Reduce Acquisition Time Research Articles

Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers.

A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson's correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (-0.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis.

Pushing MP2RAGE boundaries: Ultimate time-efficient parameterization combined with exhaustive T1 synthetic contrasts.

MP2RAGE parameter optimization is redefined to allow more time-efficient MR acquisitions, whereas the T1 -based synthetic imaging framework is used to obtain on-demand T1 -weighted contrasts. Our aim was to validate this concept on healthy volunteers and patients with multiple sclerosis, using plug-and-play parallel-transmission brain imaging at 7 T. A "time-efficient" MP2RAGE sequence was designed with optimized parameters including TI and TR set as small as possible. Extended phase graph formalism was used to set flip-angle values to maximize the gray-to-white-matter contrast-to-noise ratio (CNR). Several synthetic contrasts (UNI, EDGE, FGATIR, FLAWSMIN , FLAWSHCO ) were generated online based on the acquired T1 maps. Experimental validation was performed on 4 healthy volunteers at various spatial resolutions. Clinical applicability was evaluated on 6 patients with multiple sclerosis, scanned with both time-efficient and conventional MP2RAGE parameterizations. The proposed time-efficient MP2RAGE protocols reduced acquisition time by 40%, 30%, and 19% for brain imaging at (1 mm)3 , (0.80 mm)3 and (0.65 mm)3 , respectively, when compared with conventional parameterizations. They also provided all synthetic contrasts and comparable contrast-to-noise ratio on UNI images. The flexibility in parameter selection allowed us to obtain a whole-brain (0.45 mm)3 acquisition in 19 min 56 s. On patients with multiple sclerosis, a (0.67 mm)3 time-efficient acquisition enhanced cortical lesion visualization compared with a conventional (0.80 mm)3 protocol, while decreasing the scan time by 15%. The proposed optimization, associated with T1 -based synthetic contrasts, enabled substantial decrease of the acquisition time or higher spatial resolution scans for a given time budget, while generating all typical brain contrasts derived from MP2RAGE.

Specific and rapid guanidinium CEST imaging using double saturation power and QUASS analysis in a rodent model of global ischemia.

Guanidinium CEST is sensitive to metabolic changes and pH variation in ischemia, and it can offer advantages over conventional pH-sensitive amide proton transfer (APT) imaging by providing hyperintense contrast in stroke lesions. However, quantifying guanidinium CEST is challenging due to multiple overlapping components and a close frequency offset from water. This study aims to evaluate the applicability of a new rapid and model-free CEST quantification method using double saturation power, termed DSP-CEST, for isolating the guanidinium CEST effect from confounding factors in ischemia. To further reduce acquisition time, the DSP-CEST was combined with a quasi-steady state (QUASS) CEST technique to process non-steady-state CEST signals. The specificity and accuracy of the DSP-CEST method in quantifying the guanidinium CEST effect were assessed by comparing simulated CEST signals with/without the contribution from confounding factors. The feasibility of this method for quantifying guanidinium CEST was evaluated in a rat model of global ischemia induced by cardiac arrest and compared to a conventional multiple-pool Lorentzian fit method. The DSP-CEST method was successful in removing all confounding components and quantifying the guanidinium CEST signal increase in ischemia. This suggests that the DSP-CEST has the potential to provide hyperintense contrast in stroke lesions. Additionally, the DSP-CEST was shown to be a rapid method that does not require the acquisition of the entire or a portion of the CEST Z-spectrum that is required in conventional model-based fitting approaches. This study highlights the potential of DSP-CEST as a valuable tool for rapid and specific detection of viable tissues.

M3S-Net: multi-modality multi-branch multi-self-attention network with structure-promoting loss for low-dose PET/CT enhancement.

