T1 Error Research Articles

Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on native T1 maps from cardiac MRI in both long-axis and short-axis orientations. Models were trained on native myocardial T1 maps from 50 healthy volunteers and 75 patients using manual segmentation as the reference standard. Based on a U-Net architecture, we systematically optimized the model design using two different training metrics (Sørensen-Dice coefficient = DSC and Intersection-over-Union = IOU), two different activation functions (ReLU and LeakyReLU) and various numbers of training epochs. Training with DSC metric and a ReLU activation function over 35 epochs achieved the highest overall performance (mean error in T1 10.6 ± 17.9 ms, mean DSC 0.88 ± 0.07). Limits of agreement between model results and ground truth were from -35.5 to + 36.1 ms. This was superior to the agreement between two human raters (-34.7 to + 59.1 ms). Segmentation was as accurate for long-axis views (mean error T1: 6.77 ± 8.3 ms, mean DSC: 0.89 ± 0.03) as for short-axis images (mean error ΔT1: 11.6 ± 19.7 ms, mean DSC: 0.88 ± 0.08). Fully automated segmentation and quantitative analysis of native myocardial T1 maps is possible in both long-axis and short-axis orientations with very high accuracy.

Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking.

To develop a novel low-rank tensor reconstruction approach leveraging the complete acquired data set to improve precision and repeatability of multiparametric mapping within the cardiovascular MR Multitasking framework. A novel approach that alternated between estimation of temporal components and spatial components using the entire data set acquired (i.e., including navigator data and imaging data) was developed to improve reconstruction. The precision and repeatability of the proposed approach were evaluated on numerical simulations, 10 healthy subjects, and 10 cardiomyopathy patients at multiple scan times for 2D myocardial T1/T2 mapping with MR Multitasking and were compared with those of the previous navigator-derived fixed-basis approach. In numerical simulations, the proposed approach outperformed the previous fixed-basis approach with lower T1 and T2 error against the ground truth at all scan times studied and showed better motion fidelity. In human subjects, the proposed approach showed no significantly different sharpness or T1/T2 measurement and significantly improved T1 precision by 20%-25%, T2 precision by 10%-15%, T1 repeatability by about 30%, and T2 repeatability by 25%-35% at 90-s and 50-s scan times The proposed approach at the 50-s scan time also showed comparable results with that of the previous fixed-basis approach at the 90-s scan time. The proposed approach improved precision and repeatability for quantitative imaging with MR Multitasking while maintaining comparable motion fidelity, T1/T2 measurement, and septum sharpness and had the potential for further reducing scan time from 90 s to 50 s.

Demonstrating a Long-Coherence Dual-Rail Erasure Qubit Using Tunable Transmons

Quantum error correction with erasure qubits promises significant advantages over standard error correction due to favorable thresholds for erasure errors. To realize this advantage in practice requires a qubit for which nearly all errors are such erasure errors, and the ability to check for erasure errors without dephasing the qubit. We demonstrate that a “dual-rail qubit” consisting of a pair of resonantly coupled transmons can form a highly coherent erasure qubit, where transmon T1 errors are converted into erasure errors and residual dephasing is strongly suppressed, leading to millisecond-scale coherence within the qubit subspace. We show that single-qubit gates are limited primarily by erasure errors, with erasure probability perasure=2.19(2)×10−3 per gate while the residual errors are ∼40 times lower. We further demonstrate midcircuit detection of erasure errors while introducing <0.1% dephasing error per check. Finally, we show that the suppression of transmon noise allows this dual-rail qubit to preserve high coherence over a broad tunable operating range, offering an improved capacity to avoid frequency collisions. This work establishes transmon-based dual-rail qubits as an attractive building block for hardware-efficient quantum error correction. Published by the American Physical Society 2024

Pilot tone-based prospective correction of respiratory motion for free-breathing myocardial T1 mapping

