Iodine-based extracellular volume for evaluating myocardial status in patients undergoing percutaneous coronary intervention for acute myocardial infarction by using dual-layer spectral detector computed tomography: a comparison study with magnetic resonance

Introduction

Coronary artery disease (CAD) can cause a heart attack or stroke by decreasing blood flow through the arteries (1). Percutaneous coronary intervention (PCI) has been successfully used to treat and improve CAD symptoms in patients, and is less invasive, lower cost, and associated with shorter hospital stays than is surgical intervention (2). However, there are various perioperative complications that can affect the myocardial prognosis after PCI, including coronary perforation, abrupt vessel closure, stent deformation, wire fracture, and, in particular, chronic myocardial ischemia caused by in-stent restenosis (3). It is therefore critical to accurately assess myocardial status postoperatively to support the development of treatment plans for such complications.

Previous studies have demonstrated that myocardial extracellular volume (ECV) based on late gadolinium-enhanced cardiovascular magnetic resonance (CMR) imaging can quantitatively reflect myocardial infarction (4). It has also been proven to be an effective quantitative parameter for predicting long-term cardiac functional recovery and adverse left ventricular remodeling events (5,6). However, ECV may be difficult to obtain in patients who have undergone PCI, due to poor-quality CMR images resulting from the long scanning time. Currently, cardiac coronary computed tomography angiography (CCTA) is widely used to evaluate arterial stenosis before and after PCI (7). Studies have revealed that ECV can also be calculated using data obtained from late iodine-enhanced (LIE) scans after CCTA to assess the myocardial condition (8,9). The ECV is calculated using the difference in the myocardial computed tomography (CT) values obtained before and after contrast enhancement scanning. However, the accuracy of the results can be affected by the position registration of the myocardium between the pre- and post-enhancement scans (8).

The emergence of spectral CT has made it possible to perform material decomposition with high- and low-energy X-rays, which enables iodine quantitation and improves tissue differentiation in the myocardium (10-12). Dual-layer spectral detector CT (SDCT) permits the simultaneous detection of high- and low-energy X-rays. The detector’s unique design allows for spectral reconstruction in the projection domain and enables spectral data acquisition for every scan. One important application of this technique is the calculation of myocardial ECV values. To overcome registration issues in calculating the myocardial ECV, an iodine-based method that only requires data from the LIE phase has been proposed (13). The present study investigated the feasibility of using iodine-based ECV obtained from SDCT to evaluate the myocardium in patients after PCI. We present the following article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1103/rc).

Methods

Study population

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This single-center prospective cohort study was approved by the Institutional Review Board of Nanjing Drum Tower Hospital (No. 2018-046-01), and written informed consent was obtained from all participants.

Between September 2019 and June 2020, 21 patients with coronary heart disease who underwent PCI for the treatment of acute myocardial infarction (AMI) in Nanjing Drum Tower Hospital were prospectively enrolled in the study (Figure 1). To be eligible for inclusion in the study, patients needed to have: (I) a diagnosis of AMI based on biochemistry and electrocardiogram (ECG) changes within 12 hours after the onset of clinical symptoms, such as angina; (II) at least one unobstructed coronary artery (to permit comparison between the infarcted and normal myocardium); and (III) postoperative New York Heart Association (NYHA) functional class I or II. The diagnostic criteria for AMI in this study were a rise and/or fall in serially tested cardiac biomarkers (preferably cardiac troponins) with at least one value above the 99^th percentile of the upper reference limit, in addition to symptoms of ischemia, new changes on ECG, imaging evidence of a new loss of viable myocardium or new regional wall motion abnormalities, or the identification of an intracoronary thrombus. Patients were excluded from the study if they had undergone coronary artery bypass grafting, had arrhythmia during cardiac CT or CMR follow-up examinations, or had cardiomyopathy (e.g., dilated cardiomyopathy, myocardial hypertrophy), valvular heart disease (e.g., mitral insufficiency, severe tricuspid regurgitation), hemodynamic instability, renal insufficiency, or contraindications for MR or CT. Patients with SDCT and CMR images of poor quality, which was defined as an image quality (IQ) score <2, were also excluded (IQ was assessed using a 4-point scale, on which 1= poor and 4= excellent) (1). Follow-up CMR and CT examinations were performed within 24 hours of each other at 12 weeks after PCI. Hematocrit (Hct) levels were measured in venous blood samples obtained within 1 hour before each examination.

Figure 1 Study flowchart. PCI, percutaneous coronary intervention; AMI, acute myocardial infarction; NYHA, New York Heart Association; CT, computed tomography; MR, magnetic resonance.

MRI protocol

Participants underwent CMR examination on a 3-T MR scanner (Ingenia CX 3.0T; Philips Healthcare, Best, The Netherlands) with a 32-element phased-array body coil. To ensure good IQ and avoid motion artifacts, CMR images were collected with the assistance of ECG and respiratory-gated technology.

