Echo Images Research Articles

Balanced-force shim system for correcting magnetic-field inhomogeneities in the heart due to implanted cardioverter defibrillators.

In the US, 1.4 million people have implanted ICDs for reducing the risk of sudden death due to ventricular arrhythmias. Cardiac MRI (cMR) is of particular interest in the ICD patient population as cMR is the optimal imaging modality for distinguishing cardiac conditions that predispose to sudden death, and it is the best method to plan and guide therapy. However, all ICDs contain a ferromagnetic transformer which imposes a large inhomogeneous magnetic field in sections of the heart, creating large image voids that can mask important pathology. A shim system was devised to resolve these ICD issues. A shim coil system (CSS) that corrects ICD artifacts over a user-selected Region-of-Interest (ROI), was constructed and validated. A shim coil was constructed that can project a large magnetic field for distances of ~15 cm. The shim-coil can be positioned safely anywhere within the scanner bore. The CSS includes a cantilevered beam to hold the shim coil. Remotely controlled MR-conditional motors allow 2 mm-accuracy three-dimensional shim-coil position. The shim coil is located above the subjects and the imaging surface-coils. Interaction of the shim coil with the scanner's gradients was eliminated with an amplifier that is in a constant current mode. Coupling with the scanners' radio-frequency (rf) coils, was reduced with shielding, low-pass filters, and cable shield traps. Software, which utilizes magnetic field (B0) mapping of the ICD inhomogeneity, computes the optimal location for the shim coil and its corrective current. ECG gated single- and multiple-cardiac-phase 2D GRE and SSFP sequences, as well as 3D ECG-gated respiratory-navigated IR-GRE (LGE) sequences were tested in phantoms and N = 3 swine with overlaid ICDs. With all cMR sequences, the system reduced artifacts from >100 ppm to <25 ppm inhomogeneity, which permitted imaging of the entire left ventricle in swine with ICD-related voids. Continuously acquired Gradient recalled echo or Steady State Free Precession images were used to interactively adjust the shim current and coil location. The shim system reduced large field inhomogeneities due to implanted ICDs and corrected most ICD-related image distortions. Externally-controlled motorized translation of the shim coil simplified its utilization, supporting an efficient cardiac MRI workflow.

Myelin water quantification in multiple sclerosis using short repetition time adiabatic inversion recovery prepared-fast spin echo (STAIR-FSE) imaging.

Myelin water imaging (MWI) is a myelin-specific technique, which has great potential for the assessment of demyelination and remyelination. This study develops a new MWI method, which employs a short repetition time adiabatic inversion recovery (STAIR) technique in combination with a commonly used fast spin echo (FSE) sequence and provides quantification of myelin water (MW) fractions. Whole-brain MWI was performed using the short repetition time adiabatic inversion recovery prepared-fast spin echo (STAIR-FSE) technique on eight healthy volunteers (mean age: 38±14 years, four-males) and seven patients with multiple sclerosis (MS) (mean age: 53.7±8.7 years, two-males) on a 3T clinical magnetic resonance imaging scanner. To facilitate the quantification of apparent myelin water fraction (aMWF), a proton density-weighted FSE was also used during the scans to allow total water imaging. The aMWF measurements of MS lesions and normal-appearing white matter (NAWM) regions in MS patients were compared with those measured in normal white matter (NWM) regions in healthy volunteers. Both the analysis of variance (ANOVA) test and paired comparison were performed for the comparison. The MW in the whole-brain was selectively imaged and quantified using the STAIR-FSE technique in all participants. MS lesions showed much lower signal intensities than NAWM in the STAIR-FSE images. ANOVA analysis revealed a significant difference in the aMWF measurements between the three groups. Moreover, the aMWF measurements in MS lesions were significantly lower than those in both NWM of healthy volunteers and NAWM of MS patients. Lower aMWF measurements in NAWM were also found in comparison with those in NWM. The STAIR-FSE technique is capable of measuring aMWF values for the indirect detection of myelin loss in MS, thus facilitating clinical translation of whole brain MWI and quantification, which show great potential for the detection and evaluation of changes in myelin in the brain of patients with MS for future larger cohort studies.