Objective.PET (Positron Emission Tomography) inherently involves radiotracer injections and long scanning time, which raises concerns about the risk of radiation exposure and patient comfort. Reductions in radiotracer dosage and acquisition time can lower the potential risk and improve patient comfort, respectively, but both will also reduce photon counts and hence degrade the image quality. Therefore, it is of interest to improve the quality of low-dose PET images.Approach.A supervised multi-modality deep learning model, named M3S-Net, was proposed to generate standard-dose PET images (60 s per bed position) from low-dose ones (10 s per bed position) and the corresponding CT images. Specifically, we designed a multi-branch convolutional neural network with multi-self-attention mechanisms, which first extracted features from PET and CT images in two separate branches and then fused the features to generate the final generated PET images. Moreover, a novel multi-modality structure-promoting term was proposed in the loss function to learn the anatomical information contained in CT images.Main results.We conducted extensive numerical experiments on real clinical data collected from local hospitals. Compared with state-of-the-art methods, the proposed M3S-Net not only achieved higher objective metrics and better generated tumors, but also performed better in preserving edges and suppressing noise and artifacts.Significance.The experimental results of quantitative metrics and qualitative displays demonstrate that the proposed M3S-Net can generate high-quality PET images from low-dose ones, which are competable to standard-dose PET images. This is valuable in reducing PET acquisition time and has potential applications in dynamic PET imaging.

Dynamic total-body PET/CT imaging with reduced acquisition time shows acceptable performance in quantification of [18F]FDG tumor kinetic metrics.

To investigate the feasibility of reducing the acquisition time for continuous dynamic positron emission tomography (PET) while retaining acceptable performance in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose ([18F]FDG) in tumors. In total, 78 oncological patients underwent total-body dynamic PET imaging for ≥ 60min, with 8, 20, and 50 patients receiving full activity (3.7 MBq/kg), half activity (1.85 MBq/kg), and ultra-low activity (0.37 MBq/kg) of [18F]FDG, respectively. The dynamic data were divided into 21-, 30-, 45- and ≥ 60-min groups. The kinetic analysis involved model fitting to derive constant rates (VB, K1 to k3, and Ki) for both tumors and normal tissues, using both reversible and irreversible two-tissue-compartment models. One-way ANOVA with repeated measures or the Freidman test compared the kinetic metrics among groups, while the Deming regression assessed the correlation of kinetic metrics among groups. All kinetic metrics in the 30-min and 45-min groups were statistically comparable to those in the ≥ 60-min group. The relative differences between the 30-min and ≥ 60-min groups ranged from 12.3% ± 15.1% for K1 to 29.8% ± 30.0% for VB, and those between the 45-min and ≥ 60-min groups ranged from 7.5% ± 8.7% for Ki to 24.0% ± 24.3% for VB. However, this comparability was not observed between the 21-min and ≥ 60-min groups. The significance trend of these comparisons remained consistent across different models (reversible or irreversible), administrated activity levels, and partial volume corrections for lesions. Significant correlations in tumor kinetic metrics were identified between the 30-/45-min and ≥ 60-min groups, with Deming regression slopes > 0.813. In addition, the comparability of kinetic metrics between the 30-min and ≥ 60-min groups were established for normal tissues. The acquisition time for dynamic PET imaging can be reduced to 30min without compromising the ability to reveal tumor kinetic metrics of [18F]FDG, using the total-body PET/CT system.

An ultra‐fast MRI protocol in dementia enabled by Wave‐CAIPI

AbstractBackgroundStructural brain imaging is essential for the diagnostic workup in dementia and for safety monitoring of disease modifying therapies. MRI has several advantages over other imaging techniques, but its use is limited by cost and availability. Reducing acquisition time holds the potential for its democratisation. We developed a prototype ultra‐fast MRI protocol based on an optimised 3D parallel imaging approach. Here we present the finalised version, developed using a qualitative and quantitative approach to optimisation.MethodIn this ongoing real‐world study, participants are sequentially recruited from the cognitive disorders' clinics of a tertiary centre. Patients due to have a standard 3T MRI scan as part of their diagnostic pathway are eligible. The standard‐of‐care protocol includes T1w, T2w, FLAIR and T2*/SWI sequences [scan time ∼18mins]. The prototype rapid protocol utilises Siemens work‐in‐progress 3D Wave‐CAIPI (Controlled Aliasing in Parallel Imaging) sequences, enabling a reduction in scan time of more than 60% (table 1). For qualitative analysis, scans (of 9 individuals) were reviewed by six raters and assessed side‐by‐side against the gold‐standard scan for diagnostic utility. For a quantitative analysis, volumes were calculated from T1w scans using the default cortical reconstruction and volumetric segmentation pipeline in FreeSurfer 7.3.2. Key volumes (e.g., deep grey matter, cortical grey matter, white matter) for each participant were compared between the two protocols. Independent visual QC was performed on T1w datasets by an experienced clinical trials team.ResultSide‐by‐side visual assessment results of 9 participants is shown in figure 1. For all sequences bar FLAIR, raters scored the ultra‐fast sequences of comparable image quality in at least 67% of cases. For quantitative analysis, 10 T1w sequence pairs were reviewed, with 1 clinical scan failing QC due to motion. Bland‐Altman analyses (figure 2.) comparing volumes between the clinical and ultra‐fast sequences show little bias.ConclusionOur preliminary results demonstrate that for most contrasts the ultra‐fast sequences are diagnostically non‐inferior and quantitatively equivalent to the clinical protocol both for diagnosis and for safety monitoring.

Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla

To increase granularity in human neuroimaging science, we designed and built a next-generation 7 Tesla magnetic resonance imaging scanner to reach ultra-high resolution by implementing several advances in hardware. To improve spatial encoding and increase the image signal-to-noise ratio, we developed a head-only asymmetric gradient coil (200 mT m−1, 900 T m−1s−1) with an additional third layer of windings. We integrated a 128-channel receiver system with 64- and 96-channel receiver coil arrays to boost signal in the cerebral cortex while reducing g-factor noise to enable higher accelerations. A 16-channel transmit system reduced power deposition and improved image uniformity. The scanner routinely performs functional imaging studies at 0.35–0.45 mm isotropic spatial resolution to reveal cortical layer functional activity, achieves high angular resolution in diffusion imaging and reduces acquisition time for both functional and structural imaging.

Assessment of the Impact of Turbo Factor on Image Quality and Tissue Volumetrics in Brain Magnetic Resonance Imaging Using the Three-Dimensional T1-Weighted (3D T1W) Sequence.

The 3D T1W turbo field echo sequence is a standard imaging method for acquiring high-contrast images of the brain. However, the contrast-to-noise ratio (CNR) can be affected by the turbo factor, which could affect the delineation and segmentation of various structures in the brain and may consequently lead to misdiagnosis. This study is aimed at evaluating the effect of the turbo factor on image quality and volumetric measurement reproducibility in brain magnetic resonance imaging (MRI). Brain images of five healthy volunteers with no history of neurological diseases were acquired on a 1.5 T MRI scanner with varying turbo factors of 50, 100, 150, 200, and 225. The images were processed and analyzed with FreeSurfer. The influence of the TFE factor on image quality and reproducibility of brain volume measurements was investigated. Image quality metrics assessed included the signal-to-noise ratio (SNR) of white matter (WM), CNR between gray matter/white matter (GM/WM) and gray matter/cerebrospinal fluid (GM/CSF), and Euler number (EN). Moreover, structural brain volume measurements of WM, GM, and CSF were conducted. Turbo factor 200 produced the best SNR (median = 17.01) and GM/WM CNR (median = 2.29), but turbo factor 100 offered the most reproducible SNR (IQR = 2.72) and GM/WM CNR (IQR = 0.14). Turbo factor 50 had the worst and the least reproducible SNR, whereas turbo factor 225 had the worst and the least reproducible GM/WM CNR. Turbo factor 200 again had the best GM/CSF CNR but offered the least reproducible GM/CSF CNR. Turbo factor 225 had the best performance on EN (-21), while turbo factor 200 was next to the most reproducible turbo factor on EN (11). The results showed that turbo factor 200 had the least data acquisition time, in addition to superior performance on SNR, GM/WM CNR, GM/CSF CNR, and good reproducibility characteristics on EN. Both image quality metrics and volumetric measurements did not vary significantly (p > 0.05) with the range of turbo factors used in the study by one-way ANOVA analysis. Since no significant differences were observed in the performance of the turbo factors in terms of image quality and volume of brain structure, turbo factor 200 with a 74% acquisition time reduction was found to be optimal for brain MR imaging at 1.5 T.

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm.