ObjectiveTo provide respiratory motion correction for free-breathing myocardial T1 mapping using a pilot tone (PT) and a continuous golden-angle radial acquisition.Materials and methodsDuring a 45 s prescan the PT is acquired together with a dynamic sagittal image covering multiple respiratory cycles. From these images, the respiratory heart motion in head-feet and anterior–posterior direction is estimated and two linear models are derived between the PT and heart motion. In the following scan through-plane motion is corrected prospectively with slice tracking based on the PT. In-plane motion is corrected for retrospectively. Our method was evaluated on a motion phantom and 11 healthy subjects.ResultsNon-motion corrected measurements using a moving phantom showed T1 errors of 14 ± 4% (p < 0.05) compared to a reference measurement. The proposed motion correction approach reduced this error to 3 ± 4% (p < 0.05). In vivo the respiratory motion led to an overestimation of T1 values by 26 ± 31% compared to breathhold T1 maps, which was successfully corrected to an average difference of 3 ± 2% (p < 0.05) between our free-breathing approach and breathhold data.DiscussionOur proposed PT-based motion correction approach allows for T1 mapping during free-breathing with the same accuracy as a corresponding breathhold T1 mapping scan.

A relaxometry method that emphasizes practicality and availability.

Although many methods have been proposed to quantitatively map the main MRI parameters (e.g., T1 , T2 , C × M0 ), these methods often involve special sequences not readily available on clinical scanners and/or may require long scan times. In contrast, the proposed method can readily run on most scanners, offer flexible tradeoffs between scan time and image quality, and map MRI parameters jointly to ensure spatial alignment. The approach is based on the multi-shot spin-echo (SE) EPI sequence. The corresponding signal equation was derived and strategies for solving it were developed. As usual with multi-shot EPI, scan time can readily be traded-off against image quality by adjusting the echo train length. Validation was performed against reference relaxometry methods, in gel phantoms with varying concentrations of gadobutrol and gadoterate meglumine contrast agents. In vivo examples are further presented, from 3 neuroradiology patients. Bland-Altman analysis was performed: for T2 , as compared to 2D SE, bias was 0.29 ms and the 95% limits of agreement ranged from -1.15 to +1.73 ms. For T1 , compared to inversion-recovery SE (and MOLLI), bias was -20.2ms (and -14.5ms) and the limits of agreement ranged from -62.4 to +22.0ms (and -53.8 to +24.9ms). The mean relative T1 error between the proposed method and each of the 2 reference methods was similar to that of the reference methods among themselves. In the constellation of existing relaxometry methods, the proposed method is meant to stand out in terms of its practicality and availability.

Low-rank inversion reconstruction for through-plane accelerated radial MR fingerprinting applied to relaxometry at 0.35 T.

PurposeTo reduce scan time, methods to accelerate phase‐encoded/non‐Cartesian MR fingerprinting (MRF) acquisitions for variable density spiral acquisitions have recently been developed. These methods are not applicable to MRF acquisitions, wherein a single k‐space spoke is acquired per frame. Therefore, we propose a low‐rank inversion method to resolve MRF contrast dynamics from through‐plane accelerated Cartesian/radial measurements applied to quantitative relaxation‐time mapping on a 0.35T system.MethodsAn algorithm was implemented to reconstruct through‐plane aliased low‐rank images describing the contrast dynamics occurring because of the transient‐state MRF acquisition. T1 and T2 times from accelerated acquisitions were compared with those from unaccelerated linear reconstructions in a standardized system phantom and within in vivo brain and prostate experiments on a hybrid 0.35T MRI/linear accelerator.ResultsNo significant differences between T1 and T2 times for the accelerated reconstructions were observed compared to fully sampled acquisitions (p = 0.41 and p = 0.36, respectively). The mean absolute errors in T1 and T2 were 5.6% and 2.9%, respectively, between the full and accelerated acquisitions. The SDs in T1 and T2 decreased with the advanced accelerated reconstruction compared with the unaccelerated reconstruction (p = 0.02 and p = 0.03, respectively). The quality of the T1 and T2 maps generated with the proposed approach are comparable to those obtained using the unaccelerated data sets.ConclusionsThrough‐plane accelerated MRF with radial k‐space coverage was demonstrated at a low field strength of 0.35 T. This method enabled 3D T1 and T2 mapping at 0.35 T with a 3‐min scan.

