Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Association of Adverse Clinical Outcomes With Peri-Infarct Ischemia Detected by Stress Cardiac Magnetic Imaging.

BACKGROUND: Early invasive revascularization guided by moderate to severe ischemia did not improve outcomes over medical therapy alone, underlying the need to identify high-risk patients for a more effective invasive referral. CMR could determine the myocardial extent and matching locations of ischemia and infarction. OBJECTIVES: This study sought to investigate if CMR peri-infarct ischemia is associated with adverse events incremental to known risk markers. METHODS: Consecutive patients were included in an expanded cohort of the multicenter SPINS (Stress CMR Perfusion Imaging in the United States) study. Peri-infarct ischemia was defined by the presence of any ischemic segment neighboring an infarcted segment by late gadolinium enhancement imaging. Primary outcome events included acute myocardial infarction and cardiovascular death, whereas secondary events included any primary events, hospitalization for unstable angina, heart failure hospitalization, and late coronary artery bypass surgery. RESULTS: Among 3,915 patients (age: 61.0 ± 12.9 years; 54.7% male), ischemia, infarct, and peri-infarct ischemia were present in 752 (19.2%), 1,123 (28.8%), and 382 (9.8%) patients, respectively. At 5.3 years (Q1-Q3: 3.9-7.2 years) of median follow-up, primary and secondary events occurred in 406 (10.4%) and 745 (19.0%) patients, respectively. Peri-infarct ischemia was the strongest multivariable predictor for primary and secondary events (HRadjusted: 1.72 [95% CI: 1.23-2.41] and 1.71 [95% CI: 1.32-2.20], respectively; both P < 0.001), adjusted for clinical risk factors, left ventricular function, ischemia extent, and infarct size. The presence of peri-infarct ischemia portended to a >6-fold increased annualized primary event rate compared to those with no infarct and ischemia (6.5% vs 0.9%). CONCLUSIONS: Peri-infarct ischemia is a novel and robust prognostic marker of adverse cardiovascular events.

The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSION: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

Motion-robust free-running volumetric cardiovascular MRI.

PURPOSE: To present and assess an outlier mitigation method that makes free-running volumetric cardiovascular MRI (CMR) more robust to motion. METHODS: The proposed method, called compressive recovery with outlier rejection (CORe), models outliers in the measured data as an additive auxiliary variable. We enforce MR physics-guided group sparsity on the auxiliary variable, and jointly estimate it along with the image using an iterative algorithm. For evaluation, CORe is first compared to traditional compressed sensing (CS), robust regression (RR), and an existing outlier rejection method using two simulation studies. Then, CORe is compared to CS using seven three-dimensional (3D) cine, 12 rest four-dimensional (4D) flow, and eight stress 4D flow imaging datasets. RESULTS: Our simulation studies show that CORe outperforms CS, RR, and the existing outlier rejection method in terms of normalized mean square error and structural similarity index across 55 different realizations. The expert reader evaluation of 3D cine images demonstrates that CORe is more effective in suppressing artifacts while maintaining or improving image sharpness. Finally, 4D flow images show that CORe yields more reliable and consistent flow measurements, especially in the presence of involuntary subject motion or exercise stress. CONCLUSION: An outlier rejection method is presented and tested using simulated and measured data. This method can help suppress motion artifacts in a wide range of free-running CMR applications.

The impact of severe obesity on image quality and ventricular function assessment in echocardiography and cardiac MRI.

This study sought to evaluate the impact of severe obesity on image quality and ventricular function assessment in cardiovascular magnetic resonance (MRI) and trans-thoracic echocardiography (TTE). We studied 100 consecutive patients who underwent clinically indicated cardiac MRI and TTE studies within 12 months between July 2017 and December 2020; 50 (28 females and 22 males; 54.5 ± 18.7 years) with normal body mass index (BMI) (18.5-25 kg/m2) and 50 (21 females and 29 males; 47.2 ± 13.3 years) with severe obesity (BMI ≥ 40 kg/m2). MRI and TTE image quality scores were compared within and across cohorts using a linear mixed model. Categorical left (LVF) and right (RVF) ventricular function were compared using Cohens Kappa statistic. Mean BMI for normal weight and obese cohorts were 22.2 ± 1.7 kg/m2 and 50.3 ± 5.9 kg/m2, respectively. Out of a possible 93 points, mean MRI image quality score was 91.5 ± 2.5 for patients with normal BMI, and 88.4 ± 5.5 for patients with severe obesity; least square (LS) mean difference 3.1, p = 0.460. TTE scores were 64.2 ± 13.6 for patients with normal BMI and 46.0 ± 12.9 for patients with severe obesity, LS mean difference 18.2, p < 0.001. Ventricular function agreement between modalities was worse in the obese cohort for both LVF (72% vs 80% agreement; kappa 0.53 vs 0.70, obese vs. normal BMI), and RVF (58% vs 72% agreement, kappa 0.18 vs 0.34, obese vs. normal BMI). Severe obesity had limited impact on cardiac MRI image quality, while obesity significantly degraded TTE image quality and ventricular function agreement with MRI.

