Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Rumac, Simone; Pavon, Anna Giulia; Hamilton, Jesse I.; Rodrigues, David; Seiberlich, Nicole; Schwitter, Juerg; van Heeswijk, Ruud B.

doi:10.1038/s41598-022-23573-3

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Published: 04 November 2022

Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers

Simone Rumac¹,
Anna Giulia Pavon²,
Jesse I. Hamilton³,
David Rodrigues¹,
Nicole Seiberlich³,
Juerg Schwitter^2,4 &
…
Ruud B. van Heeswijk¹

Scientific Reports volume 12, Article number: 18705 (2022) Cite this article

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Abstract

Cardiac Magnetic Resonance Fingerprinting (cMRF) has been demonstrated to enable robust and accurate T₁ and T₂ mapping for the detection of myocardial fibrosis and edema. However, the relatively long acquisition window (250 ms) used in previous cMRF studies might leave it vulnerable to motion artifacts in patients with high heart rates. The goal of this study was therefore to compare cMRF with a short acquisition window (154 ms) and low-rank reconstruction to routine cardiac T₁ and T₂ mapping at 1.5 T. Phantom studies showed that the proposed cMRF had a high T₁ and T₂ accuracy over a wider range than routine mapping techniques. In 9 healthy volunteers, the proposed cMRF showed small but significant myocardial T₁ and T₂ differences compared to routine mapping (ΔT₁ = 1.5%, P = 0.031 and ΔT₂ = − 7.1%, P < 0.001). In 61 consecutive patients referred for CMR, the native T₁ values were slightly lower (ΔT₁ = 1.6%; P = 0.02), while T₂ values did not show statistical difference (ΔT₂ = 4.3%; P = 0.11). However, the difference was higher in post-contrast myocardial T₁ values (ΔT₁ = 12.3%; P < 0.001), which was reflected in the extracellular volume (ΔECV = 2.4%; P < 0.001). Across all subjects, the proposed cMRF had a lower precision when compared to routine techniques, although its higher spatial resolution enabled the visualization of smaller details.

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Introduction

Over the past decade, the field of cardiac magnetic resonance (CMR) saw an increasing interest in the measurement of myocardial T₁ and T₂ relaxation times^1,2,3, and multiple studies have now shown the added diagnostic value of myocardial T₁ and T₂ values⁵. Native T₁ mapping (i.e. without a gadolinium-based contrast agent—GBCA) has become a widely used technique to detect interstitial fibrosis^6,5,8, while T₁ mapping after the administration of GBCA provides unique additional prognostic information through the estimation of the extracellular volume (ECV) fraction^9,8,11. Increased myocardial T₂ values similarly provide a quantitative measure of myocardial edema in various pathologies^12,13.

While these findings are highly encouraging, the clinical adoption of parametric mapping is hindered by relatively long acquisition times and ongoing discussions about accuracy, precision, robustness, and sensitivity⁵. These challenges have encouraged the exploration of new mapping techniques^{14,13,14,15,16,19}. It would especially be desirable to develop a fast and reliable method capable of simultaneously quantifying both T₁ and T₂ relaxation times, in order to save time and to remove the bias that one parameter can have on the estimation of the other.

Magnetic Resonance Fingerprinting (MRF) has been proposed²⁰ as an alternative approach to parameter mapping that enables the simultaneous quantification of multiple relaxation times. The idea behind MRF is to deliberately vary the pulse sequence parameters in order to induce a unique signal evolution that depends exclusively on the tissue properties under investigation (e.g. T₁ and T₂). After acquiring data with a highly undersampled k-space sampling trajectory, a pattern matching algorithm is used to match each voxel’s time course to an entry in a dictionary containing simulated MRF time courses. The best-matching dictionary entries are used to populate the parametric maps with the corresponding values. The accuracy and efficiency of this approach have been demonstrated for stationary tissues such as the brain^20,21 and prostate²².