Improved accuracy and precision with three-parameter simultaneous myocardial T1 and T2 mapping using multiparametric SASHA.

To develop and validate a three-parameter model for improved precision multiparametric SAturation-recovery single-SHot Acquisition (mSASHA) cardiac T1 and T2 mapping with high accuracy in a single breath-hold. The mSASHA acquisition consists of nine images of variable saturation recovery and T2 preparation in 11 heartbeats with T1 and T2 values calculated using a three-parameter model. It was validated in simulations and phantoms at 3 T with comparison to a four-parameter joint T1 -T2 technique. The mSASHA acquisition was compared with MOLLI, SASHA, and T2 -prepared balanced SSFP in 10 volunteers. The mSASHA technique had high accuracy in phantoms compared to spin echo, with -0.2 ± 0.3% T1 error and -2.4 ± 1.3% T2 error. The mSASHA coefficient of variation in phantoms for T1 was similar to MOLLI (0.7 ± 0.2% for both) and T2 -prepared balanced SSFP for T2 (1.3 ± 0.7% vs 1.4 ± 0.3%, adjusted p > .05 for both). In simulations, three-parameter mSASHA had higher precision than four-parameter joint T1 -T2 for both T1 and T2 (46% and 11% reductions in T1 and T2 interquartile range for native myocardium). In vivo myocardial mSASHA T1 was similar to SASHA (1523 ± 18 ms vs 1520 ± 18 ms) with similar coefficient of variation to both MOLLI and SASHA (3.3 ± 0.6% vs 3.1 ± 0.6% and 3.3 ± 0.5% respectively, adjusted p > .05 for all). Myocardial mSASHA T2 was 37.1 ± 1.1 ms with similar precision to T2 -prepared balanced SSFP (6.7 ± 1.7% vs 6.0 ± 1.6%, adjusted p > .05). Three-parameter mSASHA provides high-accuracy cardiac T1 and T2 quantification in a single breath-hold with similar precision to MOLLI and T2 -prepared balanced SSFP. Further study is required to both establish normative values and demonstrate clinical utility in patient populations.

Simultaneous 3D acquisition of 1 H MRF and 23 Na MRI.

To develop a 3D MR technique to simultaneously acquire proton multiparametric maps (T1 , T2 , and proton density) and sodium density weighted images over the whole brain. We implemented a 3D stack-of-stars MR pulse sequence which consists of interleaved proton (1 H) and sodium (23 Na) excitations, tailored slice encoding gradients that can encode the same slice for both nuclei, and simultaneous readout with different radial trajectories (1 H, full-radial; 23 Na, center-out radial). The receive chain of our 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. A heuristically optimized flip angle train was implemented for proton MR fingerprinting (MRF). The SNR and the accuracy of proton T1 and T2 were evaluated in phantoms. Finally, in vivo application of the method was demonstrated in five healthy subjects. The SNR for the simultaneous measurement was almost identical to that for the single-nucleus measurements (<2% change). The proton T1 and T2 maps remained similar to the results from a reference 2D MRF technique (normalized RMS error in T1 ≈ 4.2% and T2 ≈ 11.3%). Measurements in healthy subjects corroborated these results and demonstrated the feasibility of our method for in vivo application. The in vivo T1 values measured using our method were lower than the results measured by other conventional techniques. With the 3D simultaneous implementation, we were able to acquire sodium and proton density weighted images in addition to proton T1 , T2 , and from 1 H MRF that covers the whole brain volume within 21 min.

Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy.