Cardiac MRI at Low Field Strengths.

Cardiac MR imaging is well established for assessment of cardiovascular structure and function, myocardial scar, quantitative flow, parametric mapping, and myocardial perfusion. Despite the clear evidence supporting the use of cardiac MRI for a wide range of indications, it is underutilized clinically. Recent developments in low-field MRI technology, including modern data acquisition and image reconstruction methods, are enabling high-quality low-field imaging that may improve the cost-benefit ratio for cardiac MRI. Studies to-date confirm that low-field MRI offers high measurement concordance and consistent interpretation with clinical imaging for several routine sequences. Moreover, low-field MRI may enable specific new clinical opportunities for cardiac imaging such as imaging near metal implants, MRI-guided interventions, combined cardiopulmonary assessment, and imaging of patients with severe obesity. In this review, we discuss the recent progress in low-field cardiac MRI with a focus on technical developments and early clinical validation studies. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Environmental Sustainability and MRI: Challenges, Opportunities, and a Call for Action.

The environmental impact of magnetic resonance imaging (MRI) has recently come into focus. This includes its enormous demand for electricity compared to other imaging modalities and contamination of water bodies with anthropogenic gadolinium related to contrast administration. Given the pressing threat of climate change, addressing these challenges to improve the environmental sustainability of MRI is imperative. The purpose of this review is to discuss the challenges, opportunities, and the need for action to reduce the environmental impact of MRI and prepare for the effects of climate change. The approaches outlined are categorized as strategies to reduce greenhouse gas (GHG) emissions from MRI during production and use phases, approaches to reduce the environmental impact of MRI including the preservation of finite resources, and development of adaption plans to prepare for the impact of climate change. Co-benefits of these strategies are emphasized including lower GHG emission and reduced cost along with improved heath and patient satisfaction. Although MRI is energy-intensive, there are many steps that can be taken now to improve the environmental sustainability of MRI and prepare for the effects of climate change. On-going research, technical development, and collaboration with industry partners are needed to achieve further reductions in MRI-related GHG emissions and to decrease the reliance on finite resources. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Cardiac and respiratory motion extraction for MRI using pilot tone-a patient study.

This study aims to evaluate the accuracy and reliability of the cardiac and respiratory signals extracted from Pilot Tone (PT) in patients clinically referred for cardiovascular MRI. Twenty-three patients were scanned under free-breathing conditions using a balanced steady-state free-precession real-time (RT) cine sequence on a 1.5T scanner. The PT signal was generated by a built-in PT transmitter integrated within the body array coil, and retrospectively processed to extract respiratory and cardiac signals. For comparison, ECG and BioMatrix (BM) respiratory sensor signals were also synchronously recorded. To assess the performances of PT, ECG, and BM, cardiac and respiratory signals extracted from the RT cine images were used as the ground truth. The respiratory motion extracted from PT correlated positively with the image-derived respiratory signal in all cases and showed a stronger correlation (absolute coefficient: 0.95 ± 0.09) than BM (0.72 ± 0.24). For the cardiac signal, PT trigger jitter (standard deviation of PT trigger locations relative to ECG triggers) ranged from 6.6 to 83.3 ms, with a median of 21.8 ms. The mean absolute difference between the PT and corresponding ECG cardiac cycle duration was less than 5% of the average ECG RR interval for 21 out of 23 patients. We did not observe a significant linear dependence (p > 0.28) of PT delay and PT jitter on the patients' BMI or cardiac cycle duration. This study demonstrates the potential of PT to monitor both respiratory and cardiac motion in patients clinically referred for cardiovascular MRI.

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.

Computational modeling of the thermal effects of flow on radio frequency-induced heating of peripheral vascular stents during MRI.

