Quantitative cardiac MRI

Reference Values for Fetal Cardiac Dimensions, Volumes, Ventricular Function and Left Ventricular Longitudinal Strain Using Doppler Ultrasound Gated Cardiac Magnetic Resonance Imaging in Healthy Third Trimester Fetuses

BACKGROUND

Recent advances in hardware and software permit the use of cardiac MRI of late gestation fetuses, however there is a paucity of MRI-based reference values.

PURPOSE

To provide initial data on fetal cardiac MRI-derived cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain in healthy developing fetuses >30 weeks gestational age.

STUDY TYPE

Prospective.

POPULATION

Twenty-five third trimester (34 ± 1 weeks, range of 32-37 weeks gestation) women with healthy developing fetuses.

FIELD STRENGTH/SEQUENCE

Studies were performed at 1.5 T and 3 T. Cardiac synchronization was achieved with a Doppler ultrasound device. The protocol included T2 single shot turbo spin echo stacks for fetal weight and ultrasound probe positioning, and multiplanar multi-slice cine balanced steady state free precession gradient echo sequences.

ASSESSMENT

Primary analyses were performed by a single observer. Weight indexed right ventricular (RV) and left ventricular (LV) volumes and function were calculated from short axis (SAX) stacks. Cardiac dimensions were calculated from the four-chamber and SAX stacks. Single plane LV longitudinal strain was calculated from the four-chamber stack. Interobserver variability was assessed in 10 participants. Cardiac MRI values were compared against available published normative fetal echocardiogram data using z-scores.

STATISTICAL TESTS

Mean and SDs were calculated for baseline maternal/fetal demographics, cardiac dimensions, volumes, ventricular function, and left ventricular longitudinal strain. Bland-Altman and intraclass correlation coefficient analysis was performed to test interobserver variability.

RESULTS

The mean gestational age was 34 ± 1.4 weeks. The mean RV and LV end diastolic volumes were 3.1 ± 0.6 mL/kg and 2.4 ± 0.5 mL/kg respectively. The mean RV cardiac output was 198 ± 49 mL/min/kg while the mean LV cardiac output was 173 ± 43 mL/min/kg.

DATA CONCLUSION

This paper reports initial reference values obtained by cardiac MRI in healthy developing third trimester fetuses. MRI generally resulted in slightly larger indexed values (by z-score) compared to reports in literature using fetal echocardiography.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.

Global longitudinal strain is an informative index of left ventricular performance in neonates receiving intensive care

Echocardiographic assessment of left ventricular function is crucial in NICU. The study aimed to compare the accuracy and agreement of global longitudinal strain (GLS) with conventional measurements. Real-life echocardiograms of neonates receiving intensive care were retrospectively reviewed. Shortening fraction (SF), ejection fraction (EF) and S' measurements were retrieved from health records. GLS was calculated offline from stored images. The association with stroke volume indexed for body weight (iSV) was evaluated by regression analysis. The diagnostic ability to identify uncompensated shock was assessed by ROC curve analysis. Cohen's κ was run to assess agreement. 334 echocardiograms of 155 neonates were evaluated. Mean ± SD gestational age and birth weight were 34.5 ± 4.1 weeks and 2264 ± 914 g, respectively. SF, EF, S' and GLS were associated with iSV with R2 of 0.133, 0.332, 0.252 and 0.633, (all p < .001). Including all variables in a regression model, iSV prediction showed an adjusted R2 of 0.667, (p < .001). GLS explained 73% of the model variance. GLS showed a better ability to diagnose uncompensated shock (AUC 0.956) compared to EF, S' and SF (AUC 0.757, 0.737 and 0.606, respectively). GLS showed a moderate agreement with EF (κ = .500, p < .001) and a limited agreement with S' and SF (κ = .260, p < .001, κ = .242, p < .001). GLS was a more informative index of left ventricular performance, providing the rationale for a more extensive use of GLS at the cotside.

