Increase In Signal-to-noise Ratio Research Articles

A size-adaptive 32-channel array coil for awake infant neuroimaging at 3Tesla MRI.

Functional magnetic resonance imaging (fMRI) during infancy poses challenges due to practical, methodological, and analytical considerations. The aim of this study was to implement a hardware-related approach to increase subject compliance for fMRI involving awake infants. To accomplish this, we designed, constructed, and evaluated an adaptive 32-channel array coil. To allow imaging with a close-fitting head array coil for infants aged 1-18 months, an adjustable head coil concept was developed. The coil setup facilitates a half-seated scanning position to improve the infant's overall scan compliance. Earmuff compartments are integrated directly into the coil housing to enable the usage of sound protection without losing a snug fit of the coil around the infant's head. The constructed array coil was evaluated from phantom data using bench-level metrics, signal-to-noise ratio (SNR) performances, and accelerated imaging capabilities for both in-plane and simultaneous multislice (SMS) reconstruction methodologies. Furthermore, preliminary fMRI data were acquired to evaluate the in vivo coil performance. Phantom data showed a 2.7-fold SNR increase on average when compared with a commercially available 32-channel head coil. At the center and periphery regions of the infant head phantom, the SNR gains were measured to be 1.25-fold and 3-fold, respectively. The infant coil further showed favorable encoding capabilities for undersampled k-space reconstruction methods and SMS techniques. An infant-friendly head coil array was developed to improve sensitivity, spatial resolution, accelerated encoding, motion insensitivity, and subject tolerance in pediatric MRI. The adaptive 32-channel array coil is well-suited for fMRI acquisitions in awake infants.

A smarter algorithm-IFR3+FFR in wireless network to optimize frequency allocation

All latest technologies require frequency channels to be allocated intelligently so that interference can be avoided. Nowadays, it is still quite challenging for people to propose an effective method to enhance the frequency allocation system, which reduces interference further and allows more users to utilize the base station at the same time. Here we describe the method of improving the frequency allocation system-IFR3 (Integer frequency reuse) plus FFR (fractional frequency reuse) in a nineteen-cell network. We simulate the system based on MATLAB, comparing capacity results against different parameters. We draw some useful conclusions after the simulation. When the number of frequencies increases, average network capacity also increases. When SNR increases, average network capacity increases at the beginning, and then at a certain critical point, it starts to decrease. Thereby, it is required to find the best system with both the appropriate number of frequencies and SNR to maximize the network capacity. As for users who are in the distance, the interference will be large when using IFR1 but small if using IFR3. Therefore, finding the best reuse factor when using FFR is indispensable. Smaller transmit power should be allocated to users in the cell’s inner area, and larger transmit power allocated to users in the outer area of the cell. This system is more advanced and capable of reducing interference efficiently.

Psychometric function for speech-in-noise tests accounts for word-recognition deficits in older listeners.

Speech-in-noise (SIN) understanding in older age is affected by hearing loss, impaired central auditory processing, and cognitive deficits. SIN-tests measure these factors' compound effects by a speech reception threshold, defined as the signal-to-noise ratio required for 50% word understanding (SNR50). This study compared two standard SIN tests, QuickSIN (n = 354) in young and older adults and BKB-SIN (n = 139) in older adults (>60 years). The effects of hearing loss and age on SIN understanding were analyzed to identify auditory and nonauditory contributions to SIN loss. Word recognition in noise was modelled with individual psychometric functions using a logistic fit with three parameters: the midpoint (SNRα), slope (β), and asymptotic word-recognition deficit at high SNR (λ). The parameters SNRα and λ formally separate SIN loss into two components. SNRα characterizes the steep slope of the psychometric function at which a slight SNR increase provides a considerable improvement in SIN understanding. SNRα was discussed as being predominantly affected by audibility and low-level central auditory processing. The parameter λ describes a shallow segment of the psychometric function at which a further increase in the SNR provides modest improvement in SIN understanding. Cognitive factors in aging may contribute to the SIN loss indicated by λ.