Purpose. The goal of this study was to develop and validate a computational model that can accurately predict the influence of flow on the temperature rise near a peripheral vascular stent during magnetic resonance imaging (MRI).Methods. Computational modeling and simulation of radio frequency (RF) induced heating of a vascular stent during MRI at 3.0 T was developed and validated with flow phantom experiments. The maximum temperature rise of the stent was measured as a function of physiologically relevant flow rates.Results. A significant difference was not identified between the experiment and simulation (P > 0.05). The temperature rise of the stent during MRI was over 10 °C without flow, and was reduced by 5 °C with a flow rate of only 58 ml min-1, corresponding to a reduction of CEM43from 45 min to less than 1 min.Conclusion. The computer model developed in this study was validated with experimental measurements, and accurately predicted the influence of flow on the RF-induced temperature rise of a vascular stent during MRI. Furthermore, the results of this study demonstrate that relatively low flow rates significantly reduce the temperature rise of a stent and the surrounding medium during RF-induced heating under typical scanning power and physiologically relevant conditions.

Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets.

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p<0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

Two-center validation of Pilot Tone Based Cardiac Triggering of a Comprehensive Cardiovascular Magnetic Resonance Examination.

Background: 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. Purpose: To evaluate the performance of PTC triggering in a comprehensive CMR exam. Methods: Fifteen volunteers and twenty 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. Results: 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. Conclusion: PTC performance was equivalent to ECG in terms of triggering consistency, image quality, and quantitative image measurements across multiple CMR applications.

Low-field MRI: A report on the 2022 ISMRM workshop.

In March 2022, the first ISMRM Workshop on Low-Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low-field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low-field MRI within traditional radiology practices, other point-of-care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low-field portable systems that are purpose-built for clinical decision-making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value.

Parametric mapping by cardiovascular magnetic resonance imaging in sudden cardiac arrest survivors.

Etiology of sudden cardiac arrest (SCA) is identified in less than 30% of survivors without coronary artery disease. We sought to assess the diagnostic role of myocardial parametric mapping using cardiovascular magnetic resonance (CMR) in identifying SCA etiology. Consecutive SCA survivors undergoing CMR with myocardial parametric mapping were included in the study. The determination if CMR was decisive or contributory in identifying SCA etiology was made if the diagnosis was unclear prior to CMR, and the discharge diagnosis was consistent with the CMR result. Parametric mapping was considered essential for establishing probable SCA etiology by CMR if the SCA cause could not have been determined without its utilization. If the CMR diagnosis could have been potentially based on the combination of cine and LGE imaging, parametric mapping was considered contributory. Of the 35 patients (mean age 46.9 ± 14.1 years; 57% males) included, SCA diagnosis was based on CMR in 23 (66%) patients. Of those, parametric mapping was essential for the diagnosis of myocarditis and tako-tsubo cardiomyopathy (11/48%) and contributed to the diagnosis in 10 (43%) additional cases. Inclusion of quantitative T1 and T2 parametric mapping in the SCA CMR protocol has the potential to increase diagnostic yield of CMR and further specify SCA etiology, especially myocarditis.

Cardiac magnetic resonance imaging for left atrial appendage closure planning.

BACKGROUND: Transesophageal echocardiography (TEE) and cardiac computed tomography angiography (CCTA) are currently utilized for left atrial appendage closure (LAAC) planning. During the recent global iodine contrast media shortage in 2022, cardiac magnetic resonance imaging (CMR) was utilized for the first time for LAAC planning. This study sought to assess the utility of CMR versus TEE for LAAC planning. METHODS: This single center retrospective study consisted of all patients who underwent preoperative CMR for LAAC with Watchman FLX or Amplatzer Amulet. Key measures were accuracy of LAA thrombus exclusion, ostial diameter, depth, lobe count, morphology, accuracy of predicted device size, and devices deployed per case. Bland-Altman Analysis was used to compare CMR versus TEE measurements of LAA ostial diameter and depth. RESULTS: 25 patients underwent preoperative CMR for LAAC planning. A total of 24 (96%) cases were successfully completed with 1.2 ± 0.5 devices deployed per case. Among the 18 patients who underwent intraoperative TEE, there was no significant difference between CMR versus TEE in LAA thrombus exclusion (CMR 83% vs. TEE 100% cases, p = .229), lobe count (CMR 1.7 ± 0.8 vs. TEE 1.4 ± 0.6, p = .177), morphology (p = .422), and accuracy of predicted device size (CMR 67% vs. TEE 72% cases, p = 1.000). When comparing the difference between CMR and TEE measurements, Bland-Altman analysis demonstrated no significant difference in LAA ostial diameter (CMR-TEE bias 0.7 mm, 95% CI [-1.1, 2.4], p = .420), but LAA depth was significantly larger with CMR versus TEE (CMR-TEE bias 7.4 mm, 95% CI [1.6, 13.2], p = .015). CONCLUSIONS: CMR is a promising alternative for LAAC planning in cases where TEE or CCTA are contraindicated or unavailable.