Feasibility of deep learning-reconstructed thin-slice single-breath-hold HASTE for detecting pancreatic lesions: A comparison with two conventional T2-weighted imaging sequences

ObjectiveThe objective of this study was to evaluate the clinical feasibility of deep learning reconstruction-accelerated thin-slice single-breath-hold half-Fourier single-shot turbo spin echo imaging (HASTEDL) for detecting pancreatic lesions, in comparison with two conventional T2-weighted imaging sequences: compressed-sensing HASTE (HASTECS) and BLADE. MethodsFrom March 2022 to January 2023, a total of 63 patients with suspected pancreatic-related disease underwent the HASTEDL, HASTECS, and BLADE sequences were enrolled in this retrospectively study. The acquisition time, the pancreatic lesion conspicuity (LCP), respiratory motion artifact (RMA), main pancreatic duct conspicuity (MPDC), overall image quality (OIQ), signal-to-noise ratio (SNR), and contrast-noise-ratio (CNR) of the pancreatic lesions were compared among the three sequences by two readers. ResultsThe acquisition time of both HASTEDL and HASTECS was 16 s, which was significantly shorter than that of 102 s for BLADE. In terms of qualitative parameters, Reader 1 and Reader 2 assigned significantly higher scores to the LCP, RMA, MPDC, and OIQ for HASTEDL compared to HASTECS and BLADE sequences; As for the quantitative parameters, the SNR values of the pancreatic head, body, tail, and lesions, the CNR of the pancreatic lesion measured by the two readers were also significantly higher for HASTEDL than for HASTECS and BLADE sequences. ConclusionsCompared to conventional T2WI sequences (HASTECS and BLADE), deep-learning reconstructed HASTE enables thin slice and single-breath-hold acquisition with clinical acceptable image quality for detection of pancreatic lesions.

Semi-Supervised Gan for medical image segmentation

Echocardiography is a popular ultrasound imaging method used for the diagnosis of heart conditions. With the advent of numerous image processing algorithms, echocardiographic image segmentation has become more significant. This is a crucial stage since it offers a framework for evaluating numerous cardiac parameters, including LV volume and heart wall, valve motion, ejection fraction, thickness, etc. All these factors are crucial for determining a heart's health. The task of manual segmentation requires skilled operators and takes a lot of time. By requiring the discriminator network to output class labels, we extend Generative Adversarial Networks to the semi-supervised type. This paper examines image segmentation techniques for echocardiography to find the borders of the left ventricle. In this paper, we introduce a new convolution neural network model for the auto-segmentation of the left ventricle in echo images. The division of a picture into regions is known as image segmentation. Segments, that computer vision can use to automatically understand. This method makes it easier to simultaneously evaluate and diagnose echo pictures. The segmentation of echocardiographic images can be utilized to measure cardiac characteristics like heart wall thickness.

Model-based reconstruction for looping-star MRI.

The aim of this study was to develop a reconstruction method that more fully models the signals and reconstructs gradient echo (GRE) images without sacrificing the signal to noise ratio and spatial resolution, compared to conventional gridding and model-based image reconstruction method. By modeling the trajectories for every spoke and simplifying the scenario to only echo-in and echo-out mixture, the approach explicitly models the overlapping echoes. After modeling the overlapping echoes with two system matrices, we use the conjugate gradient algorithm (CG-SENSE) with the nonuniform FFT (NUFFT) to optimize the image reconstruction cost function. The proposed method is demonstrated in phantoms and in-vivo volunteer experiments for three-dimensional, high-resolution T2*-weighted imaging and functional MRI tasks. Compared to the gridding method, the high resolution protocol exhibits improved spatial resolution and reduced signal loss as a result of less intra-voxel dephasing. The fMRI task shows that the proposed model-based method produced images with reduced artifacts and blurring as well as more stable and prominent time courses. The proposed model-based reconstruction results shows improved spatial resolution and reduced artifacts. The fMRI task shows improved time series and activation map due to the reduced overlapping echoes and under-sampling artifacts.

Dynamic Imaging of Blood Coagulation Within the Hematoma of Patients With Acute Hemorrhagic Stroke.