Adaptive Power Control with Selection Combiner over Nakagami-m Fading Channel

Acceptable Quality of Service (QoS) is of paramount importance in wireless communication system. However, QoS is affected by multipath propagation resulting in unreliable reception. Selection Combiner (SC) which is one of the techniques previously used to solve this problem is associated with poor performance due to fixed power level used in the technique. Hence, in this paper, an Adaptive Power Control (APC) technique using SC with a closed form expression over Nakagami-m Fading Channel is proposed. APC technique is developed using fixed power SC and Link Adaptation Algorithm (LAA). Ten thousand (10,000) bits are randomly generated and modulated using Quadrature Amplitude Modulation (QAM) scheme. The modulated signal is passed through the Square Root Raised Cosine (SRRC) filter for suitable transmission over the Nakagami-m fading channel. The faded signals at varying paths ‘L’ (2, 3, 4) are selected by Conventional Selection Combining (CSC) to instruct the transmitter for adjustment of power level based on the value of the received signal through LAA. Mathematical expression for the received signal using Probability Density Function (PDF) of Nakagami-m Fading Channel at varying paths is derived. The APC technique is simulated using MATLAB software and evaluated using Signal to Noise Ratio (SNR), Outage Probability (OP) and Bit Error Rate (BER) to determine the performance of the proposed technique. OP values of 0.0969 and 0.1069 are obtained for APC and CSC, respectively, at SNR of 6 dB with L of 4, while BER values of 0.0052 and 0.2599 are obtained for APC and CSC, respectively. The percentage reduction in OP and BER are 29.79% and 54.43%, respectively. The results obtained show that APC gives lower OP and BER values with increase in SNR when compared with fixed power SC due to self-adjustment of the allocated power. Therefore, the APC technique proposed can be used to enhance the performance of wireless communication systems. Keywords— Adaptive Power Control (APC), Selection Combiner (SC), Link Adaptation Algorithm (LAA), Bit Error Rate (BER), Outage Probability (OP), Probability Density Function (PDF) and Diversity Combiner (DC).

GCPS: A CNN Performance Evaluation Criterion for Radar Signal Intrapulse Modulation Recognition

In radar intrapulse modulation recognition through convolutional neural networks (CNN), the recognition rate vis-a-vis varying signal-to-noise ratios (SNRs) is utilized to analyze the performance of networks. However, three shortcomings exist in this realm, (1) Proportional increase in recognition rate up to 100%, due to SNR increase, leading to saturation. (2) The dependency of recognition rate on the test set. (3) Recognition rate failing to substantiate the increase in SNR and its manifestation in CNN performance enhancement. Foregone in view, this letter proposes an evaluation criterion to evaluate CNN performance to address the shortcomings above. We propose Grad-CAM position score-Internal Benchmark (GCPS-IB) to address (1), (2), and Grad-CAM position score-External Benchmark (GCPS-EB) to address (3). Finally, we utilize GCPS to evaluate and compare the performance of existing GoogLeNet and ResNet-18 to verify the efficacy. The proposed criterion establishes its efficacy by addressing the shortcomings mentioned above, hence augmenting the recognition rate.

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

BackgroundImage quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively.Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.ResultsClinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM.Conclusion68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

Power-Consumption Outage in Beyond Fifth Generation Mobile Communication Systems.

One of the biggest problems facing future mobile systems beyond 5G (B5G) is the energy dissipation of mobile devices at high data rates. The heat generated by these devices can impact the performance as a result of a new type of outage called power-consumption outage. In this article, we propose a general definition of the power-consumption outage and describe its three features. Based on the heat transfer model in smartphones, the power-consumption outage probability is analyzed. Specifically, we derive the joint outage probability of channel and power-consumption outages in relation to the signal-to-noise ratio (SNR), communication duration, and initial temperature of the smartphone-back-plate. The joint outage probability is then used to obtain the upper bound of the maximum receiving rate of a typical smartphone. Furthermore, we propose and analyze the impact on the capacity of the power-consumption outage. Simulation results show that the power-consumption outage probability increases with an increase of SNR and with extension of the communication duration. The upper bound of the maximum receiving rate of a smartphone decreases with an extension of communication duration. Considering the joint outage probability, simulation results show that the outage capacities, i.e., channel and power-consumption outages, decrease with an increase of SNR after reaching a given capacity threshold.