The use of MRF for parametric mapping in the heart, however, faces additional challenges due to respiratory and especially cardiac motion. In order to address these challenges, cardiac MRF (cMRF) makes use of breath holds and electrocardiographic (ECG) triggering. Similar to established myocardial mapping techniques such as MOLLI²³, SASHA¹⁶ or T₂-prepared bSSFP²⁴, cMRF data are acquired during an acquisition window in the mid-diastolic phase of each heartbeat, over the span of a single breath hold. This results in a shorter and interrupted acquisition, which limits the amount of signal that can be matched to the dictionary as well as the variation in the signal, which is required for accurate pattern matching. At the same time, since the duration of the mid-diastolic quiescent period depends on the subject’s heart rate, a longer acquisition window is not always possible in the presence of pathologies. Most MRF applications in stationary organs (e.g. brain) use sequence lengths of 1000 TRs or more, and many prior cMRF studies have used a scan duration of 15–16 heartbeats with a 250 ms acquisition window, leading to approximately 750 TRs that can be used for fitting. Reducing the acquisition window to 150 ms would be especially desirable in patients with high heart rates. However, this would result in a sequence length of approximately 400 TRs, which limits the amount of cMRF data that can be collected and in turn may result in a loss of measurement precision. To counter the expected loss in precision caused by shortening the acquisition window, the intrinsic redundancy and similarity of the magnetization time courses can be exploited. Instead of using the full dictionary, the redundancy allows for the use of a low-rank approximation of the dictionary, which is a highly efficient regularization constraint for iterative reconstruction. Such a low-rank iterative reconstruction can thus be used to reduce noise and aliasing artifacts and to compensate for the precision loss^25,24,27.

The feasibility of a cMRF sequence that combines such a short acquisition window and low-rank reconstruction has recently been presented in healthy volunteers²⁸. Therefore, the goal of this study was to assess the robustness of the technique in a routine clinical setting by comparing its accuracy and precision to routine myocardial T₁ and T₂ mapping in a cohort of patients referred for clinical CMR for a wide range of indications.

Results

Phantom studies

When considered over the entire tested range, the phantom mapping demonstrated similar or higher T₁ and T₂ accuracy for cMRF than the routine mapping techniques (Fig. 1A,B, Suppl. Fig. 1). In the T₁ vials, the average bias and 95% limits of agreement obtained from the Bland–Altman analysis were equal to 12.81 ± 13.34% for cMRF with a 154 ms acquisition window and 13.03 ± 12.15% for the cMRF sequence with a 250 ms acquisition window (Fig. 1A). The two MOLLI variants had overall smaller biases and comparable limits of agreement, and the errors were equal to 5.38 ± 8.63% and 0.78 ± 12.39% for 5(3)3-MOLLI and 4(1)3(1)2-MOLLI, respectively (Fig. 1B). The same analysis was performed for T₂ relaxation times and confirmed the overall good agreement with reference measurements. The biases and limits of agreement were respectively 1.55 ± 9.21% and 6.76 ± 11.93% for the two variants with shorter and longer acquisition window (Fig. 1C). T₂-prepared bSSFP (Fig. 1D) showed a similar bias, but a considerably larger confidence interval: 8.76 ± 48.9%. The short-acquisition-window cMRF reconstruction time was 297 ± 20 s.

For both the T₁ and T₂ relaxation times, the heart-rate influence phantom study showed no significant variation or trend in the resulting T₁ and T₂ values as the R-R interval was increased (Suppl. Fig. 2). For the T₁ relaxation times, the CoV for both cMRF variants was below 2%, which was comparable to that of 5(3)3-MOLLI and slightly better than that of 4(1)3(1)2-MOLLI. For the T₂ relaxation times, the CoV was higher, and ranged between 2 and 8% across the relevant T₂ values (Suppl. Fig. 2C,D). For both relaxation times, no significant difference was observed between the cMRF sequences with the shorter and longer acquisition windows. Moreover, for both cMRF variations, and both T₁ and T₂, the ANCOVA analysis found no significant difference between the linear regression slopes obtained at different heart-rates (all P values > 0.05, Suppl. Fig. 3A–D).

Healthy volunteers

In 9 healthy volunteers (age 25 ± 2 years, 66% female, heart rate 69 ± 17 bpm, ranging from 53 to 111), cMRF resulted in similar relaxation times with a slightly lower precision compared to routine techniques (Fig. 2). The inter-observer variability showed good agreement between two different observers: whole-heart T₁ and T₂ values were not significantly different for cMRF_150ms (P = 0.148 and P = 0.535, respectively). A small but significant bias was found when analyzing the cMRF_250ms T₁ maps (P = 0.008, bias = 8.6 ms), while whole-heart T₂ distributions did not differ significantly between the two observers (P = 0.177).