The dynamics of blood clot (combination of Hb [hemoglobin], fibrin, and a higher concentration of aggregated red blood cells) formation within the hematoma of an intracerebral hemorrhage is not well understood. A quantitative neuroimaging method of localized coagulated blood volume/distribution within the hematoma might improve clinical decision-making. The deoxyhemoglobin of aggregated red blood cells within extravasated blood exhibits a higher magnetic susceptibility due to unpaired heme iron electrons. We propose that coagulated blood, with higher aggregated red blood cell content, will exhibit (1) a higher positive susceptibility than noncoagulated blood and (2) increase in fibrin polymerization-restricted localized diffusion, which can be measured noninvasively using quantitative susceptibility mapping and diffusion tensor imaging. In this serial magnetic resonance imaging study, we enrolled 24 patients with acute intracerebral hemorrhage between October 2021 to May 2022 at a stroke center. Patients were 30 to 70 years of age and had a hematoma volume >15 cm3 and National Institutes of Health Stroke Scale score >1. The patients underwent imaging 3×: within 12 to 24 (T1), 36 to 48 (T2), and 60 to 72 (T3) hours of last seen well on a 3T magnetic resonance imaging system. Three-dimensional anatomic, multigradient echo and 2-dimensional diffusion tensor images were obtained. Hematoma and edema volumes were calculated, and the distribution of coagulation was measured by dynamic changes in the susceptibilities and fractional anisotropy within the hematoma. Using a coagulated blood phantom, we demonstrated a linear relationship between the percentage coagulation and susceptibility (R2=0.91) with a positive red blood cell stain of the clot. The quantitative susceptibility maps showed a significant increase in hematoma susceptibility (T1, 0.29±0.04 parts per millions; T2, 0.36±0.04 parts per millions; T3, 0.45±0.04 parts per millions; P<0.0001). A concomitant increase in fractional anisotropy was also observed with time (T1, 0.40±0.02; T2, 0.45±0.02; T3, 0.47±0.02; P<0.05). This quantitative neuroimaging study of coagulation within the hematoma has the potential to improve patient management, such as safe resumption of anticoagulants, the need for reversal agents, the administration of alteplase to resolve the clot, and the need for surgery.

P067 Volumetric Assessment of Treatment Response in Newly Diagnosed Crohn Disease: A Pilot Study

Abstract Background Traditionally, mural thickness and disease length have been the radiological standards for assessing Crohn's Disease (CD) severity and treatment response. Our study aimed to evaluate and compare these linear measurements with volumetric ones in newly diagnosed pediatric CD, as well as to assess the concordance between manual and semi-automated volumetric methods. Methods In this single-center, prospective study, 20 children with newly diagnosed ileal CD treated with anti-TNF therapy from Dec 2018 to Oct 2021 (8 females; median age=14.5 years) underwent dedicated research MRI examinations at three timepoints – diagnosis (baseline) as well as 6 weeks and 6 months into therapy. For each exam and using T2-weighted Single Shot Fast Spin Echo (SSFSE) images, a single radiologist measured maximum bowel wall thickness (BWT, mm) and length of disease (cm). The inflamed ileum was manually segmented to measure tissue volume (ml) (Entrolytics; Motilent, London, UK). Semi-automated 3D volumetric measurements of the inflamed ileum (GI Seg tool) were created using the same platform. Estimated maximum BWT also was automatically calculated from manually-edited algorithmically generated masks. Mixed-effects models evaluated measurement changes over time. Intra-class correlation assessed absolute agreement between manual and automated volumetric assessments. Results BWT decreased over time with treatment (0.76 vs. 0.58 vs. 0.45 cm; p&amp;lt;0.0001), with a 24% decrease from baseline to 6 weeks and a 41% decrease from baseline to 6 months. Similarly, there was a significant decrease in length of disease over time (19.2 vs. 12.2 vs. 8.0 cm; p=0.002), with a 36% decrease from baseline to 6 weeks and a 58% decrease from baseline to 6 months. Manual volumetric measurements also showed a significant treatment response (19.8 vs. 11.6 vs. 5.1 ml; p&amp;lt;0.0001), with a 41% decrease from baseline to 6 weeks and a 74% decrease from baseline to 6 months. Using the semi-automated segmentation tool, the volume of inflamed bowel also decreased significantly over time (24.0 vs. 15.1 vs. 9.1 ml; p=0.0007), decreasing 37% between baseline and 6 weeks and 62% between baseline and 6 months. There was good agreement between manual and semi-automated volumetric bowel assessments (ICC=0.78 [95% CI: 0.57-0.88]). ICC between manual and code-estimated Max BWT was 0.60 [95% CI: 0.42, 0.72] with a strong positive correlation (r=0.839; p &amp;lt;0.001) between these two methods as shown in the Figure. Conclusion Volumetric assessments, along with automatic measurements of bowel wall thickness, show promise in accurately assessing treatment response in pediatric CD. These methods might provide more consistent and repeatable measurements, essential for long-term disease monitoring and management.