Tissue-Tracking Mitral Annular Displacement in Neonates: A Novel Index of Left Ventricular Systolic Function

OBJECTIVES

To assess the feasibility, accuracy, and reproducibility of tissue-tracking mitral annular displacement (TMAD) compared with other measures of left ventricular systolic function in healthy preterm and term neonates in the transitional period.

METHODS

This was a prospective observational study. Two echocardiograms were performed at 24 and 48 hours of life. TMAD, shortening fraction (SF), ejection fraction (EF), s', and global longitudinal strain (GLS) were measured offline. Accuracy to detect impaired GLS was tested by ROC curve analysis. DeLong test was used to compare AUCs. Intra and interobserver reproducibility of the off-line analysis was calculated.

RESULTS

Mean ± SD gestational age and weight were 34.2 ± 3.8 weeks and 2162 ± 833 g, respectively. TMAD was feasible in 168/180 scans (93%). At 24 hours the AUC (95% CI) of SF, EF, s', and TMAD (%) was 0.51 (0.36-0.67), 0.68 (0.54-0.82), 0.63 (0.49-0.77), and 0.89 (0.79-0.99) respectively. At 48 hours the AUC (95% CI) of SF, EF, s', and TMAD (%) was 0.64 (0.51-0.77), 0.59 (0.37-0.80), 0.70 (0.54-0.86), and 0.96 (0.91-1.00), respectively. The AUC of TMAD was superior to the AUC of SF, EF, s', at both timepoints (P < .02). Intraclass correlation coefficients (95% CI) of intra and interobserver reproducibility of TMAD were 0.97 (0.95-0.99) and 0.94 (0.88-0.97), respectively.

CONCLUSION

TMAD showed improved accuracy and optimal reproducibility in neonates in the first 48 hours of life.

Current use of cardiac MRI in animals

Cardiovascular magnetic resonance (CMR) imaging has evolved to become an indispensable tool in human cardiology. It is a non-invasive technique that enables objective assessment of myocardial function, size, and tissue composition. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of parametric mapping to explore tissue characteristics, and the emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. As veterinary cardiology continues to utilize CMR beyond the reference standard, clinical application of CMR will further expand our capabilities. This article describes the current use of CMR and adoption of more recent advances such as T1/T2 mapping in veterinary cardiology.

Comparative Study of 2D-Cine and 3D-wh Volumetry: Revealing Systemic Error of 2D-Cine Volumetry

This study investigates the crucial factors influencing the end-systolic and end-diastolic volumes in MRI volumetry and their direct effects on the derived functional parameters. Through the simultaneous acquisition of 2D-cine and 3D whole-heart slices in end-diastole and end-systole, we present a novel direct comparison of the volumetric measurements from both methods. A prospective study was conducted with 18 healthy participants. Both 2D-cine and 3D whole-heart sequences were obtained. Despite the differences in the creation of 3D volumes and trigger points, the impact on the LV volume was minimal (134.9 mL ± 16.9 mL vs. 136.6 mL ± 16.6 mL, p < 0.01 for end-diastole; 50.6 mL ± 11.0 mL vs. 51.6 mL ± 11.2 mL, p = 0.03 for end-systole). In our healthy patient cohort, a systematic underestimation of the end-systolic volume resulted in a significant overestimation of the SV (5.6 mL ± 2.6 mL, p < 0.01). The functional calculations from the 3D whole-heart method proved to be highly accurate and correlated well with function measurements from the phase-contrast sequences. Our study is the first to demonstrate the superiority of 3D whole-heart volumetry over 2D-cine volumetry and sheds light on the systematic error inherent in 2D-cine measurements.