The acquisition of multiparametric quantitative magnetic resonance imaging (qMRI) is becoming increasingly important for functional characterization of cancer prior to- and throughout the course of radiation therapy. The feasibility of a qMRI method known as magnetic resonance fingerprinting (MRF) for rapid T1 and T2 mapping was assessed on a low-field MR-linac system. A three-dimensional MRF sequence was implemented on a 0.35T MR-guided radiotherapy system. MRF-derived measurements of T1 and T2 were compared to those obtained with gold standard single spin echo methods, and the impacts of the radiofrequency field homogeneity and scan times ranging between 6 and 48 min were analyzed by acquiring between 1 and 8 spokes per time point in a standard quantitative system phantom. The short-term repeatability of MRF was assessed over three measurements taken over a 10-h period. To evaluate transferability, MRF measurements were acquired on two additional MR-guided radiotherapy systems. Preliminary human volunteer studies were performed. The phantom benchmarking studies showed that MRF is capable of mapping T1 and T2 values within 8% and 10% of gold standard measures, respectively, at 0.35T. The coefficient of variation of T1 and T2 estimates over three repeated scans was<5% over a broad range of relaxation times. The T1 and T2 times derived using a single-spoke MRF acquisition across three scanners were near unity and mean percent errors in T1 and T2 estimates using the same phantom were<3%. The mean percent differences in T1 and T2 as a result of truncating the scan time to 6 min over the large range of relaxation times in the system phantom were 0.65% and 4.05%, respectively. The technical feasibility and accuracy of MRF on a low-field MR-guided radiation therapy device has been demonstrated. MRF can be used to measure accurate T1 and T2 maps in three dimensions from a brief 6-min scan, offering strong potential for efficient and reproducible qMRI for future clinical trials in functional plan adaptation and tumor/normal tissue response assessment.

Application of the inverted classroom model in the teaching module "new classification of periodontal and peri-implant diseases and conditions" during the COVID-19 pandemic.

Background: Due to the need for patient-free dental education during the COVID-19 pandemic, Hannover Medical School (MHH) implemented a new periodontology module. Its didactic structure was based on the “inverted classroom model” (ICM) in combination with elements of case-based learning. The educational objective was to increase the diagnostic confidence of dental students in the classification of periodontal patients (staging & grading), based on 33 digitized patient cases. To assess the suitability of the module for future dental curricula, this study aimed to evaluate student satisfaction and skills acquisition. Methods: The periodontology module, which was attended by final year dental students of MHH (n=55, mean age: 26.5±3.9 years, male/female ratio: 24.1%/75.9%) was evaluated in a two-tiered way. Student satisfaction was recorded using a questionnaire. Learning success was assessed by comparing error rates in patient case classifications before (T0) and after (T1) participation in the periodontology module.Results: The study found a high level of student satisfaction with the ICM format and a significant reduction in error rates (T0 error rate=28.3%; MV±SD=3.12±1.67 vs. T1 error rate=18.7%; MV±SD=2.06±1.81; Δ=9.6%). However, of the 11 diagnostic decisions required, only four parameters (extent, grading, percentage of bone loss per age, phenotype) showed significant improvements, with effect sizes ranging from small to medium.Conclusions: The ICM-based teaching concept is definitely not an alternative to patient-based learning. However, in regard to student satisfaction and learning success, it might be superior to conventional classroom-based lectures, especially when complex topics are covered. In summary, the newly developed periodontology module may be a useful addition to traditional dental education in future curricula, even for the time after the COVID-19 pandemic.

Streamlined magnetic resonance fingerprinting: Fast whole-brain coverage with deep-learning based parameter estimation

