CBF). However, due to the low signal-to-noise ratio (SNR) of the technique, multiple repetitions are required, which results in prolonged scan times and increased susceptibility to artifacts. To develop a deep-learning-based algorithm for simultaneous denoising and suppression of transient artifacts in ASL images. Retrospective. 131 pediatric neuro-oncology patients for model training and 11 healthy adult subjects for model evaluation. 3T / pseudo-continuous and pulsed ASL with 3D gradient-and-spin-echo readout. A denoising autoencoder (DAE) model was designed with stacked encoding/decoding convolutional layers. Reference standard images were generated by averaging 10 pairwise ASL subtraction images. The model was trained to produce perfusion images of a similar quality using a single subtraction image. Performance was compared against Gaussian and non-local means (NLM) filters. Evaluation metrics included SNR, peak SNR (PSNR), and structural similarity index (SSIM) of the CBF images, compared to the reference standard. One-way analysis of variance (ANOVA) tests for group comparisons. The DAE model was the only model to produce a significant increase in SNR compared to the raw images (P < 0.05), providing an average SNR gain of 62%. The DAE model was also effective at suppressing transient artifacts, and was the only model to show a significant improvement in accuracy in the generated CBF images, as assessed using PSNR values (P < 0.05). In addition, using data from multiple inflow time acquisitions, the DAE images produced the best fit to the Buxton kinetic model, offering a 75% reduction in the fitting error compared to the raw images. Deep-learning-based algorithms provide superior accuracy when denoising ASL images, due to their ability to simultaneously increase SNR and suppress artifactual signals in raw ASL images. 3 TECHNICAL EFFICACY STAGE: 1.

A Critical, Event-Related Appraisal of Denoising in Resting-State fMRI Studies.

This article advances two parallel lines of argument about resting-state functional magnetic resonance imaging (fMRI) signals, one empirical and one conceptual. The empirical line creates a four-part organization of the text: (1) head motion and respiration commonly cause distinct, major, unwanted influences (artifacts) in fMRI signals; (2) head motion and respiratory changes are, confoundingly, both related to psychological and clinical and biological variables of interest; (3) many fMRI denoising strategies fail to identify and remove one or the other kind of artifact; and (4) unremoved artifact, due to correlations of artifacts with variables of interest, renders studies susceptible to identifying variance of noninterest as variance of interest. Arising from these empirical observations is a conceptual argument: that an event-related approach to task-free scans, targeting common behaviors during scanning, enables fundamental distinctions among the kinds of signals present in the data, information which is vital to understanding the effects of denoising procedures. This event-related perspective permits statements like "Event X is associated with signals A, B, and C, each with particular spatial, temporal, and signal decay properties". Denoising approaches can then be tailored, via performance in known events, to permit or suppress certain kinds of signals based on their desirability.

Abstract B31: A preprocessing filter to improve superparamagnetic relaxometry (SPMR)-based early tumor detection

Abstract The early detection of aggressive forms of ovarian cancer before they metastasize is critical for reducing overall mortality from the disease. Superparamagnetic relaxometry (SPMR) is an imaging technique useful for visualizing early-stage tumors with high sensitivity and specificity. It uses superconducting quantum interference devices (SQUIDs) to detect targeted superparamagnetic iron oxide nanoparticles (SPIONs) that visualize tumors ten times smaller than what conventional imaging techniques can. However, the ultra-sensitivity of SQUIDs increases their risk of distortion due to far-field artifacts. Therefore, a preprocessing filter was developed to mitigate far-field, low-frequency disturbances to SQUID signal acquisition. This is based on the hypothesis that correcting SQUID signal acquisition using a magnetometer for far-field detection will increase the accuracy of SPMR for early tumor detection. The hypothesis was tested in three steps. First, it was shown that the magnetometer (MAG) could specifically detect far-field noise and effectively avoid nanoparticle signatures. Second, high- and low-frequency noise was induced to show that far-field artifacts in the MAG signal correlated with distortions in the SQUID channels. Therefore, a processing filter was developed to parse through and parameterize MAG signal extrema to SQUID signal distortions. A series of further optimization steps included anchoring the MAG signal to respective channels, modeling and subtracting the component of structural (environmental) relaxation, and constraining a general subtraction window. Third, success was measured by the image reconstruction accuracy of sources with various nanoparticle concentrations, using the HSPMR dipole-fitting technique. Overall, the MAG-filter increased reconstruction accuracy more effectively with decreasing nanoparticle signal; accuracy increased the most at very low concentrations (~1ug). Therefore, these preliminary data indicate that the MAG-filter increases SPMR sensitivity for low concentrations representative of very small clusters of cells, typical of early disease stages. Future work will further optimize this initial filter to work uniformly and effectively across different nanoparticle concentrations (and tumor sizes) and translate this technology to highly sensitive early and sparse tumor detection. Note: This abstract was not presented at the conference. Citation Format: Mehdi Baqri, Sri Kandala, David Fuentes. A preprocessing filter to improve superparamagnetic relaxometry (SPMR)-based early tumor detection [abstract]. In: Proceedings of the AACR Special Conference on the Evolving Landscape of Cancer Modeling; 2020 Mar 2-5; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2020;80(11 Suppl):Abstract nr B31.

High-Resolution Optical Measurement of Cardiac Restitution, Contraction, and Fibrillation Dynamics in Beating vs. Blebbistatin-Uncoupled Isolated Rabbit Hearts.