Deep Learning Based Parameterization of Diffeomorphic Image Registration for Cardiac Image Segmentation

Cardiac segmentation from magnetic resonance imaging (MRI) is one of the essential tasks in analyzing the anatomy and function of the heart for the assessment and diagnosis of cardiac diseases. However, cardiac MRI generates hundreds of images per scan, and manual annotation of them is challenging and time-consuming, and therefore processing these images automatically is of interest. This study proposes a novel end-to-end supervised cardiac MRI segmentation framework based on a diffeomorphic deformable registration that can segment cardiac chambers from 2D and 3D images or volumes. To represent actual cardiac deformation, the method parameterizes the transformation using radial and rotational components computed via deep learning, with a set of paired images and segmentation masks used for training. The formulation guarantees transformations that are invertible and prevents mesh folding, which is essential for preserving the topology of the segmentation results. A physically plausible transformation is achieved by employing diffeomorphism in computing the transformations and activation functions that constrain the range of the radial and rotational components. The method was evaluated over three different data sets and showed significant improvements compared to exacting learning and non-learning based methods in terms of the Dice score and Hausdorff distance metrics.

Improved Simultaneous Multi-slice imaging with Composition of k-space Interpolations (SMS-COOKIE) for myocardial T1 mapping

The aim of this study is to develop and evaluate a regularized Simultaneous Multi-Slice (SMS) reconstruction method for improved Cardiac Magnetic Resonance Imaging (CMR). The proposed reconstruction method, SMS with COmpOsition of k-space IntErpolations (SMS-COOKIE) combines the advantages of Iterative Self-consistent Parallel Imaging Reconstruction (SPIRiT) and split slice-Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA), while allowing regularization for further noise reduction. The proposed SMS-COOKIE was implemented with and without regularization, and validated using a Saturation Pulse-Prepared Heart rate Independent inversion REcovery (SAPPHIRE) myocardial T1 mapping sequence. The performance of the proposed reconstruction method was compared to ReadOut (RO)-SENSE-GRAPPA and split slice-GRAPPA, on both retrospectively and prospectively three-fold SMS-accelerated data with an additional two-fold in-plane acceleration. All SMS reconstruction methods yielded similar T1 values compared to single band imaging. SMS-COOKIE showed lower spatial variability in myocardial T1 with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). The proposed method with additional locally low rank (LLR) regularization reduced the spatial variability, again with significant improvement over RO-SENSE-GRAPPA and split slice-GRAPPA (P < 10-4). In conclusion, improved reconstruction quality was achieved with the proposed SMS-COOKIE, which also provided lower spatial variability with significant improvement over split slice-GRAPPA.

Accelerating GRAPPA reconstruction using SoC design for real-time cardiac MRI

Real-time cardiac MRI is a rapidly developing area of research that has the potential to improve the diagnosis and treatment of cardiovascular diseases. However, the acquisition of high-quality real-time cardiac MR (CMR) images is challenging as it requires a high frame rate and temporal resolution. To overcome this challenge, there have been recent efforts on several approaches including hardware-based improvements and image reconstruction techniques such as compressed sensing and parallel MRI. The use of parallel MRI techniques such as GRAPPA (Generalized Autocalibrating Partial Parallel Acquisition) is a promising approach for improving the temporal resolution of MRI and expanding its applications in clinical practice. However, the GRAPPA algorithm involves a significant amount of computation, particularly for high acceleration factors and large datasets. This can result in long reconstruction times, which can limit the ability to achieve real-time imaging or high frame rates. One solution to this challenge is to use specialized hardware i.e. field-programmable gate arrays (FPGAs). In this work, a novel 32-bit floating-point FPGA-based GRAPPA accelerator is proposed with an aim to reconstruct high-quality cardiac MR images at higher frame rates, making it well suited for real-time clinical applications. The proposed FPGA-based accelerator consists of custom-designed data processing units named as dedicated computational engines (DCEs) that allow for a continuous flow of data between the calibration and synthesis stages of GRAPPA reconstruction process. This greatly increases the throughput and reduces the latency of the overall proposed system. Moreover, a high-speed memory module (DDR4-SDRAM) is integrated with the proposed architecture to store the multi-coil MR data. An on-chip quad-core ARM Cortex-A53 processor is used to manage access control information required for data transfer between the DCEs and DDR4-SDRAM. The proposed accelerator is implemented on Xilinx Zynq UltraScale + MPSoC using high-level synthesis (HLS) and hardware descriptive language (HDL) with an aim to explore the trade-offs between the reconstruction time, resource utilization and design effort. Several experiments have been performed using in-vivo cardiac datasets i.e. 18-receiver coil and 30-receiver coil to evaluate the performance of the proposed accelerator. A comparison is performed with the contemporary CPU and GPU-based GRAPPA reconstruction methods in terms of reconstruction time, frames-per-second and reconstruction accuracy (RMSE and SNR). The results show that the proposed accelerator achieves speed-up factors up to 121× and 9× as compared to the contemporary CPU-based and GPU-based GRAPPA reconstruction methods, respectively. Moreover, it has been demonstrated that the proposed accelerator can achieve reconstruction rates of up to ∼27 frames-per-second while maintaining the visual quality of the reconstructed images.

