Pulmonary blood volume measured by cardiovascular magnetic resonance: influence of pulmonary transit time methods and left atrial volume

Stress and Rest Pulmonary Transit Times Assessed by Cardiovascular Magnetic Resonance

Acquiring pulmonary circulation parameters as a potential marker of cardiopulmonary function is not new. Methods to obtain these parameters have been developed over time, with the latest being first-pass perfusion sequences in cardiovascular magnetic resonance (CMR). Even though more data on these parameters has been recently published, different nomenclature and acquisition methods are used across studies; some works even reported conflicting data. The most commonly used circulation parameters obtained using CMR include pulmonary transit time (PTT) and pulmonary transit beats (PTB). PTT is the time needed for a contrast agent (typically gadolinium-based) to circulate from the right ventricle (RV) to the left ventricle (LV). PTB is the number of cardiac cycles the process takes. Some authors also include corrected heart rate (HR) versions along with standard PTT. Besides other methods, CMR offers an option to assess stress circulation parameters, but data are minimal. This review aims to summarize the up-to-date findings and provide an overview of the latest progress on this promising, dynamically evolving topic.

Pulmonary blood volume assessment from a standard cardiac rubidium-82 imaging protocol: impact of adenosine-induced hyperemia

BACKGROUND

This study aimed to assess the feasibility of estimating the pulmonary blood volume noninvasively using standard Rubidium-82 myocardial perfusion imaging (MPI) and characterize the changes during adenosine-induced hyperemia.

METHODS

This study comprised 33 healthy volunteers (15 female, median age = 23 years), of which 25 underwent serial rest/adenosine stress Rubidium-82 MPI sessions. Mean bolus transit times (MBTT) were obtained by calculating the time delay from the Rubidium-82 bolus arrival in the pulmonary trunk to the arrival in the left myocardial atrium. Using the MBTT, in combination with stroke volume (SV) and heart rate (HR), we estimated pulmonary blood volume (PBV = (SV × HR) × MBTT). We report the empirically measured MBTT, HR, SV, and PBV, all stratified by sex [male (M) vs female (F)] as mean (SD). In addition, we report grouped repeatability measures using the within-subject repeatability coefficient.

RESULTS

Mean bolus transit times was shortened during adenosine stressing with sex-specific differences [(seconds); Rest: Female (F) = 12.4 (1.5), Male (M) = 14.8 (2.8); stress: F = 8.8 (1.7), M = 11.2 (3.0), all P ≤ 0.01]. HR and SV increased during stress MPI, with a concomitant increase in the PBV [mL]; Rest: F = 544 (98), M = 926 (105); Stress: F = 914 (182), M = 1458 (338), all P < 0.001. The following test-retest repeatability measures were observed for MBTT (Rest = 17.2%, Stress = 17.9%), HR (Rest = 9.1%, Stress = 7.5%), SV (Rest = 8.9%, Stress = 5.6%), and for PBV measures (Rest = 20.7%, Stress = 19.5%) CONCLUSION: Pulmonary blood volume can be extracted by cardiac rubidium-82 MPI with excellent test-retest reliability, both at rest and during adenosine-induced hyperemia.

Prognostic value of pulmonary transit time by cardiac magnetic resonance imaging in ST-elevation myocardial infarction

OBJECTIVES

To investigate the prognostic value of pulmonary transit time (pTT) determined by cardiac magnetic resonance (CMR) after acute ST-segment-elevation myocardial infarction (STEMI).

METHODS

Comprehensive CMR examinations were performed in 207 patients 3 days and 4 months after reperfused STEMI. Functional parameters and infarct characteristics were assessed. PTT was defined as the interval between peaks of gadolinium contrast time-intensity curves in the right and left ventricles in first-pass perfusion imaging. Cox regression models were calculated to assess the association between pTT and the occurrence of major adverse cardiac events (MACE), defined as a composite of death, re-infarction, and congestive heart failure.

RESULTS

PTT was 8.6 s at baseline and 7.8 s at the 4-month CMR. In Cox regression, baseline pTT (hazard ratio [HR]: 1.58; 95% CI: 1.12 to 2.22; p = 0.009) remained significantly associated with MACE occurrence after adjustment for left ventricular ejection fraction (LVEF) and cardiac index. The association of pTT and MACE remained significant also after adjusting for infarct size and microvascular obstruction size. In Kaplan-Meier analysis, pTT ≥ 9.6 s was associated with MACE (p < 0.001). Addition of pTT to LVEF resulted in a categorical net reclassification improvement of 0.73 (95% CI: 0.27 to 1.20; p = 0.002) and integrated discrimination improvement of 0.07 (95% CI: 0.02 to 0.13; p = 0.007).

CONCLUSIONS

After reperfused STEMI, CMR-derived pTT was associated with hard clinical events with prognostic information independent of and incremental to infarct size and LV systolic function.

KEY POINTS

• Pulmonary transit time is the duration it takes the heart to pump blood from the right chambers across lung vessels to the left chambers. • This prospective single-centre study showed inferior outcome in patients with prolonged pulmonary transit time after myocardial infarction. • Pulmonary transit time assessed by magnetic resonance imaging added incremental information to established prognostic markers.

2021-2022 state of our JCMR

In 2021, there were 136 articles published in the Journal of Cardiovascular Magnetic Resonance (JCMR), including 122 original research papers, six reviews, four technical notes, one Society for Cardiovascular Magnetic Resonance (SCMR) guideline, one SCMR position paper, one study protocol, and one obituary (Nathaniel Reichek). The volume was up 53% from 2020 (n = 89) with a corresponding 21% decrease in manuscript submissions from 435 to 345. This led to an increase in the acceptance rate from 24 to 32%. The quality of the submissions continues to be high. The 2021 JCMR Impact Factor (which is released in June 2022) markedly increased from 5.41 to 6.90 placing us in the top quartile of Society and cardiac imaging journals. Our 5 year impact factor similarly increased from 6.52 to 7.25. Fifteen years ago, the JCMR was at the forefront of medical and medical society journal migration to the Open-Access format. The Open-Access system has dramatically increased the availability and JCMR citation. Full-text article requests in 2021 approached 1.5 M!. As I have mentioned, it takes a village to run a journal. JCMR is very fortunate to have a group of very dedicated Associate Editors, Guest Editors, Journal Club Editors, and Reviewers. I thank each of them for their efforts to ensure that the review process occurs in a timely and responsible manner. These efforts have allowed the JCMR to continue as the premier journal of our field. My role, and the entire editorial process would not be possible without the ongoing high dedication and efforts of our managing editor, Jennifer Rodriguez. Her premier organizational skills have allowed for streamlining of the review process and marked improvement in our time-to-decision (see later). As I conclude my 6th and final year as your editor-in-chief, I thank you for entrusting me with the JCMR editorship and appreciate the time I have had at the helm. I am very confident that our Journal will reach new heights under the stewardship of Dr. Tim Leiner, currently at the Mayo Clinic with a seamless transition occurring as I write this in late November. I hope that you will continue to send your very best, high quality CMR manuscripts to JCMR, and that our readers will continue to look to JCMR for the very best/state-of-the-art CMR publications.

The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review

Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.