Background: Recently faster cardiac magnetic resonance (CMR) cine sequences basing on k-t compressed sensing have been developed. Purpose: To compare two compressed sensing CMR sequences-one in breath-hold technique and one during free breathing—with the standard SSFP sequence with respect to regional left ventricular function assessment. Material and Methods: Left ventricular short-axis stacks of two compressed sensing sequences in breath-hold technique (sparse_HB) and during free breathing (sparse_FB; both spatial resolution, 1.8 × 1.8 × 8 mm3) and a standard SSFP cine sequence (spatial resolution, 1.9 × 1.9 × 8 mm3) were acquired in 50 patients on a 1.5 T MR system. Regional wall motion abnormalities (RWMA) were rated qualitatively (normal/hypo-/a-/dyskinesia) by two experienced readers in consensus for all cardiac segments ( American Heart Association’s segment model) and sequences. RWMA detection rates were compared between sequences by kappa statistic. Results: In 13 patients , RWMA were detected in at least one cardiac segment. The RWMA detection rates were similar between CMR sequences (hypokinesia, 7.2% to 7.9%; akinesia, 0.8% to 1.3%; dyskinesia 0.3% to 0.4%) and kappa statistics revealed an almost perfect agreement in RWMA detection between both sparse and the standard SSFP sequence (standard versus sparse_HB: kappa, 0.918, p value, <0.001; standard versus sparse_FB: kappa, 0.868, p value, <0.001). Conclusion: Compressed sensing cine CMR acquired during breath-hold or free-breathing allows reliable RWMA detection, thus, might alternatively be used in cine CMR for regional left ventricular function assessment.
Cardiac magnetic resonance imaging (CMR) is an established imaging tool in the diagnostic workup of patients with suspected heart disease and plays an important role in risk stratification (e.g. in coronary artery disease or myocarditis) and non-invasive therapy monitoring [
Thus, the aim of the present study was to compare two different compressed sensing CMR sequences-one acquired in breath-hold technique, with reduced breath-hold times and one during free breathing―with the current standard SSFP sequence with the focus on regional left ventricular function assessment.
Prospective analysis and use of data was approved by the local ethic committee. All included patients gave written informed consent for CMR examination and study participation.
All CMR scans were performed on a 1.5-Tesla system (Magnetom Aera, Siemens Healthcare, Erlangen, Germany). Three stacks of short-axis slices covering the complete left ventricle were acquired in every patient using the following sequences: [A] retrospectively ECG-gated, segmented cine steady-state free precession sequence in breath-hold technique (standard SSFP; TR: 44.54 ms; TE: 1.1 ms; matrix: 192 × 156; FOV: 370 × 301 mm2; flip angle: 59˚; 17 segments; 25 calculated phases; spatial resolution: 1.9 × 1.9 × 8 mm3; bandwidth: 930 Hz/px; median breath-hold time (for acquisition of all short-axis slices): 130 sec), [B, C] prospectively ECG-triggered segmented compressed sensing cine SSFP sequence in breath-hold technique (B, sparse_HB; median breath-hold time (for acquisition of all short-axis slices): 21 sec) and during free breathing (C, sparse_FB) (sparse_HBand sparse_FB: TR: 39,75 ms; TE: 1.1 ms; matrix: 224 × 146; FOV: 400 × 300 mm2; flip angle: 60˚; 15 segments; spatial resolution: 1.8 × 1.8 × 8 mm3; vendor provided sparse acceleration factors, A (defined as the acceleration factor in the central part of the k-space): 3 and B (defined as the acceleration factor in the k-space periphery): 14; number of iterations: 80; bandwidth: 893 Hz/px).
Analysis for RWMA was performed in consensus by two experienced readers (CMR experience > 12 years and >6 years). Presence and severity of RWMA were evaluated visually and graded as “normal”, “hypokinesia”, “akinesia”, or “dyskinesia” in all CMR sequences and in all cardiac segments, except for segment 17 (in accordance to the American Heart Association’s segmental model) [
Left ventricular volumetry was performed in all patients and CMR sequences by one reader (CMR experience > 6 years) using the Argus software (Siemens Healthcare, Erlangen, Germany; employed standard values based upon [
For statistical analysis MedCalc (version 12.3.0.0, MedCalc Software, Mariakerke, Belgium) and SPSS software package (version 19.0, IBM, Armonk, NY, USA) were used. Testing for normal distribution was performed by D’Agostino- Pearson test. Normally distributed data are presented as mean ± standard deviation, otherwise medians and interquartile ranges are given. To analyze for differences in RWMA detection between the three employed CMR sequences kappa statistic was performed and interpreted as proposed by Landis and Koch [
Fifty consecutive unselected patients (17 female, 33 male) referred for clinical CMR examination and willed to participate in the present study were examined. Patients were referred to CMR for suspected myocarditis (n = 14), pericarditis (n = 1), cardiac infarction (n = 5), cardiomyopathy (n = 12), congenital heart disease (n = 2), cardiac tumor (n = 1), or unclear reduction of heart output or dysrhythmia (n = 15). Mean patient age was 41.5 ± 20.2 years (range: 8 - 77 years). Median weight was 75 kg (interquartile range: 27 kg; range: 30 - 152 kg), median height 175 cm (interquartile range: 12 cm; range: 130 - 195 cm), and median body mass index 24.8 ± 7.2 kg/m2 (range: 13.1 - 51.4kg/m2). The median heart rate was 68 beats/minute (interquartile range: 16 beats/ minute; range: 46 - 113 beats/minute).