The CMR parameters were as follows:

• Steady state free-precession sequence: repetition time (TR), 3.2 ms; echo time (TE), 1.6 ms; flip angle, 45°; slice thickness, 8 mm.
• T₂-weighted short-tau triple inversion recovery sequence: TR, 2 R-R intervals; TE, 90 ms; flip angle, 90°; slice thickness, 8 mm.
• Breath-hold Modified Look-Locker inversion recovery (MOLLI) sequence: TR, 8.0 ms; TE, 3.5 ms; flip angle, 45°; slice thickness, 8 mm; in-plane resolution, 2.78×2.92×10.0 mm³.

To obtain T₁-weighted maps of the myocardium and to calculate ECV, T₁-weighted mapping was performed using the ECG-gated MOLLI sequence before and at 10 minutes after intravenous administration of 0.2 mmol/kg MR contrast agent (Magnevist; Bayer Healthcare, Berlin, Germany) in three short-axis slices, including at the basal, midventricular, and apical levels.

CT protocol

All CT examinations were performed using an SDCT (IQon Spectral CT; Philips, Best, The Netherlands). Patients with sinus tachycardia were administered metoprolol tartrate tablets (12.5 mg, sublingual; AstraZeneca, London, UK). To ensure that the whole heart was scanned, the scanning range was set from 1 cm below the carina to the upper boundary of the diaphragm. First, CCTA was performed with iodine contrast (Ioversol; 350 mg/mL iodine; Hengrui, Jiangsu, China), administered at a dose of 0.9–1.0 mg/kg in each patient at an injection flow rate of 4.5–5.0 mL/s. At 8 minutes after the contrast injection, LIE of a prospective ECG-triggered axial scan, with the mid-diastolic phase of the left ventricle (LV) selected as the trigger time point, was performed. The parameters were set as follows: tube voltage, 120 kV; tube current, 150 mA; collimator width, 64×0.625 mm; tube rotation speed, 0.27 s/cycle; reconstruction field of view, 350 mm. The radiation dose of the LIE phase scan was expressed as the effective dose (ED; mSv) and was calculated using the following equation (14):

ED(mSv)=DLP×0.14[1]

where DLP (mGy·cm) is the dose–length product.

Image analysis

All CMR and CT images were independently reviewed by two radiologists (WC and DM, with 5 and 8 years’ experience in cardiovascular imaging, respectively), who were blinded to the participants’ clinical histories and other diagnostic test results. In cases of discrepancy, a decision was reached by consensus.

IQ evaluation

The IQ of both CMR and CT images was evaluated. For CMR images, the delineation of the myocardium and the luminal margin was considered, as well as the presence of motion, flow, and pulsation artifacts. For CT images, due to the high scanning speed, attention was paid to the continuity of the coronary arteries after reconstruction. A 4-point scale was used to grade IQ, as follows: the score of 1 indicated poor quality: the myocardial margin was not identifiable. A score of 2 indicated adequate quality: the myocardial margin was visible, but the compositional substructure was partially obscured. A score of 3 indicated good quality: motion and flow artifacts were minimal, and the myocardial margin was clearly defined. A score of 4 indicated excellent quality: no artifacts were seen, and the myocardial margin were depicted in detail. Images with an IQ score <2 were excluded from the analysis (15).

Calculation of CT- and CMR-based ECV

The myocardial ECV was quantified by either CT (ECV_CT) or MR (ECV_MR).

Iodine mapping was derived from spectral-based images generated by SDCT using a commercially available three-dimensional workstation (IntelliSpacePortal version 10; Philips Healthcare) (16,17). After the iodine map had been generated, the short-axis plane of the LV was reconstructed in accordance with the positional and geometric parameters of T₁-weighted mapping for further analysis. The myocardium was then manually divided into 16 segments according to the American Heart Association (AHA) model, and the ECV_CT map was generated (Figure 2). To obtain the iodine value of the blood pool, a region of interest of at least 150 mm² was placed in the LV cavity. The ECV_CT (%) value of each segment was calculated using the following equation, taking Hct into consideration:

ECV=(1−Hct)×Iodinemyocardium/Iodinebloodpool[2]

Figure 2 Measurement of myocardial ECV_CT, based on the AHA 16-segment model, in a patient (a 67-year-old man with inferior myocardial infarction) who underwent percutaneous coronary intervention. (A-C) Iodine maps obtained with delayed contrast-enhanced dual-layer spectral detector computed tomography. The regions of interest (green) are the areas for which ECV_CT was calculated based on AHA left ventricular segmentation. (D,E) Corresponding ECV_CT maps showing that ECV_CT values of the infarcted myocardium are higher (red). ECV_CT, extracellular volume quantified by computed tomography; AHA, American Heart Association.