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality,decreased administered activity and/or acquisition times. To evaluate the impact of reducing counts(simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images. Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60s/bed, β=800; 50s/bed, β=900; 40s/bed, β=1000; and 30s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions. The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90s/bed, β=700 only at a simulated acquisition time of 30s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40s/bed, β=1000 images were generally preferred over 60s/bed TOF OSEM images. In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Front Cover

A deep learning model based on hybrid convolutional and recurrent neural networks is proposed for generating sequential optoacoustic, unmixed optoacoustic of a specific contrast agent, and ultrasound images in order to extend the stack of crosssectional images and reduce acquisition time by approximately 71%.For further details please visit the article by Aniwat Juhong, Bo Li, Yifan Liu, Cheng‐You Yao, Chia‐Wei Yang, Dalen W. Agnew, Yu Leo Lei, Gary D. Luker, Harvey Bumpers, Xuefei Huang, Wibool Piyawattanametha, and Zhen Qiu (e202300142).image

Diagnostic accuracy of virtual non-contrast CT for aortic valve stenosis severity evaluation

BackgroundComputed tomography aortic valve calcium (AVC) score has accepted value for diagnosing and predicting outcomes in aortic stenosis (AS). Multi-energy CT (MECT) allows virtual non-contrast (VNC) reconstructions from contrast scans. We aim to compare the VNC-AVC score to the true non-contrast (TNC)-AVC score for assessing AS severity. MethodsWe prospectively included patients undergoing a MECT for transcatheter aortic valve replacement (TAVR) planning. TNC-AVC was acquired before contrast, and VNC-AVC was derived from a retrospectively gated contrast-enhanced scan. The Agatston scoring method was used for quantification, and linear regression analysis to derive adjusted-VNC values. ResultsAmong 109 patients (55% female) included, 43% had concordant severe and 14% concordant moderate AS. TNC scan median dose-length product was 116 ​mGy∗cm. The median TNC-AVC was 2,107 AU (1,093–3,372), while VNC-AVC was 1,835 AU (1293-2,972) after applying the coefficient (1.46) and constant (743) terms. A strong correlation was demonstrated between methods (r ​= ​0.93; p ​< ​0.001). Using accepted thresholds (>1,300 AU for women and >2,000 AU for men), 65% (n ​= ​71) of patients had severe AS by TNC-AVC and 67% (n ​= ​73) by adjusted-VNC-AVC. After estimating thresholds for adjusted-VNC (>1,564 AU for women and >2,375 AU for men), 56% (n ​= ​61) had severe AS, demonstrating substantial agreement with TNC-AVC (κ ​= ​0.77). ConclusionsMECT-derived VNC-AVC showed a strong correlation with TNC-AVC. After adjustment, VNC-AVC demonstrated substantial agreement with TNC-AVC, potentially eliminating the requirement for an additional scan and enabling reductions in both radiation exposure and acquisition time.

OCT-angiography based artificial intelligence-inferred fluorescein angiography for leakage detection in retina [Invited

Optical coherence tomography angiography (OCTA) covers most functions of fluorescein angiography (FA) when imaging the retina but lacks the ability to depict vascular leakage. Based on OCTA, we developed artificial intelligence-inferred-FA (AI-FA) to delineate leakage in eyes with diabetic retinopathy (DR). Training data of 19,648 still FA images were prepared from FA-photo and videos of 43 DR eyes. AI-FA images were generated using a convolutional neural network. AI-FA images achieved a structural similarity index of 0.91 with corresponding real FA images in DR. The AI-FA generated from OCTA correctly depicted vascular occlusion and associated leakage with enough quality, enabling precise DR diagnosis and treatment planning. A combination of OCT, OCTA, and AI-FA yields more information than real FA with reduced acquisition time without risk of allergic reactions.

AliasNet: Alias artefact suppression network for accelerated phase-encode MRI

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application.

On-Machine CIS SoC-Based Layerwise Inspection System for MEX Additive Manufacturing