Averaged over the three slices and the nine volunteers, the myocardial T₁ values were slightly higher and varied more for both versions of cMRF when compared to MOLLI (cMRF_154ms: 1020 ± 45 ms; cMRF_250ms: 1026 ± 34 ms; MOLLI: 1005 ± 20 ms). Both techniques also differed significantly from MOLLI (P = 0.031 and P = 0.002, respectively for cMRF_154ms and cMRF_250ms). No significant difference was found between the two cMRF variants (P > 0.999). When compared on a per-segment basis, the T₁ difference was significant in the septum of the mid slice between cMRF_250ms and MOLLI (segment 12; Fig. 2C–E). No significant difference was observed between the two cMRF variants and between cMRF_154ms and MOLLI. Overall T₂ values, on the contrary, were lower in cMRF compared to T₂prep-bSSFP, and confirmed the trend observed in vitro (cMRF_154ms: 42.4 ± 2.4 ms; cMRF_250ms: 38.5 ± 2.6 ms; T₂prep-bSSFP: 45.4 ± 2.0 ms). All whole-heart distributions were significantly different (P < 0.001 for all comparisons). When comparing the segments, significant difference was found between cMRF_250ms and T₂prep-bSSFP in the lateral part of all three slices (Fig. 2C–F). The per-segment precision was higher in routine techniques: cMRF with 154 ms acquisition window resulted in a T₁ CoV of 7.9 ± 2.0% and T₂ CoV of 7.7 ± 3.3%. cMRF with 250 ms acquisition window resulted in a T₁ CoV of 6.8 ± 1.8% and T₂ CoV of 8.1 ± 3.2%. MOLLI resulted in a T₁ CoV of 5.3 ± 0.9% and T₂prep-bSSFP in a T₂ CoV of 6.5 ± 2.0%.

Patient scans

Scans were completed in n = 61 patients referred for CMR (Suppl. Fig. 4); in one patient, all mapping failed due to the presence of an implantable cardioverter-defibrillator (ICD). Native cMRF was completed in 59 patients; in one case, it failed due a sudden deep breath of the patient (not noticed until the maps were reconstructed). Native routine T₁ and T₂ mapping were completed in 58 and 13 subjects and failed in two and one cases respectively due to bSSFP banding artifacts. Post-contrast cMRF and routine T₁ mapping were completed in 39 subjects, while for the latter, two acquisitions failed due to bSSFP banding artifacts. This resulted in n = 59 for native cMRF, n = 58 for native T₁ mapping, n = 13 for native T₂ mapping, n = 39 for post-contrast cMRF, and n = 37 for post-contrast T₁ mapping.

Overall, we observed a robust performance of cMRF with a low number of failed acquisitions. We anecdotally observed that the higher spatial resolution of cMRF allowed for the easier identification of small and non-transmural lesions in several patients (Fig. 3, Suppl. Fig. 5). cMRF also still resulted in accurate maps in the presence of large respiratory drifts, albeit producing visibly noisier maps (Fig. 4).

The agreement between cMRF and routine techniques seen in the phantom and healthy volunteer studies was confirmed in the native myocardial T₁ relaxation times (cMRF: 1015 ± 61 ms; MOLLI: 1029 ± 53 ms; ΔT₁ = 1.6%; P = 0.02). A small but significant difference was found in the inferior and inferolateral segments as well as in the blood pool, where cMRF measured lower T₁ values than MOLLI (Figs. 5A, 6A,B, 7). cMRF showed a substantial difference from the routine post-contrast myocardial T₁ value (cMRF: 395 ± 50 ms; MOLLI: 444 ± 53 ms; ΔT₁ = 12.3%; P < 0.001, Fig. 6C), which was consistent in all segments (Fig. 5B). Contrary to this, the post-contrast blood-pool T₁ value had similar average values (cMRF: 279 ± 45 ms; MOLLI: 289 ± 54 ms; ΔT₁ = 3.6%; P < 0.001, Fig. 6D). The different post-contrast myocardial (and the native blood pool) T₁ values were reflected in a higher ECV than that established with MOLLI (cMRF: 28.4 ± 4.8%; MOLLI: 26.0 ± 4.0%; ΔECV = 8.4%; P < 0.001, Figs. 5C, 6F). In the myocardium, T₂ values obtained with cMRF were marginally lower compared to those obtained with T₂-prep bSSFP, but no significant difference was observed (cMRF: 44.5 ± 5.5 ms; T₂-prep bSSFP: 46.4 ± 3.7 ms; ΔT₂ = 6.1%; P = 0.11; Figs. 5D, 6E). The segmental precision of cMRF was again lower than that of the routine techniques for the native T₁ (cMRF: 12.0 ± 3.7%; MOLLI: 6.8 ± 1.5%), post-contrast T₁ (cMRF: 10.6 ± 3.1%; MOLLI: 7.4 ± 2.5%), and T₂ values (cMRF: 12.3 ± 2.9%; T₂prep-bSSFP: 9.1 ± 1.7%); all with P < 0.001. Finally, none of measured relaxation times (routine or cMRF) resulted in a significant linear regression with the individual patient heartrate (P > 0.14 for all, Suppl. Fig. 6).