Enhancing Nigrosome-1 Sign Identification via Interpretable AI using True Susceptibility Weighted Imaging.

Nigrosome 1 (N1), the largest nigrosome region in the ventrolateral area of the substantia nigra pars compacta, is identifiable by the "N1 sign" in long echo time gradient echo MRI. The N1 sign's absence is a vital Parkinson's disease (PD) diagnostic marker. However, it is challenging to visualize and assess the N1 sign in clinical practice. To automatically detect the presence or absence of the N1 sign from true susceptibility weighted imaging by using deep-learning method. Prospective. 453 subjects, including 225 PD patients, 120 healthy controls (HCs), and 108 patients with other movement disorders, were prospectively recruited including 227 males and 226 females. They were divided into training, validation, and test cohorts of 289, 73, and 91 cases, respectively. 3D gradient echo SWI sequence at 3T; 3D multiecho strategically acquired gradient echo imaging at 3T; NM-sensitive 3D gradient echo sequence with MTC pulse at 3T. A neuroradiologist with 5 years of experience manually delineated substantia nigra regions. Two raters with 2 and 36 years of experience assessed the N1 sign on true susceptibility weighted imaging (tSWI), QSM with high-pass filter, and magnitude data combined with MTC data. We proposed NINet, a neural model, for automatic N1 sign identification in tSWI images. We compared the performance of NINet to the subjective reference standard using Receiver Operating Characteristic analyses, and a decision curve analysis assessed identification accuracy. NINet achieved an area under the curve (AUC) of 0.87 (CI: 0.76-0.89) in N1 sign identification, surpassing other models and neuroradiologists. NINet localized the putative N1 sign within tSWI images with 67.3% accuracy. Our proposed NINet model's capability to determine the presence or absence of the N1 sign, along with its localization, holds promise for enhancing diagnostic accuracy when evaluating PD using MR images. 2 TECHNICAL EFFICACY: Stage 1.

A Radar Echo Extrapolation Model Based on a Dual-Branch Encoder–Decoder and Spatiotemporal GRU

Precipitation forecasting is an immensely significant aspect of meteorological prediction. Accurate weather predictions facilitate services in sectors such as transportation, agriculture, and tourism. In recent years, deep learning-based radar echo extrapolation techniques have found effective applications in precipitation forecasting. However, the ability of existing methods to extract and characterize complex spatiotemporal features from radar echo images remains insufficient, resulting in suboptimal forecasting accuracy. This paper proposes a novel extrapolation algorithm based on a dual-branch encoder–decoder and spatiotemporal Gated Recurrent Unit. In this model, the dual-branch encoder–decoder structure independently encodes radar echo images in the temporal and spatial domains, thereby avoiding interference between spatiotemporal information. Additionally, we introduce a Multi-Scale Channel Attention Module (MSCAM) to learn global and local feature information from each encoder layer, thereby enhancing focus on radar image details. Furthermore, we propose a Spatiotemporal Attention Gated Recurrent Unit (STAGRU) that integrates attention mechanisms to handle temporal evolution and spatial relationships within radar data, enabling the extraction of spatiotemporal information from a broader receptive field. Experimental results demonstrate the model’s ability to accurately predict morphological changes and motion trajectories of radar images on real radar datasets, exhibiting superior performance compared to existing models in terms of various evaluation metrics. This study effectively improves the accuracy of precipitation forecasting in radar echo images, provides technical support for the short-range forecasting of precipitation, and has good application prospects.

Quantifying 3D MR fingerprinting (3D-MRF) reproducibility across subjects, sessions, and scanners automatically using MNI atlases.