Additive manufacturing processes build three-dimensional objects usually following a layer-upon-layer strategy. An interesting feature of this strategy is that each layer could be inspected before the next one is deposited. On-machine integration of layerwise inspection systems would not only allow for early characterization of the dimensional and geometric quality of the part, but also for the detection of intralayer defects. Contact image sensors (CIS), such as those used in desktop flatbed scanners, could be used for this purpose since they would provide bi-dimensional digital images of the whole layer and its neighborhood. CIS images combine high resolutions with a reduced acquisition time. In this work, a material extrusion (MEX) additive manufacturing system, with layerwise inspection capabilities is proposed. The system has been equipped with the CIS that Epson uses in its Perfection V39 flatbed scanner. The sensor provides two analog output signals, each one consisting on 2584 voltage levels, that represent the amount of light reflected by the material. This analog information is sent to a parallel AD converter, where an 8-bit encoding is assigned to each one of the pixels on the digitized image. To overcome microcontroller-related problems, a Zynq®-7000 system-on-chip (SoC) has been used. This SoC integrates an ARM® based processor, with the hardware programming of a field programmable gate array (FPGA). This architecture ensures an accurate and controlled readout of the various AD converters. The resultant digital image of each layer could then be then processed using different algorithms to detect defects, extract the geometry of the layer contour and characterize the dimensional and geometric quality of the object. In the example provided, a forced error consisting on 0.2 mm height local deviations, caused by a variation in extrusion temperature, was identified from 2D grayscale images obtained with the CIS sensor.

Compressed sensing on displacement signals measured with optical coherence tomography.

Optical coherence tomography (OCT) is capable of angstrom-scale vibrometry of particular interest to researchers of auditory mechanics. We develop a method for compressed sensing vibrometry using OCT that significantly reduces acquisition time for dense motion maps. Our method, based on total generalized variation with uniform subsampling, can reduce the number of samples needed to measure motion maps by a factor of ten with less than 5% normalized mean square error when tested on a diverse set of in vivo measurements from the gerbil cochlea. This opens up the possibility for more complex in vivo experiments for cochlear mechanics.

NMR characterization of proton exchange membranes in controlled hygrometry conditions

NMR methods are highly sensitive to the state of water adsorbed in ionomer membranes for PEM fuel cells and electrolyzers. This study demonstrates the potential of NMR and MRI to reliably and accurately determine a wide range of water parameters such as proton chemical shift, transverse relaxation time, water content, and membrane thickness. Measurements are carried out in-situ in membranes in contact with humid air flows that are precisely controlled in terms of flow rate, temperature, and humidity. This precise management of the climatic conditions makes it possible to detect very small variations in the parameters measured, linked to the hydrothermal history of the membrane described in the literature. This history is erased when the membrane is humidity cycled several times. In addition, we demonstrate the use of this MRI imaging method to determine the resistance to water transfer across the membrane/humid air interface under steady-state conditions. Finally, we propose a refinement of the imaging method that reduces acquisition time by a factor of two, without degrading the 1D water content profile. This improvement gives us access to the study of transient hydration regimes typical of PEMFC and PEME operation.

Comparison between compressed sensing and segmented cine cardiac magnetic resonance: a meta-analysis

PurposeHighly accelerated compressed sensing cine has allowed for quantification of ventricular function in a single breath hold. However, compared to segmented breath hold techniques, there may be underestimation or overestimation of LV volumes. Furthermore, a heterogeneous sample of techniques have been used in volunteers and patients for pre-clinical and clinical use. This can complicate individual comparisons where small, but statistically significant differences exist in left ventricular morphological and/or functional parameters. This meta-analysis aims to provide a comparison of conventional cine versus compressed sensing based reconstruction techniques in patients and volunteers.MethodsTwo investigators performed systematic searches for eligible studies using PubMed/MEDLINE and Web of Science to identify studies published 1/1/2010-3/1/2021. Ultimately, 15 studies were included for comparison between compressed sensing cine and conventional imaging.ResultsCompared to conventional cine, there were small, statistically significant overestimation of LV mass, underestimation of stroke volume and LV end diastolic volume (mean difference 2.65 g [CL 0.57–4.73], 2.52 mL [CL 0.73–4.31], and 2.39 mL [CL 0.07–4.70], respectively). Attenuated differences persisted across studies using prospective gating (underestimated stroke volume) and non-prospective gating (underestimation of stroke volume, overestimation of mass). There were no significant differences in LV volumes or LV mass with high or low acceleration subgroups in reference to conventional cine except slight underestimation of ejection fraction among high acceleration studies. Reduction in breath hold acquisition time ranged from 33 to 64%, while reduction in total scan duration ranged from 43 to 97%.ConclusionLV volume and mass assessment using compressed sensing CMR is accurate compared to conventional parallel imaging cine.

Optimization of 3D dynamic speech MRI: Poisson-disc undersampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis.

