Simultaneous T1 , T2 , and T1ρ cardiac magnetic resonance fingerprinting for contrast agent-free myocardial tissue characterization

Experimental multiparametric magnetic resonance imaging characterization of iliocaval venous thrombosis pathological changes

OBJECTIVE

Iliocaval thrombotic obstruction is a challenging condition, especially because thrombus age and corresponding pathological remodeling at presentation are unknown, which directly impacts management. Our aim was to assess the ability of magnetic resonance imaging (MRI) in determining age thresholds of experimentally created inferior vena cava (IVC) thrombosis in pigs.

METHODS

We used a previously described swine model of IVC thrombosis. The animals underwent MRI at baseline, immediately after thrombosis creation, and after a follow-up period extending from 2 to 28 days. Thirteen pigs were divided into three groups according to disease chronicity: acute group (AG; n = 5), subacute group (SAG; n = 4), and chronic group (CG; n = 4), with a mean thrombosis age of 6.4 ± 2.5 days, 15.7 ± 2.8 days, and 28 ± 5.7 days, respectively. A T1-weighted volumetric interpolated breath-hold examination sequence was used to anatomically delineate IVC thrombus as a region of interest. Three other MRI sequences were used to assess the thrombus signal.

RESULTS

The Kruskal-Wallis test showed a statistically significant difference in T1 relaxation times after contrast injection (P = .026) between the three groups of chronicity. The AG (360.2 ± 102.5 ms) was significantly different from the CG (336.7 ± 55.2 ms; P = .003), and the SAG (354.1 ± 89.7 ms) was significantly different from the AG (P = .027). There was a statistically significant difference in native T2 relaxation times (P = .038) between the three groups. The AG (160 ± 86.7 ms) was significantly different from the SAG (142.3 ± 55.4 ms; P = .027), and the SAG was significantly different from the CG (178.4 ± 11.7 ms; P = .004).

CONCLUSIONS

This study highlighted MRI characteristics in a swine model that might have the potential to significantly differentiate subacute and chronic stages from an acute stage of deep vein thrombosis in humans. Further clinical studies in humans are warranted.

CLINICAL RELEVANCE

In addition to providing a better understanding of venous thrombosis remodeling over time, magnetic resonance imaging has the potential to be a tool that could allow us to characterize the composition of venous thrombus over an interval, allowing for a refined analysis of the local evolution of venous thrombosis. We propose a noninvasive and innovative method to characterize different thresholds of chronicity with magnetic resonance imaging features of central deep vein thrombosis of the inferior vena cava experimentally obtained using a totally endovascular in vivo swine model, mimicking human pathophysiology. Being able to determine these features noninvasively is critical for vascular specialists when it comes to choosing between fibrinolytic therapy, percutaneous thrombectomy, or surgical management.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

Emerging Trends in Magnetic Resonance Fingerprinting for Quantitative Biomedical Imaging Applications: A Review

Magnetic resonance imaging (MRI) stands as a vital medical imaging technique, renowned for its ability to offer high-resolution images of the human body with remarkable soft-tissue contrast. This enables healthcare professionals to gain valuable insights into various aspects of the human body, including morphology, structural integrity, and physiological processes. Quantitative imaging provides compositional measurements of the human body, but, currently, either it takes a long scan time or is limited to low spatial resolutions. Undersampled k-space data acquisitions have significantly helped to reduce MRI scan time, while compressed sensing (CS) and deep learning (DL) reconstructions have mitigated the associated undersampling artifacts. Alternatively, magnetic resonance fingerprinting (MRF) provides an efficient and versatile framework to acquire and quantify multiple tissue properties simultaneously from a single fast MRI scan. The MRF framework involves four key aspects: (1) pulse sequence design; (2) rapid (undersampled) data acquisition; (3) encoding of tissue properties in MR signal evolutions or fingerprints; and (4) simultaneous recovery of multiple quantitative spatial maps. This paper provides an extensive literature review of the MRF framework, addressing the trends associated with these four key aspects. There are specific challenges in MRF for all ranges of magnetic field strengths and all body parts, which can present opportunities for further investigation. We aim to review the best practices in each key aspect of MRF, as well as for different applications, such as cardiac, brain, and musculoskeletal imaging, among others. A comprehensive review of these applications will enable us to assess future trends and their implications for the translation of MRF into these biomedical imaging applications.

Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents

Gadolinium-based contrast agents (GBCA) are essential for diagnostic MRI examinations. GBCA are only used in small quantities on a per-patient basis; however, the acquisition of contrast-enhanced MRI examinations worldwide results in the use of many thousands of litres of GBCA per year. Data shows that these GBCA are present in sewage water, surface water, and drinking water in many regions of the world. Therefore, there is growing concern regarding the environmental impact of GBCA because of their ubiquitous presence in the aquatic environment. To address the problem of GBCA in the water system as a whole, collaboration is necessary between all stakeholders, including the producers of GBCA, medical professionals and importantly, the consumers of drinking water, i.e. the patients. This paper aims to make healthcare professionals aware of the opportunity to take the lead in making informed decisions about the use of GBCA and provides an overview of the different options for action.In this paper, we first provide a summary on the metabolism and clinical use of GBCA, then the environmental fate and observations of GBCA, followed by measures to reduce the use of GBCA. The environmental impact of GBCA can be reduced by (1) measures focusing on the application of GBCA by means of weight-based contrast volume reduction, GBCA with higher relaxivity per mmol of Gd, contrast-enhancing sequences, and post-processing; and (2) measures that reduce the waste of GBCA, including the use of bulk packaging and collecting residues of GBCA at the point of application.Critical relevance statement This review aims to make healthcare professionals aware of the environmental impact of GBCA and the opportunity for them to take the lead in making informed decisions about GBCA use and the different options to reduce its environmental burden.Key points• Gadolinium-based contrast agents are found in sources of drinking water and constitute an environmental risk.• Radiologists have a wide spectrum of options to reduce GBCA use without compromising diagnostic quality.• Radiology can become more sustainable by adopting such measures in clinical practice.

The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1)

Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR. To this end, the vision of each is described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.

Cardiac MR Fingerprinting: Overview, Technical Developments, and Applications

Cardiovascular magnetic resonance (CMR) is an established imaging modality with proven utility in assessing cardiovascular diseases. The ability of CMR to characterize myocardial tissue using T1 - and T2 -weighted imaging, parametric mapping, and late gadolinium enhancement has allowed for the non-invasive identification of specific pathologies not previously possible with modalities like echocardiography. However, CMR examinations are lengthy and technically complex, requiring multiple pulse sequences and different anatomical planes to comprehensively assess myocardial structure, function, and tissue composition. To increase the overall impact of this modality, there is a need to simplify and shorten CMR exams to improve access and efficiency, while also providing reproducible quantitative measurements. Multiparametric MRI techniques that measure multiple tissue properties offer one potential solution to this problem. This review provides an in-depth look at one such multiparametric approach, cardiac magnetic resonance fingerprinting (MRF). The article is structured as follows. First, a brief review of single-parametric and (non-Fingerprinting) multiparametric CMR mapping techniques is presented. Second, a general overview of cardiac MRF is provided covering pulse sequence implementation, dictionary generation, fast k-space sampling methods, and pattern recognition. Third, recent technical advances in cardiac MRF are covered spanning a variety of topics, including simultaneous multislice and 3D sampling, motion correction algorithms, cine MRF, synthetic multicontrast imaging, extensions to measure additional clinically important tissue properties (proton density fat fraction, T2 *, and T1ρ ), and deep learning methods for image reconstruction and parameter estimation. The last section will discuss potential clinical applications, concluding with a perspective on how multiparametric techniques like MRF may enable streamlined CMR protocols. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Free-breathing simultaneous native myocardial T1, T2 and T1ρ mapping with Cartesian acquisition and dictionary matching

BACKGROUND

T1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition.

METHODS

The proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases.

RESULTS

The phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients.

CONCLUSION

The proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.

Robust cardiac T 1 ρ $$ {\mathrm{T}}_{1_{\boldsymbol{\rho}}} $$ mapping at 3T using adiabatic spin-lock preparations

PURPOSE

The aim of this study is to develop and optimize an adiabaticT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ (T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ ) mapping method for robust quantification of spin-lock (SL) relaxation in the myocardium at 3T.