Quantitative MRI techniques such as MR fingerprinting (MRF) promise more objective and comparable measurements of tissue properties at the point-of-care than weighted imaging. However, few direct cross-modal comparisons of MRF's repeatability and reproducibility versus weighted acquisitions have been performed. This work proposes a novel fully automated pipeline for quantitatively comparing cross-modal imaging performance in vivo via atlas-based sampling. We acquire whole-brain 3D-MRF, turbo spin echo, and MPRAGE sequences three times each on two scanners across 10 subjects, for a total of 60 multimodal datasets. The proposed automated registration and analysis pipeline uses linear and nonlinear registration to align all qualitative and quantitative DICOM stacks to Montreal Neurological Institute (MNI) 152 space, then samples each dataset's native space through transformation inversion to compare performance within atlas regions across subjects, scanners, and repetitions. Voxel values within MRF-derived maps were found to be more repeatable (σT1 = 1.90, σT2 = 3.20) across sessions than vendor-reconstructed MPRAGE (σT1w = 6.04) or turbo spin echo (σT2w = 5.66) images. Additionally, MRF was found to be more reproducible across scanners (σT1 = 2.21, σT2 = 3.89) than either qualitative modality (σT1w = 7.84, σT2w = 7.76). Notably, differences between repeatability and reproducibility of in vivo MRF were insignificant, unlike the weighted images. MRF data from many sessions and scanners can potentially be treated as a single dataset for harmonized analysis or longitudinal comparisons without the additional regularization steps needed for qualitative modalities.

Strategically Acquired Gradient Echo (STAGE) Imaging, part IV: Constrained Reconstruction of White Noise (CROWN) Processing as a Means to Improve Signal-to-Noise in STAGE Imaging at 3 Tesla

Increasing the signal-to-noise ratio (SNR) has always been of critical importance for magnetic resonance imaging. Although increasing field strength provides a linear increase in SNR, it is more and more costly as field strength increases. Therefore, there is a major effort today to use signal processing methods to improve SNR since it is more efficient and economical. There are a variety of methods to improve SNR such as averaging the data at the expense of imaging time, or collecting the data with a lower resolution, all of these methods, including imaging processing methods, usually come at the expense of loss of image detail or image blurring. Therefore, we developed a new mathematical approach called CROWN (Constrained Reconstruction of White Noise) to enhance SNR without loss of structural detail and without affecting scanning time. In this study, we introduced and tested the concept behind CROWN specifically for STAGE (strategically acquired gradient echo) imaging. The concept itself is presented first, followed by simulations to demonstrate its theoretical effectiveness. Then the SNR improvement on proton spin density (PSD) and R2⁎ maps was investigated using brain STAGE data acquired from 10 healthy controls (HCs) and 10 patients with Parkinson's disease (PD). For the PSD and R2* maps, the SNR and CNR between white matter and gray matter were improved by a factor of 1.87 ± 0.50 and 1.72 ± 0.88, respectively. The white matter hyperintensity lesions in PD patients were more clearly defined after CROWN processing. Using these improved maps, simulated images for any repeat time, echo time or flip angle can be created with improved SNR. The potential applications of this technology are to trade off the increased SNR for higher resolution images and/or faster imaging.

Deep Learning Reconstruction to Improve the Quality of MR Imaging: Evaluating the Best Sequence for T-category Assessment in Non-small Cell Lung Cancer Patients.

Deep learning reconstruction (DLR) has been recommended as useful for improving image quality. Moreover, compressed sensing (CS) or DLR has been proposed as useful for improving temporal resolution and image quality on MR sequences in different body fields. However, there have been no reports regarding the utility of DLR for image quality and T-factor assessment improvements on T2-weighted imaging (T2WI), short inversion time (TI) inversion recovery (STIR) imaging, and unenhanced- and contrast-enhanced (CE) 3D fast spoiled gradient echo (GRE) imaging with and without CS in comparison with thin-section multidetector-row CT (MDCT) for non-small cell lung cancer (NSCLC) patients. The purpose of this study was to determine the utility of DLR for improving image quality and the appropriate sequence for T-category assessment for NSCLC patients. As subjects for this study, 213 pathologically diagnosed NSCLC patients who underwent thin-section MDCT and MR imaging as well as T-factor diagnosis were retrospectively enrolled. SNR of each tumor was calculated and compared by paired t-test for each sequence with and without DLR. T-factor for each patient was assessed with thin-section MDCT and all MR sequences, and the accuracy for T-factor diagnosis was compared among all sequences and thin-section CT by means of McNemar's test. SNRs of T2WI, STIR imaging, unenhanced thin-section Quick 3D imaging, and CE-thin-section Quick 3D imaging with DLR were significantly higher than SNRs of those without DLR (P < 0.05). Diagnostic accuracy of STIR imaging and CE-thick- or thin-section Quick 3D imaging was significantly higher than that of thin-section CT, T2WI, and unenhanced thick- or thin-section Quick 3D imaging (P < 0.05). DLR is thus considered useful for image quality improvement on MR imaging. STIR imaging and CE-Quick 3D imaging with or without CS were validated as appropriate MR sequences for T-factor evaluation in NSCLC patients.