To improve the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach. For data acquisition, we used a Poisson-disc random under sampling scheme that reduced the undersampling coherence. For image reconstruction, we proposed a novel locally higher-rank partial separability model. This reconstruction model represented the oral and static regions using separate low-rank subspaces, therefore, preserving their distinct temporal signal characteristics. Regional optimized temporal basis was determined from the regional-optimized virtual coil approach. Overall, we achieved a better spatiotemporal image reconstruction quality with the potential of reducing total acquisition time by 50%. The proposed method was demonstrated through several 2-mm isotropic, 64 mm total thickness, dynamic acquisitions with 40 frames per second and compared to the previous approach using a global subspace model along with other k-space sampling patterns. Individual timeframe images and temporal profiles of speech samples were shown to illustrate the ability of the Poisson-disc under sampling pattern in reducing total acquisition time. Temporal information of sagittal and coronal directions was also shown to illustrate the effectiveness of the locally higher-rank operator and regional optimized temporal basis. To compare the reconstruction qualities of different regions, voxel-wise temporal SNR analysis were performed. Poisson-disc sampling combined with a locally higher-rank model and a regional-optimized temporal basis can drastically improve the spatiotemporal image quality and provide a 50% reduction in overall acquisition time.

Improved Terahertz Time-Domain Spectroscopy via the Extended Kalman Filter

To implement terahertz time-domain spectroscopy (THz-TDS) systems that have short integration time and maintain performance, the extended Kalman filter (EKF) has been used. Performance of EKF with THz-TDS on vapor samples and solid samples is untested. Previous EKF methods were not adaptive for use on multiple THz-TDS systems. We provide an adaptive EKF method that can work on any THz setup, to improve maximum measurable absorption coefficient. Our adaptive EKF method responds to THz pulse properties. The adaptive EKF is exhaustively demonstrated for six different time-constant data sets spanning 1 ms to 300 ms for THz-TDS on vapor with electro-optic THz detection. The acquisition time saved is quantified and shown to significantly reduce acquisition time from 2.58 ms to 1 ms. This improvement of 2.58 times will have a significant effect to move spectral imaging from taking hours to taking minutes. Vapor absorption lines are maintained. We show the adaptive EKF maintain correct spectral information when compared to the high-resolution transmission molecular absorption (HITRAN) database. The adaptive EKF is also demonstrated for THz-TDS on vapor with photoconductive THz detection, thus demonstrating adaptability, and is applied for THz-TDS of a solid sample of polyethylene terephthalate glycol.

Evaluation of Compressed SENSE on Image Quality and Reduction of MRI Acquisition Time: A Clinical Validation Study

To evaluate the effect of compressed SENSE (CS)in clinical settings on scan time reduction and image quality. Ninety-five magnetic resonance imaging (MRI) scans from different anatomical regions were acquired, consisting of a standard protocol sequence (SS) and sequence accelerated with CS. Anonymized paired sequences were randomly displayed and rated by six blinded subspecialty radiologists. Side-by-side evaluation on perceived sharpness, perceived signal-to-noise-ratio (SNR), lesion conspicuity, and artifacts were compared and scored on a five-point Likert scale, and individual image quality was evaluated on a four-point Likert scale. CS reduced overall scan time by 32% while maintaining acceptable MRI quality for all regions. The largest time savings were seen in the spine (mean = 68 seconds, 44% reduction) followed by the brain (mean = 86seconds, 37% reduction). The sequence with maximum time savings was intracranial 3D-time-of-flight magnetic resonance angiography (202seconds, 56% reduction). CS was mildly inferior to SS on perceived sharpness, perceived SNR, and lesion conspicuity (mean scores = 2.32-2.96, P<.001 [1:SS superior; 3: equivalent; 5: CS superior]). CS was equivalent to SS for joint and body scans on overall image quality (CS = 3.02-3.37, SS = 3.04-3.68, P>.05, [1: lowest quality and 4: highest quality]). The overall image quality of CS was slightly less for brain and spine scans (mean CS = 2.79-3.05, mean SS = 3.13-3.43, P=.021) but still diagnostic. Good overall clinical acceptance for CS (88%) was noted with full clinical acceptance for body scans (100%)and high acceptance for other regions (68%-95%). CS significantly reduced MR acquisition time while maintaining acceptable image quality. The implementation of CS may improve departmental workflows and enhance patient care.