METHODS

Adiabatic SL (aSL) preparations were optimized for resilience againstB 0 $$ {\mathrm{B}}_0 $$ andB 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities using Bloch simulations. OptimizedB 0 $$ {\mathrm{B}}_0 $$ -aSL, Bal-aSL andB 1 $$ {\mathrm{B}}_1 $$ -aSL modules, each compensating for different inhomogeneities, were first validated in phantom and human calf. MyocardialT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ mapping was performed using a single breath-hold cardiac-triggered bSSFP-based sequence. Then, optimizedT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ preparations were compared to each other and to conventional SL-preparedT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ maps (RefSL) in phantoms to assess repeatability, and in 13 healthy subjects to investigate image quality, precision, reproducibility and intersubject variability. Finally, aSL and RefSL sequences were tested on six patients with known or suspected cardiovascular disease and compared with LGE,T 1 $$ {\mathrm{T}}_1 $$ , and ECV mapping.

RESULTS

The highestT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ preparation efficiency was obtained in simulations for modules comprising 2 HS pulses of 30 ms each. In vivoT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ maps yielded significantly higher quality than RefSL maps. Average myocardialT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ values were 183.28± $$ \pm $$ 25.53 ms, compared with 38.21± $$ \pm $$ 14.37 ms RefSL-preparedT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ .T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ maps showed a significant improvement in precision (avg. 14.47± $$ \pm $$ 3.71% aSL, 37.61± $$ \pm $$ 19.42% RefSL, p < 0.01) and reproducibility (avg. 4.64± $$ \pm $$ 2.18% aSL, 47.39± $$ \pm $$ 12.06% RefSL, p < 0.0001), with decreased inter-subject variability (avg. 8.76± $$ \pm $$ 3.65% aSL, 51.90± $$ \pm $$ 15.27% RefSL, p < 0.0001). Among aSL preparations,B 0 $$ {\mathrm{B}}_0 $$ -aSL achieved the better inter-subject variability. In patients,B 1 $$ {\mathrm{B}}_1 $$ -aSL preparations showed the best artifact resilience among the adiabatic preparations.T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ times show focal alteration colocalized with areas of hyper-enhancement in the LGE images.

CONCLUSION

Adiabatic preparations enable robust in vivo quantification of myocardial SL relaxation times at 3T.

Artificial Intelligence to Reduce or Eliminate the Need for Gadolinium-Based Contrast Agents in Brain and Cardiac MRI: A Literature Review

ABSTRACT

Brain and cardiac MRIs are fundamental noninvasive imaging tools, which can provide important clinical information and can be performed without or with gadolinium-based contrast agents (GBCAs), depending on the clinical indication. It is currently a topic of debate whether it would be feasible to extract information such as standard gadolinium-enhanced MRI while injecting either less or no GBCAs. Artificial intelligence (AI) is a great source of innovation in medical imaging and has been explored as a method to synthesize virtual contrast MR images, potentially yielding similar diagnostic performance without the need to administer GBCAs. If possible, there would be significant benefits, including reduction of costs, acquisition time, and environmental impact with respect to conventional contrast-enhanced MRI examinations. Given its promise, we believe additional research is needed to increase the evidence to make these AI solutions feasible, reliable, and robust enough to be integrated into the clinical framework. Here, we review recent AI studies aimed at reducing or replacing gadolinium in brain and cardiac imaging while maintaining diagnostic image quality.

Magnetic Resonance Fingerprinting: A Review of Clinical Applications

ABSTRACT

Magnetic resonance fingerprinting (MRF) is an approach to quantitative magnetic resonance imaging that allows for efficient simultaneous measurements of multiple tissue properties, which are then used to create accurate and reproducible quantitative maps of these properties. As the technique has gained popularity, the extent of preclinical and clinical applications has vastly increased. The goal of this review is to provide an overview of currently investigated preclinical and clinical applications of MRF, as well as future directions. Topics covered include MRF in neuroimaging, neurovascular, prostate, liver, kidney, breast, abdominal quantitative imaging, cardiac, and musculoskeletal applications.

Cardiac Magnetic Resonance Fingerprinting: Potential Clinical Applications

PURPOSE OF REVIEW

Cardiac magnetic resonance fingerprinting (cMRF) has developed as a technique for rapid, multi-parametric tissue property mapping that has potential to both improve cardiac MRI exam efficiency and expand the information captured. In this review, we describe the cMRF technique, summarize technical developments and in vivo reports, and highlight potential clinical applications.

RECENT FINDINGS

Technical developments in cMRF continue to progress rapidly, including motion compensated reconstruction, additional tissue property quantification, signal time course analysis, and synthetic LGE image generation. Such technical developments can enable simplified CMR protocols by combining multiple evaluations into a single protocol and reducing the number of breath-held scans. cMRF continues to be reported for use in a range of pathologies; however barriers to clinical implementation remain. Technical developments are described in this review, followed by a focus on potential clinical applications that they may support. Clinical translation of cMRF could shorten protocols, improve CMR accessibility, and provide additional information as compared to conventional cardiac parametric mapping methods. Current needs for clinical implementation are discussed, as well as how those needs may be met in order to bring cMRF from its current research setting to become a viable tool for patient care.