Recent technologies in cardiac imaging

Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.

Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes

The term chronic coronary syndromes encompasses a variety of clinical presentations of coronary artery disease (CAD), ranging from stable angina due to epicardial coronary artery disease to microvascular coronary dysfunction. Cardiac magnetic resonance (CMR) imaging has an established role in the diagnosis, prognostication and treatment planning of patients with CAD. Recent advances in parametric mapping CMR techniques have added value in the assessment of patients with chronic coronary syndromes, even without the need for gadolinium contrast administration. Furthermore, quantitative perfusion CMR techniques have enabled the non-invasive assessment of myocardial blood flow and myocardial perfusion reserve and can reliably identify multivessel coronary artery disease and microvascular dysfunction. This review summarizes the clinical applications and the prognostic value of the novel CMR parametric mapping techniques in the setting of chronic coronary syndromes and discusses their strengths, pitfalls and future directions.

The Merits, Limitations, and Future Directions of Cost-Effectiveness Analysis in Cardiac MRI with a Focus on Coronary Artery Disease: A Literature Review

Cardiac magnetic resonance (CMR) imaging has a wide range of clinical applications with a high degree of accuracy for many myocardial pathologies. Recent literature has shown great utility of CMR in diagnosing many diseases, often changing the course of treatment. Despite this, it is often underutilized possibly due to perceived costs, limiting patient factors and comfort, and longer examination periods compared to other imaging modalities. In this regard, we conducted a literature review using keywords “Cost-Effectiveness” and “Cardiac MRI” and selected articles from the PubMed MEDLINE database that met our inclusion and exclusion criteria to examine the cost-effectiveness of CMR. Our search result yielded 17 articles included in our review. We found that CMR can be cost-effective in quality-adjusted life years (QALYs) in select patient populations with various cardiac pathologies. Specifically, the use of CMR in coronary artery disease (CAD) patients with a pretest probability below a certain threshold may be more cost-effective compared to patients with a higher pretest probability, although its use can be limited based on geographic location, professional society guidelines, and differing reimbursement patterns. In addition, a stepwise combination of different imaging modalities, with conjunction of AHA/ACC guidelines can further enhance the cost-effectiveness of CMR.

Simultaneous multi-parametric acquisition and reconstruction techniques in cardiac magnetic resonance imaging: Basic concepts and status of clinical development

Simultaneous multi-parametric acquisition and reconstruction techniques (SMART) are gaining attention for their potential to overcome some of cardiovascular magnetic resonance imaging's (CMR) clinical limitations. The major advantages of SMART lie within their ability to simultaneously capture multiple "features" such as cardiac motion, respiratory motion, T1/T2 relaxation. This review aims to summarize the overarching theory of SMART, describing key concepts that many of these techniques share to produce co-registered, high quality CMR images in less time and with less requirements for specialized personnel. Further, this review provides an overview of the recent developments in the field of SMART by describing how they work, the parameters they can acquire, their status of clinical testing and validation, and by providing examples for how their use can improve the current state of clinical CMR workflows. Many of the SMART are in early phases of development and testing, thus larger scale, controlled trials are needed to evaluate their use in clinical setting and with different cardiac pathologies.

Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death

Sudden cardiac death (SCD) is a leading cause of mortality, comprising approximately half of all deaths from cardiovascular disease. In the US, the majority of SCD (85%) occurs in patients with ischemic cardiomyopathy (ICM) and a subset in patients with non-ischemic cardiomyopathy (NICM), who tend to be younger and whose risk of mortality is less clearly delineated than in ischemic cardiomyopathies. The conventional means of SCD risk stratification has been the determination of the ejection fraction (EF), typically via echocardiography, which is currently a means of determining candidacy for primary prevention in the form of implantable cardiac defibrillators (ICDs). Advanced cardiac imaging methods such as cardiac magnetic resonance imaging (CMR), single-photon emission computerized tomography (SPECT) and positron emission tomography (PET), and computed tomography (CT) have emerged as promising and non-invasive means of risk stratification for sudden death through their characterization of the underlying myocardial substrate that predisposes to SCD. Late gadolinium enhancement (LGE) on CMR detects myocardial scar, which can inform ICD decision-making. Overall scar burden, region-specific scar burden, and scar heterogeneity have all been studied in risk stratification. PET and SPECT are nuclear methods that determine myocardial viability and innervation, as well as inflammation. CT can be used for assessment of myocardial fat and its association with reentrant circuits. Emerging methodologies include the development of "virtual hearts" using complex electrophysiologic modeling derived from CMR to attempt to predict arrhythmic susceptibility. Recent developments have paired novel machine learning (ML) algorithms with established imaging techniques to improve predictive performance. The use of advanced imaging to augment risk stratification for sudden death is increasingly well-established and may soon have an expanded role in clinical decision-making. ML could help shift this paradigm further by advancing variable discovery and data analysis.

Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease

Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.

Definition and epidemiology of coronary microvascular disease

Ischemic heart disease remains one of the leading causes of death and disability worldwide. However, most patients referred for a noninvasive computed tomography coronary angiogram (CTA) or invasive coronary angiogram for the investigation of angina do not have obstructive coronary artery disease (CAD). Approximately two in five referred patients have coronary microvascular disease (CMD) as a primary diagnosis and, in addition, CMD also associates with CAD and myocardial disease (dual pathology). CMD underpins excess morbidity, impaired quality of life, significant health resource utilization, and adverse cardiovascular events. However, CMD often passes undiagnosed and the onward management of these patients is uncertain and heterogeneous. International standardized diagnostic criteria allow for the accurate diagnosis of CMD, ensuring an often overlooked patient population can be diagnosed and stratified for targeted medical therapy. Key to this is assessing coronary microvascular function-including coronary flow reserve, coronary microvascular resistance, and coronary microvascular spasm. This can be done by invasive methods (intracoronary temperature-pressure wire, intracoronary Doppler flow-pressure wire, intracoronary provocation testing) and non-invasive methods [positron emission tomography (PET), cardiac magnetic resonance imaging (CMR), transthoracic Doppler echocardiography (TTDE), cardiac computed tomography (CT)]. Coronary CTA is insensitive for CMD. Functional coronary angiography represents the combination of CAD imaging and invasive diagnostic procedures.

The Severity of Pectus Excavatum Defect Is Associated With Impaired Cardiopulmonary Function

BACKGROUND

Repair of pectus excavatum has cosmetic benefits, but the physiologic impact remains controversial. The aim of this study was to characterize the relationship between the degree of pectus excavatum and cardiopulmonary dysfunction seen on cardiac magnetic resonance (CMR) imaging, cardiopulmonary exercise testing (CPET), and pulmonary function testing (PFT).

METHODS

A single-center analysis of CMR, CPET, and PFT was conducted. Regression models evaluated relationships between pectus indices and the clinical end points of cardiopulmonary function.

