PURPOSE:To optimize a spiral acquisition using a fixed-angle subspace navigator and golden angle trajectory, combined with motion-corrected (MOCO) subspace reconstruction, for motion-robust, quantitative high-resolution whole-heart first-pass perfusion imaging at 3 T. METHODS:Spiral acquisition used a fixed-angle navigator to extract the temporal basis, combined with seven spiral arms ((1 + 7)-arm) rotated using k-t golden angles, yielding a 90 ms temporal footprint for two interleaved slices at 1.3 × 1.3 mm2. Non-rigid deformation fields estimated from auxiliary images were incorporated into the subspace reconstruction. Performance was tested in XCAT simulations and 5 retrospectively undersampled free-breathing datasets. Prospective reconstructions using Subspace, L1-SENSE, and SENSE with and without MOCO from 22 patients were visually graded (1-5) by three cardiovascular imagers. In 11 of these patients an arterial input function (AIF) was acquired and myocardial blood flow (MBF) maps were calculated using Fermi-function deconvolution. RESULTS:In simulations and retrospective datasets, the navigator captured cardiac motion and contrast dynamics effectively, and the (1 + 7)-arm k-t GA DD1-Hs configuration improved subspace reconstruction through increased spatiotemporal incoherency. In prospective studies, Subspace-MOCO achieved the highest visual scores (p < 0.001), with MOCO improving all methods (EMM ± 1.96 × SE: SENSE-MOCO 2.2 ± 0.2, L1-SENSE-MOCO 4.1 ± 0.2, Subspace-MOCO 4.6 ± 0.12). Subspace-MOCO also provided superior boundary sharpness and global edge sharpness, higher NCC and NMI (p < 0.05). Its MBF values were similar to those obtained with SENSE-MOCO and closer than those reconstructed from L1-SENSE-MOCO. CONCLUSIONS:The proposed navigator-guided MOCO subspace reconstruction substantially reduces respiratory motion artifacts and enables high-resolution, motion-corrected whole-heart quantitative spiral perfusion imaging.

Motivation: Cine imaging is important for CMR quantification of left ventricular (LV) function, but conventional breath-held techniques are time consuming. Goal(s): This study aims to improve continuous spiral-based cardiac cine acquisition efficiency. Approach: A CAIPIRINHA-based SMS imaging using spiral acquisition with respiratory correction and cardiac self-gating strategy is proposed. Results: By optimizing the phase modulation pattern with robust self-gating signal extraction, cine images covering the whole LV are obtained in approximately 30s free breathing without ECG gating. The proposed technique yielded comparable LVEF to the clinical gold-standard. Although image quality was slightly inferior to the clinical technique, SMS-SPARCS produced diagnostically acceptable image quality. Impact: This study furthers our insight into designing phase modulation and reconstruction techniques for continuous spiral-based multi-slice self-gated acquisition. This technique improves efficiency of cine acquisition and does not require ECG or breath-holding.

INTRODUCTION:Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). METHODS:This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. RESULTS:Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2-0.46), moderate to almost perfect (Cohen's κ: 0.55-0.92), and moderate to almost perfect (Cohen's κ: 0.53-0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). CONCLUSIONS:Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. SUMMARY SENTENCE:Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.

The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-resolution spiral real-time cine imaging, including 2D balanced steady-state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U-Net-based image reconstruction network and 2D U-Net-based image segmentation network were proposed and evaluated. Low-rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal-to-noise ratio, normalized root-mean-square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL-based image reconstruction and quantification techniques enabled 1-min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high-resolution spiral real-time cine imaging with excellent performance.

Purpose To develop a compact MR‐compatible ergometer for exercise stress and to initially evaluate the reproducibility of myocardial native T1 and myocardial blood flow (MBF) measurements during exercise stress performed on this ergometer. Methods The compact ergometer consists of exercise, workload, and data processing components. The exercise stress can be achieved by pedaling on a pair of cylinders at a predefined frequency with adjustable resistances. Ten healthy subjects were recruited to perform cardiac MRI scans twice in a 3.0T MR scanner, at different days to assess reproducibility. Myocardial native T1 and MBF were acquired at rest and during a moderate exercise. The reproducibility of the two tests was determined by the intra‐group correlation coefficient (ICC) and coefficient of variation (CoV). Results The mean exercise intensity in this pilot study was 45 Watts (W), with an exercise duration of 5 min. Stress induced a significant increase in systolic blood pressure (from 113 ± 11 mmHg to 141 ± 12, P < 0.05) and maximal increase in heart rate by 74 ± 19%. The rate pressure product increased two‐fold ( P < 0.001). Excellent reproducibility was demonstrated in native T1 during the exercise (CoV = 3.0%), whereas the reproducibility of MBF and myocardial perfusion reserve during the exercise was also good (CoV = 10.7% and 8.8%, respectively). Conclusion This pilot study demonstrated that it is possible to acquire reproducible measurements of myocardial native T1 and MBF during the exercise stress in healthy volunteers using our new compact ergometer.