Characterization of quantitative susceptibility mapping in the left ventricular myocardium

Myocardial quantitative susceptibility mapping (QSM) may offer better specificity to iron than conventional T2* imaging in the assessment of cardiac diseases, including intra-myocardial hemorrhage. However, the precision and repeatability of cardiac QSM have not yet been characterized. The aim of this study is to characterize these key metrics in a healthy volunteer cohort and show the feasibility of the method in patients. Free breathing respiratory-navigated multi-echo 3D gradient echo images were acquired, from which QSM maps were reconstructed using the Morphology Enhanced Dipole Inversion toolbox. This technique was first evaluated in a susceptibility phantom containing tubes with known concentrations of gadolinium. In vivo characterization of myocardial QSM was then performed in a cohort of 10 healthy volunteers where each subject was scanned twice. Mean segment susceptibility, precision (standard deviation of voxel magnetic susceptibilities within one segment), and repeatability (absolute difference in segment mean susceptibility between repeats) of QSM were calculated for each American Heart Association (AHA) myocardial segment. Finally, the feasibility of the method was shown in 10 patients, including four with hemorrhagic infarcts. The phantom experiment showed a strong linear relationship between measured and predicted susceptibility shifts (R2 >0.99). For the healthy volunteer cohort, AHA segment analysis showed the mean segment susceptibility was 0.00±0.02ppm, the mean precision was 0.05±0.04ppm, and the mean repeatability was 0.02±0.02ppm. Cardiac QSM was successfully performed in all patients. Focal iron deposits were successfully visualized in the patients with hemorrhagic myocardial infarctions. The precision and repeatability of cardiac QSM were successfully characterized in phantom and in vivo experiments. The feasibility of the technique was also successfully demonstrated in patients. While challenges still remain, further clinical evaluation of the technique is now warranted. This work does not report on a health care intervention.

Brain Structures in a Human Embryo Imaged with MR Microscopy.

To delineate brain microstructures in human embryos during the formation of the various major primordia by MR microscopy, with different contrasts appropriate for each target. We focused mainly on the internal structures in the cerebral cortex and the accessory nerves of the brain. To find appropriate sequence parameters, we measured nuclear magnetic resonance (NMR) parameters and created kernel density plots of T1 and T2 values. We performed T1-weighted gradient echo imaging with parameters similar to those used in the previous studies. We performed T2*-weighted gradient echo imaging to delineate the target structures with the appropriate sequence parameters according to the NMR parameter and flip angle measurements. We also performed high-resolution imaging with both T1- and T2*-weighted sequences. T1, T2, and T2* values of the target tissues were positively correlated and shorter than those of the surrounding tissues. In T1-weighted images with a voxel size of (30 µm)3 and (20 µm)3, various organs and tissues and the agarose gel were differentiated as in previous studies, and the structure of approximately 40 µm in size was depicted, but the detailed structures within the cerebral cortex and the accessory nerves were not delineated. In T2*-weighted images with a voxel size of (30 µm)3, the layered structure within the cerebral cortex and the accessory nerves were clearly visualized. Overall, T1-weighted images provided more information than T2*-weighted images, but important internal brain structures of interest were visible only in T2*-weighted images. Therefore, it is essential to perform MR microscopy with different contrasts. We have visualized brain structures in a human embryo that had not previously been delineated by MR microscopy. We discussed pulse sequences appropriate for the structures of interest. This methodology would provide a way to visualize crucial embryological information about the anatomical structure of human embryos.