Magnetic resonance microscopy of samples with translational symmetry with FOVs smaller than sample size

In MRI, usually the Field of View (FOV) has to cover the entire object. If this condition is not fulfilled, an infolding image artifact is observed, which suppresses visualization. In this study it is shown that for samples with translational symmetry, i.e., those consisting of identical objects in periodic unit cells, the FOV can be reduced to match the unit cell which enables imaging of an average object, of which the signal is originated from all unit cells of the sample, with no punishment by a loss in signal-to-noise ratio (SNR). This theoretical prediction was confirmed by experiments on a test sample with a 7 × 7 mm2 unit cell arranged in a 3 × 3 matrix which was scanned by the spin-echo and by single point imaging methods. Effects of experimental imperfections in size and orientation mismatch between FOV and unit cell were studied as well. Finally, this method was demonstrated on a 3D periodic sample of tablets, which yielded well-resolved images of moisture distribution in an average tablet, while single tablet imaging provided no results. The method can be applied for SNR increase in imaging of any objects with inherently low signals provided they can be arranged in a periodic structure.

Random noise suppression of magnetic resonance sounding signal based on modified short-time Fourier transform

Magnetic resonance sounding (MRS) has the advantage of detecting groundwater content directly without drilling, but the signal-to-noise ratio (SNR) is extremely low which limits the application of the method. Most of the current researches focus on eliminating spikes and powerline harmonic noise in the MRS signal, whereas the influence of random noise cannot be ignored even though it is difficult to suppress due to the irregularity. The common method to eliminate MRS random noise is stacking which requires extensive measurement repetition at the cost of detection efficiency, and it is insufficient when employed in a high-level noise surrounding. To solve this problem, we propose a modified short-time Fourier transform(MSTFT) method, in which used is the short-time Fourier transform on the analytical signal instead of the real-valued signal to obtain the high-precision time-frequency distribution of MRS signal, followed by extracting the time-frequency domain peak amplitude and peak phase to reconstruct the signal and suppress the random noise. The performance of the proposed method is tested on synthetic envelope signals and field data. The using of the MSTFT method to handle a single recording can suppress the random noise and extract MRS signals when SNR is more than –17.21 dB. Compared with the stacking method, the MSTFT achieves an 27.88dB increase of SNR and more accurate parameter estimation. The findings of this study lay a good foundation for obtaining exact groundwater distribution by utilizing magnetic resonance sounding.

Integrating photoacoustic tomography into a multimodal automated breast ultrasound scanner.

.Significance: Photoacoustic tomography (PAT) is a promising emergent modality for the screening and staging of breast cancer. To minimize barriers to clinical translation, it is common to develop PAT systems based upon existing ultrasound hardware, which can entail significant design challenges in terms of light delivery. This often results in inherently non-uniform fluence within the tissue and should be accounted for during image reconstruction.Aim: We aim to integrate PAT into an automated breast ultrasound scanner with minimal change to the existing system.Approach: We designed and implemented an illuminator that directs spatially non-uniform light to the tissue near the acquisition plane of the imaging array. We developed a graphics processing unit-accelerated reconstruction method, which accounts for this illumination geometry by modeling the structure of the light in the sample. We quantified the performance of this system using a custom, modular photoacoustic phantom and graphite rods embedded in chicken breast tissue.Results: Our illuminator provides a fluence of at the tissue surface, which was sufficient to attain a signal-to-noise ratio (SNR) of 8 dB at 2 cm in chicken breast tissue and image 0.25-mm features at depths of up to 3 cm in a medium with moderate optical scattering. Our reconstruction scheme is faster than a CPU implementation; it provides a 25% increase in SNR at 2 cm in chicken breast tissue and lowers image error by an average of 31% at imaging depths compared with a method that does not account for the inhomogeneity of the illumination or the transducer directivity.Conclusions: A fan-shaped illumination geometry is feasible for PAT; however, it is important to account for non-uniform fluence in illumination scenarios such as this. Future work will focus on increasing fluence and further optimizing the ultrasound hardware to improve SNR and overall image quality.

The Sensitivity Improvement Characterization of Distributed Strain Sensors Due to Weak Fiber Bragg Gratings.