RESULTS

Data from 345 CMRs, 261 CPETs, and 281 PFTs were analyzed. Patients were a mean age of 15.2 ± 4 years, and 81% were aged <18 years. The right ventricular ejection fraction (RVEF) was <0.50 in 16% of patients, left ventricular ejection fraction (LVEF) was <0.55 in 22%, RVEF Z-score was < -2 in 32%, and the LVEF Z-score was < -2 in 18%. CPET revealed 33% of patients had reduced aerobic fitness. PFT results were abnormal in 23.1% of patients. Adjusted analyses revealed the Haller index had significant (P < .05) inverse associations with RVEF and LVEF.

CONCLUSIONS

The severity of pectus excavatum is associated with ventricular systolic dysfunction. Pectus excavatum impacts right and left ventricular systolic function and can also impact exercise tolerance. The Haller index and correction index may be the most useful predictors of impairment.

Cardiac Imaging in Athlete's Heart: The Role of the Radiologist

Athlete’s heart (AH) is the result of morphological and functional cardiac modifications due to long-lasting athletic training. Athletes can develop very marked structural myocardial changes, which may simulate or cover unknown cardiomyopathies. The differential diagnosis between AH and cardiomyopathy is necessary to prevent the risk of catastrophic events, such as sudden cardiac death, but it can be a challenging task. The improvement of the imaging modalities and the introduction of the new technologies in cardiac magnetic resonance (CMR) and cardiac computed tomography (CCT) can allow overcoming this challenge. Therefore, the radiologist, specialized in cardiac imaging, could have a pivotal role in the differential diagnosis between structural adaptative changes observed in the AH and pathological anomalies of cardiomyopathies. In this review, we summarize the main CMR and CCT techniques to evaluate the cardiac morphology, function, and tissue characterization, and we analyze the imaging features of the AH and the key differences with the main cardiomyopathies.

Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis?

In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians and engineers in reducing the costs of exams and, at the same time, to improve image acquisition and reconstruction, thus simplifying their analysis, interpretation, and decision-making process as well. In fact, the role of AI and machine learning in cardiovascular imaging relies on evaluating images more quickly, improving their quality, nulling intraobserver and interobserver variability in their interpretation, upgrading the understanding of the stage of the disease, and providing with a personalized approach to cardiovascular care. In addition, AI algorithm could be directed toward workflow management. This article presents an overview of the existing AI literature in cardiac magnetic resonance, with its strengths and limitations, recent applications, and promising developments. We conclude that AI is very likely be used in all the various process of diagnosis routine mode for cardiac care of patients.

Clinical quantitative MRI and the need for metrology

MRI has been an essential diagnostic tool in healthcare for several decades. It offers unique insights into most tissues without the need for ionising radiation. Historically, MRI has been predominantly used qualitatively, images are formed to allow visual discrimination of tissues types and pathologies, rather than providing quantitative measurements. Increasingly, quantitative MRI (qMRI) is also finding clinical application, where images provide the basis for physical measurements of, e.g. tissue volume measures and represent aspects of tissue composition and microstructure. This article reviews some common current research and clinical applications of qMRI from the perspective of measurement science. qMRI not only offers additional information for radiologists, but also the opportunity for improved harmonisation and calibration between scanners and as such it is well-suited to large-scale investigations such as clinical trials and longitudinal studies. Realising these benefits, however, presents a new kind of technical challenge to MRI practioners. When measuring a parameter quantitatively, it is crucial that the reliability and reproducibility of the technique are well understood. Strictly speaking, a numerical result of a measurement is meaningless unless it is accompanied by a description of the associated measurement uncertainty. It is therefore necessary to produce not just estimates of physical properties in a quantitative image, but also their associated uncertainties. As the process of determining a physical property from the raw MR signal is complicated and multistep, estimation of uncertainty is challenging and there are many aspects of the MRI process that require validation. With the clinical implementation of qMRI techniques and its continued expansion, there is a clear and urgent need for metrology in this field.