Magnetic resonance fingerprinting (MRF) is a quantitative MRI (qMRI) framework that provides simultaneous estimates of multiple relaxation parameters as well as metrics of field inhomogeneity in a single acquisition. However, current challenges exist in the forms of (1) scan time; (2) need for custom image reconstruction; (3) large dictionary sizes; (4) long dictionary-matching time. This study aims to introduce a novel streamlined magnetic-resonance fingerprinting (sMRF) framework based on a single-shot echo-planar imaging (EPI) sequence to simultaneously estimate tissue T1, T2, and T2* with integrated B1+ correction. Encouraged by recent work on EPI-based MRF, we developed a method that combines spin-echo EPI with gradient-echo EPI to achieve T2 in addition to T1 and T2* quantification. To this design, we add simultaneous multi-slice (SMS) acceleration to enable full-brain coverage in a few minutes. Moreover, in the parameter-estimation step, we use deep learning to train a deep neural network (DNN) to accelerate the estimation process by orders of magnitude. Notably, due to the high image quality of the EPI scans, the training process can rely simply on Bloch-simulated data. The DNN also removes the need for storing large dictionaries. Phantom scans along with in-vivo multi-slice scans from seven healthy volunteers were acquired with resolutions of 1.1×1.1×3 mm3 and 1.7×1.7×3 mm3, and the results were validated against ground truth measurements. Excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches. In the phantom scan, a strong linear relationship (R = 1–1.04, R2>0.96) was found for all parameter estimates, with a particularly high agreement for T2 estimation (R2>0.99). Similar findings are reported for the in-vivo human data for all of our parameter estimates. Incorporation of DNN results in a reduction of parameter estimation time on the order of 1000 x and a reduction in storage requirements on the order of 2500 x while achieving highly similar results as conventional dictionary matching (%differences of 7.4 ± 0.4%, 3.6 ± 0.3% and 6.0 ± 0.4% error in T1, T2, and T2* estimation). Thus, sMRF has the potential to be the method of choice for future MRF studies by providing ease of implementation, fast whole-brain coverage, and ultra-fast T1/T2/T2* estimation.

Adaptive rescheduling of error monitoring in multitasking

The concurrent execution of temporally overlapping tasks leads to considerable interference between the subtasks. This also impairs control processes associated with the detection of performance errors. In the present study, we investigated how the human brain adapts to this interference between task representations in such multitasking scenarios. In Experiment 1, participants worked on a dual-tasking paradigm with partially overlapping execution of two tasks (T1 and T2), while we recorded error-related scalp potentials. The error positivity (Pe), a correlate of higher-level error evaluation, was reduced after T1 errors but occurred after a correct T2-response instead. MVPA-based and regression-based single-trial analysis revealed that the immediate Pe and deferred Pe are negatively correlated, suggesting a trial-wise trade-off between immediate and postponed error processing. Experiment 2 confirmed this finding and additionally showed that this result is not due to credit-assignment errors in which a T1 error is falsely attributed to T2. For the first time reporting a Pe that is temporally detached from its eliciting error event by a considerable amount of time, this study illustrates how reliable error detection in dual-tasking is maintained by a mechanism that adaptively schedules error processing, thus demonstrating a remarkable flexibility of the human brain when adapting to multitasking situations.

Measurement of T1 Mapping in Patients With Cardiac Devices: Off-Resonance Error Extends Beyond Visual Artifact but Can Be Quantified and Corrected.

Background: Measurement of myocardial T1 is increasingly incorporated into standard cardiovascular magnetic resonance (CMR) protocols, however accuracy may be reduced in patients with metallic cardiovascular implants. Measurement is feasible in segments free from visual artifact, but there may still be off-resonance induced error.Aim: To quantify off-resonance induced T1 error in patients with metallic cardiovascular implants, and validate a method for error correction for a conventional MOLLI pulse sequence.Methods: Twenty-four patients with cardiac implantable electronic devices (CIEDs: 46% permanent pacemakers, PPMs; 33% implantable loop recorders, ILRs; and 21% implantable cardioverter-defibrillators, ICDs); and 31 patients with aortic valve replacement (AVR) (45% metallic) were studied. Paired mid-myocardial short-axis MOLLI and single breath-hold off-resonance field maps were acquired at 1.5 T. T1 values were measured by AHA segment, and segments with visual artifact were excluded. T1 correction was applied using a published relationship between off-resonance and T1. The accuracy of the correction was assessed in 10 healthy volunteers by measuring T1 before and after external placement of an ICD generator next to the chest to generate off-resonance.Results: T1 values in healthy volunteers with an ICD were underestimated compared to without (967 ± 52 vs. 997 ± 26 ms respectively, p = 0.0001), but were similar after correction (p = 0.57, residual difference 2 ± 27 ms). Artifact was visible in 4 ± 12, 42 ± 31, and 53 ± 27% of AHA segments in patients with ILRs, PPMs, and ICDs, respectively. In segments without artifact, T1 was underestimated by 63 ms (interquartile range: 7–143) per patient. The greatest error for patients with ILRs, PPMs and ICDs were 79, 146, and 191 ms, respectively. The presence of an AVR did not generate T1 error.Conclusion: Even when there is no visual artifact, there is error in T1 in patients with CIEDs, but not AVRs. Off-resonance field map acquisition can detect error in measured T1, and a correction can be applied to quantify T1 MOLLI accurately.