ECV_MR was calculated using pre- and post-enhancement T₁-weighted mapping with cvi42 version 5.1 software (Circle Cardiovascular Imaging, Calgary, Canada). T₁-weighted maps were acquired from the MOLLI sequence. Endocardial and epicardial contours were manually drawn on all slices of T₁-weighted maps obtained in the end-diastolic phase in the LV, and the T₁-weighted blood pool values were calculated based on the LV cavity drawn manually. The ECV_MR map was then generated automatically after entry of the Hct value into the software (Figure 3A-3C). Finally, polar maps were generated according to the AHA’s 16-segment model, and the ECV_MR value of each segment was determined (Figure 3D). Myocardial ECV_MR (%) was calculated using the following equation (18):

ECVMR=R1myocardiumpost−R1myocardiumpreR1bloodpoolpost−R1bloodpoolpre×(100−Hct)[3]

Figure 3 Measurement of myocardial ECV_MR, based on the AHA 16-segment model in the same patient as in Figure 2. (A-C) Extracellular volume mapping generated from cardiac magnetic resonance imaging showing higher ECV_MR values for the infarcted myocardium (red). (D) Polar mapping based on the AHA 16-segment model and ECV_MR values for each segment. The regions of infarcted myocardium shown by ECV_CT mapping are consistent with the those shown in the corresponding ECV_MR mapping. ECV_MR, extracellular volume quantified by cardiac magnetic resonance imaging; ECV_CT, extracellular volume quantified by computed tomography; AHA, American Heart Association.

where R1 is equal to 1/T1 value (T1 value of myocardial tissue was measured by T1-weighted mapping), R1_{myocardium post} is 1/T1 value of myocardial tissue obtained after the contrast was injected, R1_{myocardium pre} is 1/T1 value of myocardial tissue obtained before the contrast was injected, R1_{blood pool post} is 1/T1 value of blood pool obtained after the contrast was injected, and R1_{blood pre} is 1/T1 value of blood pool obtained before the contrast was injected.

Statistical analysis

Continuous variables were expressed as the mean ± SD, and those with a normal distribution were compared using paired t-tests. Categorical variables were expressed as numbers and percentages. Interobserver variability in the evaluation of ECV_MR, ECV_CT, and IQ was assessed using intraclass correlation coefficients (ICC). Correlations between ECV_MR and ECV_CT were tested using Pearson’s correlation coefficient. Bland–Altman plots were used to analyze the consistency of ECV_MR and ECV_CT. Two-sided P<0.05 was considered statistically significant. Statistical analyses were performed using SPSS version 20.0 (IBM Corp., Armonk, NY, USA).

Results

Patient characteristics and IQ scores

A total of 21 patients with planned follow-up examinations 3 months after PCI were enrolled in the present study; of them, 19 patients successfully completed cardiac CT and CMR scans, while two patients failed to undergo CMR scans because of arrhythmia. None of the 21 patients experienced an adverse event. There was good interobserver agreement in the scoring of IQ for CT and CMR images, with ICCs of 0.93 and 0.92, respectively. Sixteen patients with an IQ score ≥2 were enrolled in the final analysis (2 patients were excluded because of poor IQ caused by breathing artifacts on CMR images, and 1 patient was excluded because of a step artifact of the coronary arteries on CT images). The mean IQ scores for CT and CMR images were 3.81±0.40 and 3.25±0.58, respectively. Cardiovascular MR images were graded as excellent in 5 cases, good in 10, and moderate in 1, whereas CT images were graded as excellent in 13 cases and good in 3. The mean age of the 16 patients (13 males; 3 females) was 57.31±9.56 years. Detailed demographic characteristics are presented in Table 1.

Correlation analysis of ECVCT and ECVMR

According to the AHA’s 16-segment model, 256 LV segments were analyzed, and the ECV_CT and ECV_MR values were calculated for each segment.

There was good interobserver agreement for ECV_CT (ICC =0.85) and ECV_MR (ICC =0.83).

The mean ECV_CT was 35.93%±9.73%, which was higher than the mean ECV_MR (33.89%±7.51%; P<0.01). There was a good correlation between ECV_CT and ECV_MR (r=0.79, P<0.001; Figure 4). The Bland–Altman plot showed a difference of 2.04% (95% CI: –9.56%, 13.64%) between the ECV_CT and ECV_MR values (Figure 5).

Figure 4 The correlation between ECV_CT and cardiac ECV_MR (r=0.79, P<0.001). ECV_CT, extracellular volume quantified by computed tomography; ECV_MR, extracellular volume quantified by cardiac magnetic resonance imaging.

Figure 5 Bland–Altman plot comparing ECV_CT and that quantified by cardiac ECV_MR, showing a difference of 2.04% (95% CI: –9.56%, 13.64%). ECV_CT, extracellular volume quantified by computed tomography; ECV_MR, extracellular volume quantified by cardiac magnetic resonance imaging.