Variable density spiral (VDS) pulse sequences with motion compensated compressed sensing reconstruction allow for whole-heart quantitative assessment of myocardial perfusion but have not yet been clinically validated. In this study, we showed that whole-heart VDS stress perfusion has good diagnostic accuracy and ischemic burden evaluation. No significant difference was seen between visual and quantitative diagnostic performance and ischemic burden measurements.

The coronavirus disease 2019 (COVID-19) outbreak that originated in Wuhan, China has rapidly propagated due to widespread person-to-person transmission and has resulted in over 1,133,758 cases in 197 countries with a total of 62,784 deaths as of April 5, 2020. Laboratory confirmation of SARS-CoV-2 is performed with a virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test. This test can take up to two days to complete, and, due to the possibility of false negatives, serial testing may be required to reliably exclude infection. A current supply shortage of RT-PCR test kits compounds the shortcomings of entrusting diagnosis to the PCR test alone and underscores the urgent need to provide alternative methods for the rapid and accurate diagnosis of SARS-CoV-2 patients. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value to fully exclude infection, because of the normal radiologic findings in some early disease patients. In this study, we use artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history, and/or laboratory testing to more accurately and rapidly diagnose SARS-CoV-2 (+) patients. We included 905 RT-PCR confirmed patients. 419 (46.2%) tested positive for SARS-CoV-2 by laboratory-confirmed real-time RT-PCR assay and next-generation sequencing, while 486 patients (53.8%) tested negative (confirmed by at least two additional negative RT-PCR tests and clinical observation). The proposed AI system achieved an AUC of 0.92 and performed equally well in sensitivity compared to a senior thoracic radiologist on a testing set of 279 cases. The AI system also improved the detection of RT-PCR positive SARS-CoV-2 patients who presented with normal CTs, correctly identifying 17/25 (68%) patients, whereas all 25 RT-PCR SARS-CoV-2-positive CT-normal patients were classified as SARS-CoV-2 negative by radiologists.

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower.

Background Pulmonary tuberculosis (PTB) is a global health problem and remains the leading infectious cause of death worldwide. Differentiation between secondary PTB and non-tuberculous (non-TB) pneumonia is important for patient isolation and treatment, but can be difficult to determine clinically and radiologically. We proposed the application of deep learning to chest computerized tomography (CT) to assist doctors in detecting and differentiating PTB from non-TB pneumonia in an expedient, non-invasive, and reproducible manner. Methods: We retrospectively collected a dataset containing 1,124 CT scans from 923 PTB and non-TB pneumonia based on their pathological reports of lung biopsy and clinical information and 201 patients without pulmonary infiltrate from West China Hospital between 2012 and 2018. Randomly selected parts of this dataset (WCPR dataset) were used to develop, train, internally validate and test the algorithm. Patients in the WCPR dataset (PTB, n=439; non-TB pneumonia, n=484; normal, n= 201) were randomly assigned in three non-overlapping sets: training set, n=866; validation set, n=108; and test set, n=150. An additional dataset from NIH TB Portal31 comprising of cases from three countries (Belarus, n = 274; Romania, n = 43; Moldova, n = 10) was used to validate externally the algorithm's ability to identify PTB. A convolutional neural network of Inception-Res-Net-v230 was trained and tested on the entire chest CT to mimic real life application. The performance of our algorithm was compared to three trained radiology/pulmonology physicians. Findings For differentiating pulmonary infiltrates, the algorithm achieved 99·3% accuracy (149 out of 150), 99.0% sensitivity, and 100·0% specificity. For identifying PTB, the algorithm achieved 82·0% accuracy (123 out of 150), 95·9% sensitivity, and 75·2% specificity. For identifying non-TB pneumonia, the algorithm achieved 81·3% accuracy (122 out of 150), 52·7% sensitivity, and 97·9% specificity. This mostly outperformed our human readers for PTB identification, who averaged up to 81·1% accuracy, 70·8% sensitivity, and 86·1% specificity. Our algorithm identified 287 out of 327 PTB (87·8% accuracy) cases in NIH TB Portal Dataset from other countries. Interpretation: Our deep-learning-based algorithm successfully differentiated abnormal from normal chest CTs, as well as PTB from non-TB pneumonia cases and thus allows real world applicability. Early identification of PTB from non-TB pneumonia can help control outbreaks through isolation and early appropriate treatment. The application of our algorithm could expedite the identification of PTB, thereby improving clinical outcomes. Our datasets and algorithm used in this study will be publicly available to facilitate world-wide adoption. Funding Statement: The authors declare: None. Declaration of Interests: The authors declare: None. Ethics Approval Statement: This study was approved by the Institutional Review Board of West China Hospital (approval No. 2019-148) and Icahn School of Medicine at Mount Sinai (approval No. GCO#1: 19-0569(0001) ISMMS), and the patients’ written consents were waived.