Simultaneous proton density fat-fraction and imaging with water-specific T1 mapping (PROFIT1 ): application in liver.

To describe and validate a simultaneous proton density fat-fraction (PDFF) imaging and water-specific T1 mapping (T1(Water) ) approach for the liver (PROFIT1 ) with mapping and low sensitivity to calibration or inhomogeneity. A multiecho gradient-echo sequence, with and without saturation preparation, was designed for simultaneous imaging of liver PDFF, , and T1(Water) (three slices in ~13seconds). Chemical-shift-encoded MRI processing yielded fat-water separated images and maps. T1(Water) calculation utilized saturation and nonsaturation-recovery water-separated images. Several variable flip angle schemes across k-space (increasing flip angles in sequential RF pulses) were evaluated for minimization of T1 weighting, to reduce the dependence of T1(Water) and PDFF (reduced flip angle dependence). T1(Water) accuracy was validated in mixed fat-water phantoms, with various PDFF and T1 values (3T). In vivo application was illustrated in five volunteers and five patients with nonalcoholic fatty liver disease (PDFF, T1(Water) , ). A sin3 (θ) flip angle pattern (0<θ<π/2 over k-space) yielded the largest PROFIT1 signal yield with negligible dependence for both T1(Water) and PDFF. Mixed fat-water phantom experiments illustrated excellent agreement between PROFIT1 and gold-standard spectroscopic evaluation of PDFF and T1(Water) (<1% T1 error). In vivo PDFF, T1(Water) , and maps illustrated independence of the PROFIT1 values from inhomogeneity and significant differences between volunteers and patients with nonalcoholic fatty liver disease for T1(Water) (927±56ms vs. 1033±23ms; P<.05) and PDFF (2.0%±0.8% vs. 13.4%±5.0%, P<.05). was similar between groups. The PROFIT1 pulse sequence provides fast simultaneous quantification of PDFF, T1(Water) , and with minimal sensitivity to miscalibration or inhomogeneity.

Accelerated MP2RAGE imaging using Cartesian phyllotaxis readout and compressed sensing reconstruction.

MP2RAGE T1 -weighted imaging has been shown to be beneficial for various applications, mainly because of its good grey-white matter contrast, its B1 -robustness and ability to derive T1 maps. Even using parallel imaging, the method requires long acquisition times, especially at high resolution. This work aims at accelerating MP2RAGE imaging using compressed sensing. A pseudo-phyllotactic Cartesian MP2RAGE readout was implemented allowing for flexible reordering and undersampling factors. The sampling pattern was first optimized based on fully sampled data and a compressed sensing reconstruction. Changes in contrast ratios, automated brain segmentation results, and quantitative T1 values were used for benchmarking. In vivo undersampled data from eleven healthy subjects were then acquired using a 4-fold acceleration with the optimized sampling pattern. The resulting images were compared to the standard parallel imaging MP2RAGE protocol by visual inspection and using the above quality metrics. The application of incoherent undersampling and iterative compressed sensing reconstruction on MP2RAGE acquisitions allows for a 57% time reduction (corresponding to 4-fold undersampling with maintained reference lines, TA = 3:35 minutes) compared to the reference protocol using parallel imaging (GRAPPAx3 acceleration, TA = 8:22 minutes) while obtaining images with similar image quality, morphometric (volume differences = [0.07 ± 1.2-3.8 ± 1.9]%) and T1 -mapping outcomes (T1 error = [6 ± 5.1-37 ± 12.3] ms depending on the different structures). A whole-brain MP2RAGE acquisition is feasible with compressed sensing in less than 4 minutes without appreciably compromising image quality.