Building a comprehensive cardiovascular magnetic resonance exam on a commercial 0.55 T system: A pictorial essay on potential applications.

Background: Contemporary advances in low-field magnetic resonance imaging systems can potentially widen access to cardiovascular magnetic resonance (CMR) imaging. We present our initial experience in building a comprehensive CMR protocol on a commercial 0.55 T system with a gradient performance of 26 mT/m amplitude and 45 T/m/s slew rate. To achieve sufficient image quality, we adapted standard imaging techniques when possible, and implemented compressed-sensing (CS) based techniques when needed in an effort to compensate for the inherently low signal-to-noise ratio at lower field strength. Methods: A prototype CMR exam was built on an 80 cm, ultra-wide bore commercial 0.55 T MR system. Implementation of all components aimed to overcome the inherently lower signal of low-field and the relatively longer echo and repetition times owing to the slower gradients. CS-based breath-held and real-time cine imaging was built utilizing high acceleration rates to meet nominal spatial and temporal resolution recommendations. Similarly, CS 2D phase-contrast cine was implemented for flow. Dark-blood turbo spin echo sequences with deep learning based denoising were implemented for morphology assessment. Magnetization-prepared single-shot myocardial mapping techniques incorporated additional source images. CS-based dynamic contrast-enhanced imaging was implemented for myocardial perfusion and 3D MR angiography. Non-contrast 3D MR angiography was built with electrocardiogram-triggered, navigator-gated magnetization-prepared methods. Late gadolinium enhanced (LGE) tissue characterization methods included breath-held segmented and free-breathing single-shot imaging with motion correction and averaging using an increased number of source images. Proof-of-concept was demonstrated through porcine infarct model, healthy volunteer, and patient scans. Results: Reasonable image quality was demonstrated for cardiovascular structure, function, flow, and LGE assessment. Low-field afforded utilization of higher flip angles for cine and MR angiography. CS-based techniques were able to overcome gradient speed limitations and meet spatial and temporal resolution recommendations with imaging times comparable to higher performance scanners. Tissue mapping and perfusion imaging require further development. Conclusion: We implemented cardiac applications demonstrating the potential for comprehensive CMR on a novel commercial 0.55 T system. Further development and validation studies are needed before this technology can be applied clinically.

Sex-Specific Stress Perfusion Cardiac Magnetic Resonance Imaging in Suspected Ischemic Heart Disease: Insights From SPINS Retrospective Registry.

BACKGROUND: Cardiovascular disease (CVD) remains the leading cause of mortality in women, but current noninvasive cardiac imaging techniques have sex-specific limitations. OBJECTIVES: In this study, the authors sought to investigate the effect of sex on the prognostic utility and downstream invasive revascularization and costs of stress perfusion cardiac magnetic resonance (CMR) for suspected CVD. METHODS: Sex-specific prognostic performance was evaluated in a 2,349-patient multicenter SPINS (Stress CMR Perfusion Imaging in the United States [SPINS] Study) Registry. The primary outcome measure was a composite of cardiovascular death and nonfatal myocardial infarction; secondary outcomes were hospitalization for unstable angina or heart failure, and late unplanned coronary artery bypass grafting. RESULTS: SPINS included 1,104 women (47% of cohort); women had higher prevalence of chest pain (62% vs 50%; P < 0.0001) but lower use of medical therapies. At the 5.4-year median follow-up, women with normal stress CMR had a low annualized rate of primary composite outcome similar to men (0.54%/y vs 0.75%/y, respectively; P = NS). In contrast, women with abnormal CMR were at higher risk for both primary (3.74%/y vs 0.54%/y; P < 0.0001) and secondary (9.8%/y vs 1.6%/y; P < 0.0001) outcomes compared with women with normal CMR. Abnormal stress CMR was an independent predictor for the primary (HR: 2.64 [95% CI: 1.20-5.90]; P = 0.02) and secondary (HR: 2.09 [95% CI: 1.43-3.08]; P < 0.0001) outcome measures. There was no effect modification for sex. Women had lower rates of invasive coronary angiography (3.6% vs 7.3%; P = 0.0001) and downstream costs ($114 vs $171; P = 0.001) at 90 days following CMR. There was no effect of sex on diagnostic image quality. CONCLUSIONS: Stress CMR demonstrated excellent prognostic performance with lower rates of invasive coronary angiography referral in women. Stress CMR should be considered as a first-line noninvasive imaging tool for the evaluation of women. (Stress CMR Perfusion Imaging in the United States [SPINS] Study [SPINS]; NCT03192891).