Figure 1 of this original publication contained a minor error as one of the lines in the “Reconstruction pipline” was not visible. The updated Fig. 1 is published in this correction article.

PurposeTo develop a single‐shot spiral perfusion pulse sequence with outer‐volume suppression (OVS) to achieve whole‐heart coverage with a short temporal footprint of 10 ms per slice location.MethodsA highly accelerated single‐shot variable density spiral pulse sequence with an integrated OVS module for reduced field of view (rFOV) perfusion imaging with 2 mm spatial resolution was developed and evaluated in simulations, phantom experiments and in clinical patients with (n = 8) or without (n = 8) OVS. Images were reconstructed by block low‐rank sparsity with motion guidance (BLOSM) and graded by two cardiologists on a 5‐point scale (1, excellent; 5, poor).ResultsSimulation and phantom results showed that OVS effectively suppressed the signal outside the desired field of view (FOV). Clinical patient data demonstrated high quality perfusion images with rFOV. The average image quality scores of full FOV cases and rFOV cases were 3.1 ± 0.64 and 2.3 ± 0.46, respectively, (P = 0.02) from cardiologist 1 and 2.5 ± 0.54 and 1.8 ± 0.47, respectively, (P = 0.04) from cardiologist 2, showing superior image quality for the rFOV images compared with the full FOV images.ConclusionA single‐shot spiral perfusion sequence that uses OVS and BLOSM performs perfusion imaging with a very short temporal footprint per image supporting whole‐heart coverage with good image quality. Magn Reson Med 79:208–216, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The electromagnetic solitary waves of q-Gaussian laser beam in a preformed parabolic plasma channel is investigated by means of the variational method.Compared with the electromagnetic solitary waves of Gaussian laser beam,the results show that there exist two kinds of electromagnetic solitary solutions for q-Gaussian laser beam in a preformed plasma channel,i.e.,the focusing and the defocusing electromagnetic solitary wave solutions.In the same laser amplitude and plasma background,the laser beam with lower q has a larger amplitude and a poorer numerical stability for the focusing electromagnetic solitary waves.However,for the defocusing solitary waves,the results are just opposite.

检测了大豆油、菜籽油、芥花油、芝麻油、橄榄油、花生油、玉米油、葵花籽油、猪油、鱼油及地沟油的1H NMR、13C NMR光谱(核磁共振氢谱、碳谱).结果发现:植物油与猪油和鱼油有明显的区别;地沟油由于其产生、酸败和处理过程中有部分甘油酯的分子发生变化,所以在氢谱图的δ6.70～ 5.50、δ5.10和δ 3.73等处有明显的杂峰;地沟油、猪油和鱼油在氢谱图的δ1.26处的峰比δ 1.30处明显强很多;地沟油碳谱图在δ 178.33、δ 173.87、δ86.60、δ68.39和δ65.06处有明显的小峰存在,它们可以作为判断地沟油特征峰的依据.与其他分析方法相比,该方法具有操作简单、不需要预处理和快速等优点.