FASt single-breathhold 2D multislice myocardial T1 mapping (FAST1) at 1.5T for full left ventricular coverage in three breathholds.

BackgroundConventional myocardial T1 mapping techniques such as modified Look–Locker inversion recovery (MOLLI) generate one T1 map per breathhold. T1 mapping with full left ventricular coverage may be desirable when spatial T1 variations are expected. This would require multiple breathholds, increasing patient discomfort and prolonging scan time.PurposeTo develop and characterize a novel FASt single‐breathhold 2D multislice myocardial T1 mapping (FAST1) technique for full left ventricular coverage.Study TypeProspective.Population/PhantomNumerical simulation, agarose/NiCl2 phantom, 9 healthy volunteers, and 17 patients.Field Strength/Sequence1.5T/FAST1.AssessmentTwo FAST1 approaches, FAST1‐BS and FAST1‐IR, were characterized and compared with standard 5‐(3)‐3 MOLLI in terms of accuracy, precision/spatial variability, and repeatability.Statistical TestsKruskal‐Wallis, Wilcoxon signed rank tests, intraclass correlation coefficient analysis, analysis of variance, Student's t‐tests, Pearson correlation analysis, and Bland–Altman analysis.ResultsIn simulation/phantom, FAST1‐BS, FAST1‐IR, and MOLLI had an accuracy (expressed as T1 error) of 0.2%/4%, 6%/9%, and 4%/7%, respectively, while FAST1‐BS and FAST1‐IR had a precision penalty of 1.7/1.5 and 1.5/1.4 in comparison with MOLLI, respectively. In healthy volunteers, FAST1‐BS/FAST1‐IR/MOLLI led to different native myocardial T1 times (1016 ± 27 msec/952 ±22 msec/987 ± 23 msec, P < 0.0001) and spatial variability (66 ± 10 msec/57 ± 8 msec/46 ± 7 msec, P < 0.001). There were no statistically significant differences between all techniques for T1 repeatability (P = 0.18). In vivo native and postcontrast myocardial T1 times in both healthy volunteers and patients using FAST1‐BS/FAST1‐IR were highly correlated with MOLLI (Pearson correlation coefficient ≥0.93).Data ConclusionFAST1 enables myocardial T1 mapping with full left ventricular coverage in three separated breathholds. In comparison with MOLLI, FAST1 yield a 5‐fold increase of spatial coverage, limited penalty of T1 precision/spatial variability, no significant difference of T1 repeatability, and highly correlated T1 times. FAST1‐IR provides improved T1 precision/spatial variability but reduced accuracy when compared with FAST1‐BS. Level of Evidence: 1 Technical Efficacy: Stage 3J. Magn. Reson. Imaging 2020;51:492–504.

Assessing effects of scanner upgrades for clinical studies.

Scanner upgrades due to software and hardware changes are an inevitable part of MR research and, without quality assurance protocols, can jeopardize studies. To evaluate changes in T1 relaxation time by inversion recovery (IR) and variable flip angle (VFA) measurements on a 3T system that underwent an "everything but the magnet" upgrade. Longitudinal. An International Society of Magnetic Resonance in Medicine / National Institute of Standards and Technology (ISMRM/NIST) system phantom with repeated measurements across multiple (n = 3) days. T1 IR, VFA at 3T. The T1 measurements by IR and VFA were compared with the nuclear magnetic resonance (NMR) measurements, which constitute the known values for the ISMRM/NIST system phantom, to determine the measurement error. Descriptive. The T1 VFA measurement errors were distributed around zero (-15% to +10%) on the original system and then the errors were distributed entirely above zero post-upgrade (+5% to 30%). The T1 IR results had a dramatic increase in error distribution (±5% original, ±20% post-upgrade) prior to the identification of signal saturation as an issue. Once the signal saturation was accounted for, the T1 IR errors decreased to ±10% post-upgrade. The T1 VFA measurement differences between the original and post-upgrade systems can be entirely attributed to contributions from B1 . The T1 IR measurements demonstrate the need for quantitative quality assurance (QA) processes. The site under study passed the QA protocols in place, which did not identify the increased T1 error, nor the signal saturation issue. To improve on this study, we would make systematic, quantitative measurements at intervals less than a year and following any hardware or software upgrade. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1948-1954.