Quantitative MRI in cardiometabolic disease: From conventional cardiac and liver tissue mapping techniques to multi-parametric approaches

Cardiometabolic disease refers to the spectrum of chronic conditions that include diabetes, hypertension, atheromatosis, non-alcoholic fatty liver disease, and their long-term impact on cardiovascular health. Histological studies have confirmed several modifications at the tissue level in cardiometabolic disease. Recently, quantitative MR methods have enabled non-invasive myocardial and liver tissue characterization. MR relaxation mapping techniques such as T₁, T_1ρ, T₂ and T₂* provide a pixel-by-pixel representation of the corresponding tissue specific relaxation times, which have been shown to correlate with fibrosis, altered tissue perfusion, oedema and iron levels. Proton density fat fraction mapping approaches allow measurement of lipid tissue in the organ of interest. Several studies have demonstrated their utility as early diagnostic biomarkers and their potential to bear prognostic implications. Conventionally, the quantification of these parameters by MRI relies on the acquisition of sequential scans, encoding and mapping only one parameter per scan. However, this methodology is time inefficient and suffers from the confounding effects of the relaxation parameters in each single map, limiting wider clinical and research applications. To address these limitations, several novel approaches have been proposed that encode multiple tissue parameters simultaneously, providing co-registered multiparametric information of the tissues of interest. This review aims to describe the multi-faceted myocardial and hepatic tissue alterations in cardiometabolic disease and to motivate the application of relaxometry and proton-density cardiac and liver tissue mapping techniques. Current approaches in myocardial and liver tissue characterization as well as latest technical developments in multiparametric quantitative MRI are included. Limitations and challenges of these novel approaches, and recommendations to facilitate clinical validation are also discussed.

Imaging Methods: Magnetic Resonance Imaging

Myocardial inflammation occurs following activation of the cardiac immune system, producing characteristic changes in the myocardial tissue. Cardiovascular magnetic resonance is the non-invasive imaging gold standard for myocardial tissue characterization, and is able to detect image signal changes that may occur resulting from inflammation, including edema, hyperemia, capillary leak, necrosis, and fibrosis. Conventional cardiovascular magnetic resonance for the detection of myocardial inflammation and its sequela include T2-weighted imaging, parametric T1- and T2-mapping, and gadolinium-based contrast-enhanced imaging. Emerging techniques seek to image several parameters simultaneously for myocardial tissue characterization, and to depict subtle immune-mediated changes, such as immune cell activity in the myocardium and cardiac cell metabolism. This review article outlines the underlying principles of current and emerging cardiovascular magnetic resonance methods for imaging myocardial inflammation.

3D model-based super-resolution motion-corrected cardiac T1 mapping

Objective. To provide 3D high-resolution cardiac T1 maps using model-based super-resolution reconstruction (SRR).Approach. Due to signal-to-noise ratio limitations and the motion of the heart during imaging, often 2D T1 maps with only low through-plane resolution (i.e. slice thickness of 6-8 mm) can be obtained. Here, a model-based SRR approach is presented, which combines multiple stacks of 2D acquisitions with 6-8 mm slice thickness and generates 3D high-resolution T1 maps with a slice thickness of 1.5-2 mm. Every stack was acquired in a different breath hold (BH) and any misalignment between BH was corrected retrospectively. The novelty of the proposed approach is the BH correction and the application of model-based SRR on cardiac T1 Mapping. The proposed approach was evaluated in numerical simulations and phantom experiments and demonstrated in four healthy subjects.Main results. Alignment of BH states was essential for SRR even in healthy volunteers. In simulations, respiratory motion could be estimated with an RMS error of 0.18 ± 0.28 mm. SRR improved the visualization of small structures. High accuracy and precision (average standard deviation of 69.62 ms) of the T1 values was ensured by SRR while the detectability of small structures increased by 40%.Significance. The proposed SRR approach provided T1 maps with high in-plane and high through-plane resolution (1.3 × 1.3 × 1.5-2 mm³). The approach led to improvements in the visualization of small structures and precise T1 values.

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.