Optical mapping is a high-resolution fluorescence imaging technique, that uses voltage- or calcium-sensitive dyes to visualize electrical excitation waves on the heart surface. However, optical mapping is very susceptible to the motion of cardiac tissue, which results in so-called motion artifacts in the fluorescence signal. To avoid motion artifacts, contractions of the heart muscle are typically suppressed using pharmacological excitation-contraction uncoupling agents, such as Blebbistatin. The use of pharmacological agents, however, may influence cardiac electrophysiology. Recently, it has been shown that numerical motion tracking can significantly reduce motion-related artifacts in optical mapping, enabling the simultaneous optical measurement of cardiac electrophysiology and mechanics. Here, we combine ratiometric optical mapping with numerical motion tracking to further enhance the robustness and accuracy of these measurements. We evaluate the method's performance by imaging and comparing cardiac restitution and ventricular fibrillation (VF) dynamics in contracting, non-working vs. Blebbistatin-arrested Langendorff-perfused rabbit hearts (N = 10). We found action potential durations (APD) to be, on average, 25 ± 5% shorter in contracting hearts compared to hearts uncoupled with Blebbistatin. The relative shortening of the APD was found to be larger at higher frequencies. VF was found to be significantly accelerated in contracting hearts, i.e., 9 ± 2Hz with Blebbistatin and 15 ± 4Hz without Blebbistatin, and maintained a broader frequency spectrum. In contracting hearts, the average number of phase singularities was NPS = 11 ± 4 compared to NPS = 6 ± 3 with Blebbistatin during VF on the anterior ventricular surface. VF inducibility was reduced with Blebbistatin. We found the effect of Blebbistatin to be concentration-dependent and reversible by washout. Aside from the electrophysiological characterization, we also measured and analyzed cardiac motion. Our findings may have implications for the interpretation of optical mapping data, and highlight that physiological conditions, such as oxygenation and metabolic demand, must be carefully considered in ex vivo imaging experiments.

Technical Note: A deuterated 13 C-urea reference for clinical multiparametric MRI prostate cancer studies including hyperpolarized pyruvate.

Metabolic magnetic resonance imaging (MRI) using hyperpolarized [1-13 C]-pyruvate offers unprecedented new insight into disease and response to therapy. 13 C-enriched reference standards are required to enable fast and accurate calibration for 13 C studies, but care must be taken to ensure that the reference is compatible with both 13 C and 1 H acquisitions. The goal of this study was to optimize the composition of a 13 C-urea reference for a dual-tuned 13 C/1 H endorectal coil and minimize imaging artifacts in metabolic and multiparametric MRI studies involving hyperpolarized [1-13 C]-pyruvate. Due to a high amount of Gd doping for the purpose of reducing the spin-lattice relaxation time (T1 ) of urea, the 1 H signal produced by a reference of 13 C-urea in normal water was rapidly relaxed, resulting in severe artifacts in heavily T1 -weighted images. Hyperintense ringing artifacts in 1 H images were mitigated by reducing the 1 H concentration in a 13 C-urea reference via deuteration and lyophilization. Several references were fabricated and their SNR was compared using 1 H and 13 C imaging sequences on a 3T MRI scanner. Finally, 1 H prostate phantom imaging was conducted to compare image quality and 1 H signal intensity of normal and deuterated urea references. The deuterated 13 C-urea reference provides strong 13 C signal for calibration and an attenuated 1 H signal that does not interfere with heavily T1 -weighted scans. Deuteration and lyophilization were fundamental to the reduction in 1 H signal and hyperintense ringing artifacts. There was a 25-fold reduction in signal intensity when comparing the nondeuterated reference to the deuterated reference, while the 13 C signal was unaffected. A deuterated reference reduced hyperintense ringing artifacts in 1 H images by reducing the 1 H signal produced from the 13 C-urea in the reference. The deuterated reference can be used to improve anatomical image quality in future clinical 1 H and hyperpolarized [1-13 C]-pyruvate MRI prostate imaging studies.

Effects of Intraoperative Magnetic Resonance Thermal Imaging Signal Artifact During Laser Interstitial Thermal Therapy on Thermal Damage Estimate and Postoperative Magnetic Resonance Imaging Ablative Area Concordance.

Magnetic resonance-guided laser interstitial thermal therapy (MRgLITT) is a minimally invasive procedure that utilizes intraoperative magnetic resonance thermal imaging (MRTI) to generate a thermal damage estimate (TDE) of the ablative area. In select cases, the MRTI contains a signal artifact or defect that distorts the ablative region. No study has considered the impact of this artifact on TDE accuracy. To determine the effect of intraoperative MRTI signal artifact on postoperative magnetic resonance imaging (MRI)-predicted ablative area. All ablations were performed using the Visualase MRI-Guided Laser Ablation System (Medtronic). Patients were grouped based on whether the intraoperative MRTI contained signal artifact that distorted the ablative region. Cross-sectional area of the ablative lesion from the MRI image was measured, and the difference between intraoperative TDE and postoperative MRI cross-sectional area was calculated and compared between groups with and without intraoperative MRTI artifact. A total of 91 patients undergoing MRgLITT for various surgical indications were examined. MRTI artifact was observed in 43.9% of cases overall. The mean absolute difference between TDE and the postoperative MRI cross-sectional area was 94.8 mm2 (SEM=11.6) in the group with intraoperative MRTI artifact and 54.4 mm2 (SEM=5.5) in the nonartifact group. MRTI signal artifact is common during LITT. The presence of signal artifact during intraoperative MRTI results in higher variation between intraoperative TDE and postoperative MRI cross-sectional ablative area. In cases in which intraoperative MRTI artifact is observed, there may be a larger degree of variation between observed intraoperative TDE and measured postoperative MRTI ablative area.