Next-Generation Hardware Advances in CT: Cardiac Applications

Impending major hardware advances in cardiac CT include three areas: ultra-high-resolution (UHR) CT, photon-counting CT, and phase-contrast CT. Cardiac CT is a particularly demanding CT application that requires a high degree of temporal resolution, spatial resolution, and soft-tissue contrast in a moving structure. In this review, cardiac CT is used to highlight the strengths of these technical advances. UHR CT improves visualization of calcified and stented vessels but may result in increased noise and radiation exposure. Photon-counting CT uses multiple photon energies to reduce artifacts, improve contrast resolution, and perform material decomposition. Finally, phase-contrast CT uses x-ray refraction properties to improve spatial and soft-tissue contrast. This review describes these hardware advances in CT and their relevance to cardiovascular imaging.

Confounding factors in breast magnetic resonance fingerprinting: B 1 + , slice profile, and diffusion effects

PURPOSE

Magnetic resonance fingerprinting (MRF) offers rapid quantitative imaging but may be subject to confounding effects (CE) if these are not included in the model-based reconstruction. This study characterizes the influence of in-plane B 1 + , slice profile and diffusion effects on T₁ and T₂ estimation in the female breast at 1.5T.

METHODS

Simulations were used to predict the influence of each CE on the accuracy of MRF and to investigate the influence of electronic noise and spiral aliasing artefacts. The experimentally observed bias in regions of fibroglandular tissue (FGT) and fatty tissue (FT) was analyzed for undersampled spiral breast MRF data of 6 healthy volunteers by performing MRF reconstruction with and without a CE.

RESULTS

Theoretic analysis predicts T₁ under-/T₂ overestimation if the nominal flip angles are underestimated and inversely, T₁ under-/T₂ overestimation if omitting slice profile correction, and T₁ under-/T₂ underestimation if omitting diffusion in the signal model. Averaged over repeated signal simulations, including spiral aliasing artefacts affected precision more than accuracy. Strong in-plane B 1 + effects occurred in vivo, causing T₂ left-right inhomogeneity between both breasts. Their correction decreased the T₂ difference from 29 to 5 ms in FGT and from 29 to 9 ms in FT. Slice profile correction affected FGT T₂ most strongly, resulting in -22% smaller values. For the employed spoiler gradient strengths, diffusion did not affect the parameter maps, corresponding well with theoretic predictions.

CONCLUSION

Understanding CEs and their relative significance for an MRF sequence is important when defining an MRF signal model for accurate parameter mapping.

Native T1 mapping for assessment of the perilesional zone in metastases and benign lesions of the liver

Adjacent to hepatic metastases, liver parenchyma is often histopathologically altered even if its visual appearance on native magnetic resonance (MR) images is blunt. Yet, relaxation properties in MR imaging may show structural changes prior to visual alteration, and therefore, the aim of this study was to investigate whether T1 relaxation times in the perilesional zone differ between metastases and benign lesions. A total of 113 patients referred for MRI were included prospectively. Images were assessed for metastases, solid benign lesions and cysts, and regions-of-interest were drawn on T1 maps including the focal lesion and a close (inner perilesional zone = IPZ) and a larger perilesional zone (outer perilesional zone = OPZ). Simple ratios between these zones, as well as a gradient ratio between the IPZ and the entire perilesional zone (EPZ) were calculated. Within the collective, 44 patients had lesions of one or two entities. For metastases, the simple ratio between IPZ and OPZ as well as the mean EPZ gradient was significantly higher than for both solid benign lesions and cysts. Lesion size was not a significant covariate. We conclude, that native T1 properties of the perilesional zones differ significantly between malignant and both solid and cystic benign lesions.

Deep Learning for Quantitative Cardiac MRI

OBJECTIVE. The recent advancement of deep learning techniques has profoundly impacted research on quantitative cardiac MRI analysis. The purpose of this article is to introduce the concept of deep learning, review its current applications on quantitative cardiac MRI, and discuss its limitations and challenges. CONCLUSION. Deep learning has shown state-of-the-art performance on quantitative analysis of multiple cardiac MRI sequences and holds great promise for future use in clinical practice and scientific research.