Artificial intelligence in cardiac magnetic resonance fingerprinting

Magnetic resonance fingerprinting (MRF) is a fast MRI-based technique that allows for multiparametric quantitative characterization of the tissues of interest in a single acquisition. In particular, it has gained attention in the field of cardiac imaging due to its ability to provide simultaneous and co-registered myocardial T₁ and T₂ mapping in a single breath-held cardiac MRF scan, in addition to other parameters. Initial results in small healthy subject groups and clinical studies have demonstrated the feasibility and potential of MRF imaging. Ongoing research is being conducted to improve the accuracy, efficiency, and robustness of cardiac MRF. However, these improvements usually increase the complexity of image reconstruction and dictionary generation and introduce the need for sequence optimization. Each of these steps increase the computational demand and processing time of MRF. The latest advances in artificial intelligence (AI), including progress in deep learning and the development of neural networks for MRI, now present an opportunity to efficiently address these issues. Artificial intelligence can be used to optimize candidate sequences and reduce the memory demand and computational time required for reconstruction and post-processing. Recently, proposed machine learning-based approaches have been shown to reduce dictionary generation and reconstruction times by several orders of magnitude. Such applications of AI should help to remove these bottlenecks and speed up cardiac MRF, improving its practical utility and allowing for its potential inclusion in clinical routine. This review aims to summarize the latest developments in artificial intelligence applied to cardiac MRF. Particularly, we focus on the application of machine learning at different steps of the MRF process, such as sequence optimization, dictionary generation and image reconstruction.

Highlights of the Virtual Society for Cardiovascular Magnetic Resonance 2022 Scientific Conference: CMR: improving cardiovascular care around the world

The 25th Society for Cardiovascular Magnetic Resonance (SCMR) Annual Scientific Sessions saw 1524 registered participants from more than 50 countries attending the meeting virtually. Supporting the theme "CMR: Improving Cardiovascular Care Around the World", the meeting included 179 invited talks, 52 sessions including 3 plenary sessions, 2 keynote talks, and a total of 93 cases and 416 posters. The sessions were designed so as to showcase the multifaceted role of cardiovascular magnetic resonance (CMR) in identifying and prognosticating various myocardial pathologies. Additionally, various social networking sessions as well as fun activities were organized. The major areas of focus for the future are likely to be rapid efficient and high value CMR exams, automated and quantitative acquisition and post-processing using artificial intelligence and machine learning, multi-contrast imaging and advanced vascular imaging including 4D flow.

T2 mapping in myocardial disease: a comprehensive review

Cardiovascular magnetic resonance (CMR) is considered the gold standard imaging modality for myocardial tissue characterization. Elevated transverse relaxation time (T2) is specific for increased myocardial water content, increased free water, and is used as an index of myocardial edema. The strengths of quantitative T2 mapping lie in the accurate characterization of myocardial edema, and the early detection of reversible myocardial disease without the use of contrast agents or ionizing radiation. Quantitative T2 mapping overcomes the limitations of T2-weighted imaging for reliable assessment of diffuse myocardial edema and can be used to diagnose, stage, and monitor myocardial injury. Strong evidence supports the clinical use of T2 mapping in acute myocardial infarction, myocarditis, heart transplant rejection, and dilated cardiomyopathy. Accumulating data support the utility of T2 mapping for the assessment of other cardiomyopathies, rheumatologic conditions with cardiac involvement, and monitoring for cancer therapy-related cardiac injury. Importantly, elevated T2 relaxation time may be the first sign of myocardial injury in many diseases and oftentimes precedes symptoms, changes in ejection fraction, and irreversible myocardial remodeling. This comprehensive review discusses the technical considerations and clinical roles of myocardial T2 mapping with an emphasis on expanding the impact of this unique, noninvasive tissue parameter.

Quantification correction for free-breathing myocardial T1ρ mapping in mice using a recursively derived description of a T1ρ* relaxation pathway

BACKGROUND

Fast and accurate T1ρ mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T1ρ relaxation pathway. In this study, we present an improved quantification method for T1ρ using a newly derived formalism of a T1ρ* relaxation pathway.

METHODS

The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T1ρ mapping in mice. Here, the impact of the breath dependent spin recovery time Trec on the quantification results was examined in detail.

RESULTS

Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from - 7.4% to - 0.97%. In vivo, a correlation of uncorrected T1ρ with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T1ρ values in different animals was reduced by at least 39%.

CONCLUSION

The suggested quantification formalism enables fast and precise myocardial T1ρ quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results.