In all 50 patients well analyzable data sets of all three CMR sequences were acquired (
Left ventricular volumetric values of all three CMR sequences are presented in
normokinesia | hypokinesia | akinesia | dyskinesia | |
---|---|---|---|---|
standard SSFP | 729 (91.1%) | 63 (7.9%) | 6 (0.8%) | 2 (0.3%) |
sparse_HB | 725 (90.6%) | 62 (7.8%) | 10 (1.3%) | 3 (0.4%) |
sparse_FB | 731 (91.4%) | 58 (7.2%) | 9 (1.1%) | 2 (0.3%) |
SSFP, steady-state free precession sequence; sparse, compressed sensing sequence; HB, breath-hold; FB, free breathing.
differences were found for EDV (difference of median, 8 ml; p value < 0.001), SV (difference of median, 8 ml; p value < 0.001), and EF (difference of median, 1%; p value, 0.016), but not for ESV (p value, 0.198). Comparing standard SSFP and sparse_FB, small, but significant median differences were found for ESV (difference of median, 4 ml; p value < 0.001), SV (difference of median, 4ml; p value, 0.004), and EF (difference of median, 2%; p value< 0.001), but not for EDV (p value, 0.817). These findings were confirmed by the Bland-Altman analysis (
standard SSFP | sparse_HB | sparse_FB | |
---|---|---|---|
EDV [ml] | 128 (43) | 120 (39) | 127 (38) |
ESV [ml] | 45 (33) | 45 (25) | 49 (24) |
SV [ml] | 76 (20) | 68 (20) | 72 (26) |
EF [%] | 61 (11) | 60 (10) | 59 (11) |
SSFP, steady-state free precession sequence; sparse, compressed sensing sequence; HB, breath-hold; FB, free breathing; EDV, end-diastolic volume; ESV, end-systolic volume; SV, stroke volume; EF, ejection fraction.
standard SSFP vs. | bias | SD | 95%-CI | |
---|---|---|---|---|
EDV [ml] | sparse_HB | 7 | 9 | −10, 24 |
sparse_FB | 0 | 10 | −19, 20 | |
ESV [ml] | sparse_HB | 1 | 6 | −10, 12 |
sparse_FB | −4 | 7 | −18, 9 | |
SV [ml] | sparse_HB | 6 | 8 | −10, 22 |
sparse_FB | 5 | 10 | −15, 24 | |
EF [%] | sparse_HB | 2 | 4 | −6, 10 |
sparse_FB | 4 | 5 | −6, 13 |
SSFP, steady-state free precession sequence; sparse, compressed sensing cine sequence; HB, breath-hold; FB, free breathing; SD, standard deviation; 95%-CI, 95%-confidence interval; EDV, end-diastolic volume; ESV, end-systolic volume; SV, stroke volume; EF, ejection fraction.
In this study we could demonstrate for the first time that not only our compressed sensing CMR sequence acquired in breath-hold technique, but also our compressed sensing CMR sequence during free breathing allowed reliable regional left ventricular function assessment. This result is in line with the findings of Allen et al. who compared an iteratively reconstructed k-t under sampled breath-hold SENSE cine sequence with a conventional breath-hold SSFP cine sequence based on GRAPPA (acceleration factor, 2) with respect to RWMA detection in 20 patients and in 9 healthy volunteers [
Beyond the discussed studies, no other compressed sensing CMR sequence study dealt with RWMA detection, and to the best of our knowledge our study is the first investigating a compressed sensing CMR sequence during free breathing with respect to RWMA detection. And given that many patients undergoing CMR suffer from shortness of breath, CMR data acquisition during free breathing improves not only CMR acceptance by the patient but also patient’s comfort.
Regarding the global left ventricular function, only small, not relevant differences in left ventricular values were found between the standard SSFP sequence and both compressed sensing CMR sequences. For the sparse_HB sequence slightly lower EDV, SV, and EF values were found which might be caused by an insufficient capture of the end-diastole [
Our study is not without limitations. First, we analyzed the RWMA exclusively visually. Although this is common practice, accuracy might benefit from a quantitative RWMA analysis. Second, only a limited number of patients/cardiac segments with RWMA were included, which was due to our unselected patient cohort.
In conclusion, compressed sensing cine imaging of the left ventricle acquired either during breath-hold or during free breathing allows the reliable detection of regional wall motion abnormalities. Thus, these fast cine sequences can alternatively be used for the assessment of LV function.
The authors thank Marcel Gratz for his technical assistance and fruitful discussions.
The SPARSE-SENSE sequence prototype was provided by Siemens Healthcare GmbH, Erlangen, Germany. The authors declare that there is no conflict of interest to this article.
Goebel, J., Nensa, F., Schemuth, H., Maderwald, S., Quick, H.H., Schlosser, T. and Nassenstein, K. (2018) Detection of Regional Wall Motion Abnormalities in Compressed Sensing Cardiac Cine Imaging. World Journal of Cardiovascular Diseases, 8, 277-287. https://doi.org/10.4236/wjcd.2018.86027