Weak fiber Bragg gratings (WFBGs) in a phase-sensitive optical time-domain reflectometer (phi-OTDR) sensor offer opportunities to significantly improve the signal-to-noise ratio (SNR) and sensitivity of the device. Here, we demonstrate the process of the signal and noise components’ formation in the device reflectograms for a Rayleigh scattering phi-OTDR and a WFBG-based OTDR. We theoretically calculated the increase in SNR when using the same optical and electrical components under the same external impacts for both setups. The obtained values are confirmed on experimental installations, demonstrating an improvement in the SNR by about 19 dB at frequencies of 20, 100, and 400 Hz. In this way, the minimum recorded impact (at the threshold SNR = 10) can be reduced from 100 nm per 20 m of fiber to less than 5 nm per 20 m of fiber sensor.

Restoration of the electrocardiogram during mechanical cardiopulmonary resuscitation

Objective: An artefact-free electrocardiogram (ECG) is essential during cardiac arrest to decide therapy such as defibrillation. Mechanical cardiopulmonary resuscitation (CPR) devices cause movement artefacts that alter the ECG. This study analyzes the effectiveness of mechanical CPR artefact suppression filters to restore clinically relevant ECG information. Approach: In total, 495 10 s ECGs were used, of which 165 were in ventricular fibrillation (VF), 165 in organized rhythms (OR) and 165 contained mechanical CPR artefacts recorded during asystole. CPR artefacts and rhythms were mixed at controlled signal-to-noise ratios (SNRs), ranging from –20 dB to 10 dB. Mechanical artefacts were removed using least mean squares (LMS), recursive least squares (RLS) and Kalman filters. Performance was evaluated by comparing the clean and the restored ECGs in terms of restored SNR, correlation-based similarity measures, and clinically relevant features: QRS detection performance for OR, and dominant frequency, mean amplitude and waveform irregularity for VF. For each filter, a shock/no-shock support vector machine algorithm based on multiresolution analysis of the restored ECG was designed, and evaluated in terms of sensitivity (Se) and specificity (Sp). Main results: The RLS filter produced the largest correlation coefficient (0.80), the largest average increase in SNR (9.5 dB), and the best QRS detection performance. The LMS filter best restored VF with errors of 10.3% in dominant frequency, 18.1% in amplitude and 11.8% in waveform irregularity. The Se/Sp of the diagnosis of the restored ECG were 95.1/94.5% using the RLS filter and 97.0/91.4% using the LMS filter. Significance: Suitable filter configurations to restore ECG waveforms during mechanical CPR have been determined, allowing reliable clinical decisions without interrupting mechanical CPR therapy.

Quality of service (QoS): measurements of image formats in social cloud computing

With the development of multimedia and the popularity of social cloud (SC), Quality of Service (QoS) is used to measure the technical parameters of service delivery. QoS is mostly used for measuring various kinds of multimedia data. Sharing (uploading) on SC has become the daily activity of end-users. As a result of this activity, it provides an open challenge for service providers. As a service provider, the host delivers productive infrastructure, allowing end-users to upload and share their high-quality images. To evaluate the QoS of image compression, we conduct objective QoS techniques to measure the efficiency of image quality and QoS performance of service delivery via uploading /downloading of images on three popular SC, (Facebook, Twitter, and Instagram). These social clouds and image services are compared by the mean, standard deviation (STDEV),mean square error (MSE), and signal-to-noise ratio (SNR). So we can achieve better results about the SC image quality. The results show that Instagram and Facebook compressed images more as compared to Twitter. However, Twitter less supports image formats and provides an acceptable quality of compressed images as compared to others. Therefore, Facebook supports all image file formats and enhanced (QoE) levels of end-users, but Twitter provided the best QoS of compressed images as compared to Instagram. Further, we found that the decrease of MSE and increase of SNR both have a high impact on image resolutions as compared to original image parameters, which has a higher effect on quality. For the specific quality assurance of image tasks acquired for robust automated image analysis research, we are also using deep learning techniques (DLT) to classify the quality of images. We also have identified the opportunity to simplify the Convolutional Neural network (CNN) with smaller resampling grids and making this process more suitable for at least five thousand (5000) of the enormous datasets available in the future.

High-sensitivity multispeckle diffuse correlation spectroscopy.