Discussion

Cardiac MR fingerprinting with a short acquisition window and low-rank reconstruction²⁸ was characterized in vitro, in healthy volunteers and as part of a clinical CMR protocol to assess its accuracy and precision compared to routine T₁ and T₂ mapping techniques.

In vitro, cMRF had slightly lower accuracy than routine techniques when tested against reference relaxation times spanning from very low (90 ms) to very high (1900 ms) T₁ values. T₁ values in the native myocardial range were higher for both cMRF sequences, while excellent agreement was observed for the entire T₂ range. Despite the small size of these differences, they were interestingly opposite to what was found in vivo, where good agreement was found between the native T₁ values (around 1000 ms) of both healthy volunteers and patients, and lower values compared to MOLLI were found after GBCA injection. We observed high consistency between the longer and shorter cMRF sequences both in vitro and in vivo, with the measured relaxation times often overlapping. The study of heart-rate variability influence in the phantom also showed consistent values for both techniques across a wide range of relaxation times and simulated heart-rates. The slightly higher heartrate CoVs for the cMRF techniques than the routine technique over the entire heartrate range are still lower than a single segmental myocardial T₂ standard deviation observed in vivo, from which we estimate that they can be ignored.

The differences between the cMRF and the IR-TSE reference likely have several causes, such as using a TSE instead of an SE sequence, and not including the slice profile of the RF pulse³⁰, B₀ and B₁⁺ inhomogeneities³¹, and inversion efficiency¹⁷ in the fitting model of IR-TSE reference, which might lower its accuracy. It should be noted that the RF pulse profile is included in the cMRF dictionary, and that the presented cMRF technique used adiabatic inversion and refocusing pulses, as opposed to the IR-TSE and SE techniques, making it less susceptible to B₁ inhomogeneities. Moreover, the T₁/T₂ ratios in the phantom vary over a much broader range than in vivo. Given these sources for mismatching and discrepancies, it is encouraging to note that the overestimation in vitro still consistently remained below 10%.

Both in the healthy volunteer group and in the patients, cMRF showed a high agreement and comparable inter-patient standard deviations when compared to MOLLI and T₂-prep bSSFP. In the patient group, the majority of the subjects was referred with suspected and not confirmed disease, and did not have significantly altered myocardial relaxation times with any of the used techniques. The global average across all patients was therefore close to that of the healthy volunteers, with virtually identical average T₁ and T₂ values measured in both groups. When a subgroup of patients with a specific disease was selected (not reported here), this also held true, although small sub-endocardial infarctions were more clearly delineated in the cMRF maps, most likely due to the higher spatial resolution.

The relaxation times observed here are in line with previous studies where the accuracy and precision of cMRF were established^32,31,34. In particular, Hamilton et al. found moderately lower relaxation times and equivalent standard deviations (964 ± 71 ms and 41.2 ± 4.2 ms for T₁ and T₂, respectively) in fifty healthy volunteers at 1.5 T³². In twenty-four patients with suspected inflammatory cardiomyopathy³³, cMRF resulted in very similar myocardial T₁ = 1028 ± 64 ms and higher T₂ = 52.8 ± 3.8 ms. The same comparison between cMRF and routine techniques was performed pre- and post-contrast in six hypertrophic cardiomyopathy patients and 12 healthy subjects³⁴: small differences in myocardial relaxation times were found between routine techniques and cMRF, although both methods easily allowed for the distinction between patients and healthy subjects. Interestingly, larger differences were found in the ECV estimation, where cMRF measured consistently higher percentages in both groups.

The larger difference in the post-contrast myocardial T₁ values is the main cause of difference in ECV estimation, although it should be noted that Treibel et al. obtained the empirical formula to calculate the hematocrit specifically from MOLLI sequences³⁵. The timings of each post-contrast acquisition with respect to the GBCA injection were verified, as was the accuracy in the delineation of the myocardial area: both were not found to be significantly different between cMRF and routine techniques after verification by a second observer. To the best of our knowledge, neither of these factors alone could substantially influence the myocardial T₁ estimation, and an adequate explanation for the mismatch remains to be found.