MSLKNet: A Multi-Scale Large Kernel Convolutional Network for Radar Extrapolation

Radar echo extrapolation provides important information for precipitation nowcasting. Existing mainstream radar echo extrapolation methods are based on the Single-Input-Single-Output (SISO) architecture. These approaches of recursively predicting the predictive echo image with the current echo image as input often results in error accumulation, leading to severe performance degradation. In addition, the echo motion variations are extremely complex. Different regions of strong or weak echoes should receive different degrees of attention. Previous methods have not been specifically designed for this aspect. This paper proposes a new radar echo extrapolation network based entirely on a convolutional neural network (CNN). The network uses a Multi-Input-Multi-Output (MIMO) architecture to mitigate cumulative errors. It incorporates a multi-scale, large kernel convolutional attention module that enhances the extraction of both local and global information. This design results in improved performance while significantly reducing training costs. Experiments on dual-polarization radar echo datasets from Shijiazhuang and Nanjing show that the proposed fully CNN-based model can achieve better performance while reducing computational cost.

Two-center validation of Pilot Tone based cardiac triggering of a comprehensive cardiovascular magnetic resonance examination.

The electrocardiogram (ECG) signal is prone to distortions from gradient and radiofrequency interference and the magnetohydrodynamic effect during cardiovascular magnetic resonance imaging (CMR). Although Pilot Tone Cardiac (PTC) triggering has the potential to overcome these limitations, effectiveness across various CMR techniques has yet to be established. To evaluate the performance of PTC triggering in a comprehensive CMR exam. Fifteen volunteers and 20 patients were recruited at two centers. ECG triggered images were collected for comparison in a subset of sequences. The PTC trigger accuracy was evaluated against ECG in cine acquisitions. Two experienced readers scored image quality in PTC-triggered cine, late gadolinium enhancement (LGE), and T1- and T2-weighted dark-blood turbo spin echo (DB-TSE) images. Quantitative cardiac function, flow, and parametric mapping values obtained using PTC and ECG triggered sequences were compared. Breath-held segmented cine used for trigger timing analysis was collected in 15 volunteers and 14 patients. PTC calibration failed in three volunteers and one patient; ECG trigger recording failed in one patient. Out of 1987 total heartbeats, three mismatched trigger PTC-ECG pairs were found. Image quality scores showed no significant difference between PTC and ECG triggering. There was no significant difference found in quantitative measurements in volunteers. In patients, the only significant difference was found in post-contrast T1 (p = 0.04). ICC showed moderate to excellent agreement in all measurements. PTC performance was equivalent to ECG in terms of triggering consistency, image quality, and quantitative image measurements across multiple CMR applications.

Resolution enhancement, noise suppression, and joint T2* decay estimation in dual-echo sodium-23 MR imaging using anatomically guided reconstruction.

Sodium MRI is challenging because of the low tissue concentration of the 23 Na nucleus and its extremely fast biexponential transverse relaxation rate. In this article, we present an iterative reconstruction framework using dual-echo 23 Na data and exploiting anatomical prior information (AGR) from high-resolution, low-noise, 1 H MR images. This framework enables the estimation and modeling of the spatially varying signal decay due to transverse relaxation during readout (AGRdm), which leads to images of better resolution and reduced noise resulting in improved quantification of the reconstructed 23 Na images. The proposed framework was evaluated using reconstructions of 30 noise realizations of realistic simulations of dual echo twisted projection imaging (TPI) 23 Na data. Moreover, three dual echo 23 Na TPI brain datasets of healthy controls acquired on a 3T Siemens Prisma system were reconstructed using conventional reconstruction, AGR and AGRdm. Our simulations show that compared to conventional reconstructions, AGR and AGRdm show improved bias-noise characteristics in several regions of the brain. Moreover, AGR and AGRdm images show more anatomical detail and less noise in the reconstructions of the experimental data sets. Compared to AGR and the conventional reconstruction, AGRdm shows higher contrast in the sodium concentration ratio between gray and white matter and between gray matter and the brain stem. AGR and AGRdm generate 23 Na images with high resolution, high levels of anatomical detail, and low levels of noise, potentially enabling high-quality 23 Na MR imaging at 3T.

Replication of the bSTAR sequence and open-source implementation.