Discussion

In this study, the ECV of 256 myocardial segments in patients after PCI was calculated using both iodine and T₁-weighted mapping. There was good correlation between ECV_CT and ECV_MR, with the mean difference being 2.04%.

Although the usefulness of ECV based on cardiac CT has been reported in patients with Takotsubo cardiomyopathy and cardiotoxicity (19,20), the present study is the first to use an iodine-based ECV method to evaluate myocardial status in patients after PCI. Like the gadolinium-containing contrast agent used in CMR, the iodine-containing contrast used in CT examinations could also be an extracellular tracer, and myocardial iodine concentrations in the equilibrium phase could be used to calculate ECV (18). Our results are in line with those of previous studies. In a comparative study of animal and human tissues, Jablonowski et al. (21) reported that ECV_CT could predict the degree of myocardial injury. These authors inferred that ECV_CT may detect microinfarctions caused by microembolization and segmental infarction caused by coronary artery occlusion (21). In a study that compared ECV_CT and ECV_MR between patients with heart failure and healthy volunteers, preliminary results indicated that there was a good correlation between ECV_CT and ECV_MR (8), and the method was optimized in follow-up study (8,9). The present study also found a good correlation between iodine-based ECV (ECV_CT) and ECV derived from CMR imaging (ECV_MR), with only a small difference observed between them. Therefore, for evaluating myocardial recovery in patients after PCI, iodine-based ECV from SDCT is a feasible substitution for CMR-derived ECV.

Currently, CMR imaging is a widely proposed non-invasive method for myocardial evaluation. However, CMR scans may fail, or the IQ may be affected by respiration artifacts due to prolonged scanning. In the present study, CMR scans failed in 2 patients with arrhythmia, and another 2 patients were excluded because of poor IQ; however, all cardiac CT scans were completed successfully, and only 1 patient was excluded because of discontinuation of the coronary arteries. In contrast with ECV_MR, iodine-based ECV_CT only required additional LIE scans after standard CCTA scans, which could save time and costs for patients. Moreover, ECV_MR requires pre- and postcontrast enhancement scans, which may result in mage registration errors between the two phases. Thus, iodine-based ECV_CT could provide more accurate results and enable one-step cardiac coronary and myocardial evaluation in patients after PCI. Furthermore, ECV_CT obtained from SDCT, which benefits from a higher spatial resolution than MR (10,22,23), could improve the diagnostic confidence for small myocardial perfusion defects. The evaluation of IQ in this study showed that the IQ scores were significantly higher with CT than with CMR imaging. Furthermore, detector-based dual-energy CT had spatially and temporally matched high- and low-energy X-rays, allowing for projection-space material decomposition, which is critical for cardiac spectral scans. Also, unlike with tube-based dual-energy CT, the on-demand retrospective spectra require no changes to the routine clinical workflow.

This study has several limitations that need to be considered. First, the myocardium was segmented manually on CT images, which may have resulted in error from patient to patient. However, the ICC results for ECV_CT indicated good agreement between the two radiologists. Second, artifacts caused by stents on CT images may also affect the measurement of iodine values, impacting on the accuracy of ECV_CT. Also, differences in the delay time after contrast injection for LIE may have also affected the result of this study. Although some studies on ECV have analyzed the time required to achieve stability after contrast injection (24-26), no study to date has suggested an optimal delay time; therefore, more studies are needed to clarify this issue. Third, although we analyzed the consistency of ECV_CT and ECV_MR from 256 LV segments, the sample size was still small.

Conclusions

Iodine-based ECV obtained by SDCT is highly correlated with ECV obtained from CMR imaging and could serve as a biomarker for myocardial evaluation. In combination with CCTA, SDCT could be used for one-step cardiac follow-up examination in patients after PCI.

Acknowledgments

The authors thank all the study investigators, clinicians, nurses, and technicians for dedicating their time and skills to the completion of this study.

Funding: This work was supported by the Natural Science Foundation of China (Nos. 92068116, 81870358), the Key Projects of Science and Technology of Jiangsu Province (No. BE2019602), the Science Fund for Distinguished Young Scholars in Jiangsu Province, the Innovation and Entrepreneurship Doctoral Foundation (No. JSSCBS20211534), the Nanjing Medical Science and Technique Development Foundation (Nos. ZKX19018, QRX17057), China Postdoctoral Science Foundation (No. 2019M661804), and Jiangsu Province Postdoctoral Science Foundation (No. 2019k060).

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-21-1103/rc

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-21-1103/coif). ZS and XC are employees of Philips Healthcare. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work and ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Institutional Review Board of Nanjing Drum Tower Hospital (No. 2018-046-01), and written informed consent was obtained from all patients.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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