.Significance: Cerebral blood flow is an important biomarker of brain health and function as it regulates the delivery of oxygen and substrates to tissue and the removal of metabolic waste products. Moreover, blood flow changes in specific areas of the brain are correlated with neuronal activity in those areas. Diffuse correlation spectroscopy (DCS) is a promising noninvasive optical technique for monitoring cerebral blood flow and for measuring cortex functional activation tasks. However, the current state-of-the-art DCS adoption is hindered by a trade-off between sensitivity to the cortex and signal-to-noise ratio (SNR).Aim: We aim to develop a scalable method that increases the sensitivity of DCS instruments.Approach: We report on a multispeckle DCS (mDCS) approach that is based on a 1024-pixel single-photon avalanche diode (SPAD) camera. Our approach is scalable to independent speckle measurements since large-pixel-count SPAD cameras are becoming available, owing to the investments in LiDAR technology for automotive and augmented reality applications.Results: We demonstrated a 32-fold increase in SNR with respect to traditional single-speckle DCS.Conclusion: A mDCS system that is based on a SPAD camera serves as a scalable method toward high-sensitivity DCS measurements, thus enabling both high sensitivity to the cortex and high SNR.

Physical Layer Security Over Mixture Gamma Distributed Fading Channels With Discrete Inputs: A Unified and General Analytical Framework

Physical layer security is investigated over mixture Gamma (MG) distributed fading channels with discrete inputs. By the Gaussian quadrature rules, closed-form expressions are derived to characterize the average secrecy rate (ASR) and secrecy outage probability (SOP), whose accuracy is validated by numerical simulations. To show more properties of the finite-alphabet signaling, we perform an asymptotic analysis on the secrecy metrics in the large limit of the average signal-to-noise ratio (SNR) of the main channel. Leveraging the Mellin transform, we find that the ASR and SOP converge to some constants as the average SNR increases and we derive novel expressions to characterize the rates of convergence. This work establishes a unified and general analytical framework for the secrecy performance achieved by discrete inputs.

Imaging of Stroke in Rodents Using a Clinical Scanner and Inductively Coupled Specially Designed Receiver Coils.

Imaging of small laboratory animals in clinical MRI scanners is feasible but challenging. Compared with dedicated preclinical systems, clinical scanners have relatively low B0 field (1.5-3.0 T) and gradient strength (40-60 mT/m). This work explored the use of wireless inductively coupled coils (ICCs) combined with appropriate pulse sequence parameters to overcome these two drawbacks, with a special emphasis on the optimization of the coil passive detuning circuit for this application. A Bengal rose photothrombotic stroke model was used to induce cortical infarction in rats and mice. Animals were imaged in a 3T scanner using T2 and T1-weighted sequences. In all animals, the ICCs allowed acquisition of high-quality images of the infarcted brain at acute and chronic stages. Images obtained with the ICCs showed a substantial increase in SNR compared to clinical coils (by factors of 6 in the rat brain and 16-17 in the mouse brain), and the absence of wires made the animal preparation workflow straightforward.

Frontiers of Sodium MRI Revisited: From Cartilage to Brain Imaging.

Sodium magnetic resonance imaging (23Na‐MRI) is a highly promising imaging modality that offers the possibility to noninvasively quantify sodium content in the tissue, one of the most relevant parameters for biochemical investigations. Despite its great potential, due to the intrinsically low signal‐to‐noise ratio (SNR) of sodium imaging generated by low in vivo sodium concentrations, low gyromagnetic ratio, and substantially shorter relaxation times than for proton (1H) imaging, 23Na‐MRI is extremely challenging. In this article, we aim to provide a comprehensive overview of the literature that has been published in the last 10–15 years and which has demonstrated different technical designs for a range of 23Na‐MRI methods applicable for disease diagnoses and treatment efficacy evaluations. Currently, a wider use of 3.0T and 7.0T systems provide imaging with the expected increase in SNR and, consequently, an increased image resolution and a reduced scanning time. A great interest in translational research has enlarged the field of sodium MRI applications to almost all parts of the body: articular cartilage tendons, spine, heart, breast, muscle, kidney, and brain, etc., and several pathological conditions, such as tumors, neurological and degenerative diseases, and others. The quantitative parameter, tissue sodium concentration, which reflects changes in intracellular sodium concentration, extracellular sodium concentration, and intra–/extracellular volume fractions is becoming acknowledged as a reliable biomarker. Although the great potential of this technique is evident, there must be steady technical development for 23Na‐MRI to become a standard imaging tool. The future role of sodium imaging is not to be considered as an alternative to 1H MRI, but to provide early, diagnostically valuable information about altered metabolism or tissue function associated with disease genesis and progression.Level of Evidence1Technical Efficacy Stage1

A 32-channel receive array coil for bilateral breast imaging and spectroscopy at 7T.