The Attentional Blink is Related to the Microsaccade Rate Signature.

The reduced detectability of a target T2 following discrimination of a preceding target T1 in the attentional blink (AB) paradigm is classically interpreted as a consequence of reduced attention to T2 due to attentional allocation to T1. Here, we investigated whether AB was related to changes in microsaccade rate (MSR). We found a pronounced MSR signature following T1 onset, characterized by MSR suppression from 200 to 328 ms and enhancement from 380 to 568 ms. Across participants, the magnitude of the MSR suppression correlated with the AB effect such that low T2 detectability corresponded to reduced MSR. However, in the same task, T1 error trials coincided with the presence of microsaccades. We discuss this apparent paradox in terms of known neurophysiological correlates of MS whereby cortical excitability is suppressed both during the microsaccade and MSR suppression, in accordance to poor T1 performance with microsaccade occurrence and poor T2 performance with microsaccade absence. Our data suggest a novel low-level mechanism contributing to AB characterized by reduced MSR, thought to cause suppressed visual cortex excitability. This opens the question of whether attention mediates T2 performance suppression independently from MSR, and if not, how attention interacts with MSR to produce the T2 performance suppression.

T1 and extracellular volume fraction mapping in cardiac magnetic resonance: estimation of accuracy and precision of a novel algorithm

Magnetic resonance imaging is effective for non-invasive detection of myocardial diseases by extracellular volume fraction (ECV) estimation. A new methodology for T1 and ECV mapping is tested in this work, comparing results with other well-consolidated methods. The associated level of uncertainty for data was also estimated, to assess the reliability of the technique. A phantom with known T1 values was used for reference, and 60 subjects (40 controls, 20 diseased patients) were examined, using the modified look-locker inversion-recovery (MOLLI) method. Obtained T1 data were studied in terms of accuracy (difference with reference T1), paired comparisons with other methods, and Gamma-tool analysis with tolerances criteria of 4.21 mm for distance-to-agreement, and between 2%–5% for T1 difference. Accuracy and precision of the T1 mapping was analysed by phantom measurements, and the uncertainty of the ECV was estimated by T1 error propagation. Differences (in paired comparisons) of T1 datasets were not significant neither for statistical tests, nor for Bland–Altman analysis. T1 accuracy was between −12% and −1% across methods, slightly better for the tested method (mean accuracy in the T1 range of interest better than 2%). The Gamma analysis confirm slightly better results for the tested method than other methodologies. The new method improves the computational efficiency by a factor of 25 (at least), revealing to be also more suitable for Big Data-related applications.

Parameter map error due to normal noise and aliasing artifacts in MR fingerprinting.

To introduce a quantitative tool that enables rapid forecasting of T1 and T2 parameter map errors due to normal and aliasing noise as a function of the MR fingerprinting (MRF) sequence, which can be used in sequence optimization. The variances of normal noise and aliasing artifacts in the collected signal are related to the variances in T1 and T2 maps through derived quality factors. This analytical result is tested against the results of a Monte-Carlo approach for analyzing MRF sequence encoding capability in the presence of aliasing noise, and verified with phantom experiments at 3T. To further show the utility of our approach, our quality factors are used to find efficient MRF sequences for fewer repetitions. Experimental results verify the ability of our quality factors to rapidly assess the efficiency of an MRF sequence in the presence of both normal and aliasing noise. Quality factor assessment of MRF sequences is in agreement with the results of a Monte-Carlo approach. Analysis of MRF parameter map errors from phantom experiments is consistent with the derived quality factors, with T1 (T2 ) data yielding goodness of fit R2 ≥ 0.92 (0.80). In phantom and in vivo experiments, the efficient pulse sequence, determined through quality factor maximization, led to comparable or improved accuracy and precision relative to a longer sequence, demonstrating quality factor utility in MRF sequence design. The here introduced quality factor framework allows for rapid analysis and optimization of MRF sequence design through T1 and T2 error forecasting.