The reproducibility of scientific reports is crucial to advancing human knowledge. This paper is a summary of our experience in replicating a balanced SSFP half-radial dual-echo imaging technique (bSTAR) using open-source frameworks as a response to the 2023 ISMRM "repeat it with me" Challenge. We replicated the bSTAR technique for thoracic imaging at 0.55T. The bSTAR pulse sequence is implemented in Pulseq, a vendor neutral open-source rapid sequence prototyping environment. Image reconstruction is performed with the open-source Berkeley Advanced Reconstruction Toolbox (BART). The replication of bSTAR, termed open-source bSTAR, is tested by replicating several figures from the published literature. Original bSTAR, using the pulse sequence and image reconstruction developed by the original authors, and open-source bSTAR, with pulse sequence and image reconstruction developed in this work, were performed in healthy volunteers. Both echo images obtained from open-source bSTAR contain no visible artifacts and show identical spatial resolution and image quality to those in the published literature. A direct head-to-head comparison between open-source bSTAR and original bSTAR on a healthy volunteer indicates that open-source bSTAR provides adequate SNR, spatial resolution, level of artifacts, and conspicuity of pulmonary vessels comparable to original bSTAR. We have successfully replicated bSTAR lung imaging at 0.55T using two open-source frameworks. Full replication of a research method solely relying on information on a research paper is unfortunately rare in research, but our success gives greater confidence that a research methodology can be indeed replicated as described.

Stroke in a young adult: looking beyond the diffusion-weighted imaging sequence.

A 37-year-old migrainous woman was referred several hours after a sudden dysarthria lasting less than 1min. Brain MRI undertaken a few hours later showed no acute cerebral infarction on the diffusion-weighted imaging (DWI) sequence. However, an intraluminal thrombus within a distal branch of the middle cerebral artery (MCA) was seen on the T2*-weighted gradient echo images as well as hyperintense vessels related to slow flow beyond the occlusion's site. Control MRI 24h later confirmed the presence of a small infarct affecting the distal portion of the MCA territory but the thrombus was no longer visible. A comprehensive etiologic workup revealed the presence of a patent foramen ovale, as the underlying cause of the patient's stroke. Though the sensitivity of DWI sequence for acute ischemia is high, false negative exist particularly in cases of transient ischemic attack, brainstem or lacunar strokes. A careful brain MRI examination or the use of perfusion weighted imaging sequences are useful to avoid misdiagnosis particularly in young patients.

Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging.

T2-weighted Single Shot Fast Spin Echo (SSFSE) scans at 3 Tesla (3T) are increasingly used to image fetal pathology due to their excellent tissue contrast resolution and signal-to-noise ratio (SNR). Temperature changes that may occur in response to radio frequency (RF) pulses used for these sequences at 3T have not been studied in human fetal brains. To evaluate the safety of T2-weighted SSFSE for fetal brains at 3T, magnetic resonance (MR) thermometry was used to measure relative temperature changes in a typical clinical fetal brain MR exam. Relative temperature was estimated using sets of gradient recalled echo (GRE) images acquired before and after T2-weighted SSFSE images which lasted 27.47±8.19 minutes. Thirty-one fetuses with cardiac abnormalities, and 20 healthy controls were included in this study. Fetal brain temperature was estimated by proton resonance frequency (PRF) thermometry and compared to the estimated temperature in the gluteal muscle of the mother. Seven scans with excessive motion were excluded. Local outlier factor (LOF) was performed to remove 12 additional scans with spurious phase measurements due to motion degradation and potential field drift. Linear regression was performed to determine if temperature changes are dependent on the rate of energy deposition during the scan. For the 32 participants used in the analysis, 17 with cardiac abnormalities and 15 healthy controls, the average relative fetal temperate change was 0.19±0.73 ℃ higher than the mother, with no correlation between relative temperature change and the rate of images acquired during the scans (regression coefficient =-0.05, R-squared =0.05, P=0.22, F-statistic =1.60). The difference in the relative temperature changes between the fetal brain and mother's gluteal tissue in the healthy controls was on average 0.08 ℃ lower and found not to be statistically different (P=0.76) to the group with cardiac abnormalities. Our results indicate that the estimated relative temperature changes of the fetal brain compared to the mother's gluteal tissue from RF pulses during the course of the T2-weighted SSFSE fetal MR exam are minimal. The differences in acquired phase between these regions through the exam were found not to be statistically different. These findings support that fetal brain imaging at 3T is within FDA limits and safe.