This work describes the construction and evaluation of a bilateral 32-channel receive array for breast imaging at 7T. The receive array consisted of 32 receive coils, placed on two 3D-printed hemispherical formers. Each side of the receive array consisted of 16 receive loops, each loop having a corresponding detachable board with match/tune capacitors, active detuning circuitry, and a balun. Coil performance was evaluated on homogeneous canola oil phantoms using a Philips Achieva 7T system. Array coil performance was compared with a bilateral forced current excitation volume coil in transmit/receive mode and with a previously reported 16-channel unilateral coil with a similar design. The 32-channel array had an increase in average SNR throughout both phantoms by a factor of five as compared with the volume coil, with SNR increases up to 10 times along the periphery and three times in the center. Noise measurements showed low interelement noise correlation (average: 5.4%; maximum: 16.8%). Geometry factor maps were acquired for various acceleration factors and showed mean geometry factors<1.2, for combined acceleration factors of up to six. The improvements achieved demonstrate the clear potential for use in dynamic contrast-enhanced or diffusion-weighted MR studies, while maintaining diagnostically relevant spatial and temporal resolutions.

A variable resolution approach for improved acquisition of hyperpolarized 13 C metabolic MRI.

To ameliorate tradeoffs between a fixed spatial resolution and signal-to-noise ratio (SNR) for hyperpolarized 13 C MRI. In MRI, SNR is proportional to voxel volume but retrospective downsampling or voxel averaging only improves SNR by the square root of voxel size. This can be exploited with a metabolite-selective imaging approach that independently encodes each compound, yielding high-resolution images for the injected substrate and coarser resolution images for downstream metabolites, while maintaining adequate SNR for each. To assess the efficacy of this approach, hyperpolarized [1-13 C]pyruvate data were acquired in healthy Sprague-Dawley rats (n = 4) and in two healthy human subjects. Compared with a constant resolution acquisition, variable-resolution data sets showed improved detectability of metabolites in pre-clinical renal studies with a 3.5-fold, 8.7-fold, and 6.0-fold increase in SNR for lactate, alanine, and bicarbonate data, respectively. Variable-resolution data sets from healthy human subjects showed cardiac structure and neuro-vasculature in the higher resolution pyruvate images (6.0 × 6.0 mm2 for cardiac and 7.5 × 7.5 mm2 for brain) that would otherwise be missed due to partial-volume effects and illustrates the level of detail that can be achieved with hyperpolarized substrates in a clinical setting. We developed a variable-resolution strategy for hyperpolarized 13 C MRI using metabolite-selective imaging and demonstrated that it mitigates tradeoffs between a fixed spatial resolution and SNR for hyperpolarized substrates, providing both high resolution pyruvate and coarse resolution metabolite data sets in a single exam. This technique shows promise to improve future studies by maximizing metabolite SNR while minimizing partial-volume effects from the injected substrate.

Increased sensitivity and signal-to-noise ratio in diffusion-weighted MRI using multi-echo acquisitions

Post-mortem diffusion MRI (dMRI) enables acquisitions of structural imaging data with otherwise unreachable resolutions - at the expense of longer scanning times. These data are typically acquired using highly segmented image acquisition strategies, thereby resulting in an incomplete signal decay before the MRI encoding continues. Especially in dMRI, with low signal intensities and lengthy contrast encoding, such temporal inefficiency translates into reduced image quality and longer scanning times. This study introduces Multi Echo (ME) acquisitions to dMRI on a human MRI system - a time-efficient approach, which increases SNR (Signal-to-Noise Ratio) and reduces noise bias for dMRI images. The benefit of the introduced ME-dMRI method was validated using numerical Monte Carlo simulations and showcased on a post-mortem brain of a wild chimpanzee. The proposed Maximum Likelihood Estimation echo combination results in an optimal SNR without detectable signal bias. The combined strategy comes at a small price in scanning time (here 30% additional) and leads to a substantial SNR increase (here white matter: ~ 1.6x, equivalent to 2.6 averages, grey matter: ~ 1.9x, equivalent to 3.6 averages) and a general reduction of the noise bias.