In both subject groups, the inter-subject standard deviations of cMRF and routine techniques were similar and between 5 and 12% of the measured value. The individual segmental precision of cMRF is in line with previous studies^32,36,37. While it is lower than its routine counterparts, this is balanced with the 1.5-fold to 2.4-fold smaller pixel size of cMRF as well as the smoothing effects of the non-rigid motion registration techniques used for routine mapping techniques³⁸. This suggests that it might be of interest to integrate a spatial regularization filter or a denoising step in the reconstruction pipeline of cMRF data²⁷, while the cMRF spatial resolution could be lowered to that of the routine scans, if desired. This may result in a higher precision for cMRF, while leaving the other advantages of cMRF intact: acquisition of both maps at half the time, using the same protocol for all T₁ (and T₂) ranges, and intrinsic co-registration of the T₁ and T₂ maps⁵. If a larger spatial coverage is desired, the proposed shorter acquisition window could for example be combined with a simultaneous multislice (SMS) acquisition³⁹, although the accuracy and precision of such a technique would need to be established. Similarly, a 3D free-breathing technique⁴⁰ could be used if extra scan time can be allocated to obtain whole-heart coverage, which may especially be of use in diseases that manifest in unpredictable patchy patterns.

The main limitation of this study lies with the broad variety of scanned patients. While this variety is a strength of the study in that it enabled us to characterize cMRF in a cohort that represents a real heterogeneous clinical population, it did not result in a narrow range of relaxation times that was significantly different from healthy volunteers. This effect was further exacerbated by the patients only having suspected and not confirmed cardiac disease, and by the standardized segmental analysis that might average out any small regional T₁ or T₂ elevations. Therefore, it might be of interest to further characterize this technique in cohorts of patients with a specific and confirmed disease. In two of the aforementioned studies^33,34, a blinded evaluation of cMRF against MOLLI and T₂prep-bSSFP was performed, where cMRF compared favorably against both techniques. This might also be incorporated in future work to provide a better overall understanding of the diagnostic potential of cMRF.

In conclusion, the comparison of cMRF with a short acquisition window with routine myocardial T₁ and T₂ mapping techniques showed a robust performance in vitro, in healthy volunteers, and in a group of CMR patients covering a broad range of pathologies. The T₁ and T₂ maps generated from the single cMRF acquisition were generally comparable in accuracy, slightly less precise, and had a higher spatial resolution than those obtained with routine T₁ and T₂ mapping techniques. A notable exception was found in a lower estimation of the post-contrast myocardial T₁ relaxation time, which led to a small but significantly higher ECV.

Materials and methods

The local ethical committee (Commission cantonale d'éthique de la recherche sur l'être humain du canton de Vaud CER-VD, Lausanne, Switzerland) approved all in vivo studies under protocol number 2018-00656 (principal investigator JS), all studies were performed in accordance with the relevant guidelines and regulations, and all participants provided written informed consent. All scans were performed on a clinical 1.5 T MR scanner (MAGNETOM Sola, Siemens Healthcare, Erlangen, Germany), using an 18-channel chest coil combined with a 12-channel spine coil.

Phantom study

The accuracy of the proposed cMRF technique was first assessed using the ISMRM-NIST phantom (QalibreMD, Boulder, CO, USA), a 200-mm spherical phantom with two layers comprised of 14 spheres, one with a wide range of T₁ values and one with a wide range of T₂ values. The T₁ array was created by NiCl₂-doped deionized water, while the T₂ array spheres are filled with MnCl₂-doped deionized water. A single slice was planned through the T₂ layer of spheres (n = 14), since it contains a broad T₁ and T₂ range that mimics relevant physiological values. Because all sequences were designed to be accurate within physiological ranges, T₁ values lower than 90 ms and higher than 1900 ms were discarded in the analysis. For the same reason, we considered T₂ values ranging from 30 to 240 ms. For completeness, the measurements obtained in all 14 vials are reported as supplementary material.

Parameters for the cMRF pulse sequence included: spiral readout, 15-heartbeat breathhold, 48-fold undersampling, field of view (FOV) = 300 × 300 mm², pixel size = 1.6 × 1.6 mm², slice thickness = 8 mm, 2.5 µs dwell time, and sampling bandwidth ± 200 kHz. The repetition time (TR) was fixed at 5.3 ms, while the flip angle was varied between 4° and 25°. To facilitate the distinction between different relaxation times, the cMRF sequence incorporates four different magnetization preparation modules: an adiabatic inversion recovery and three adiabatic T₂ preparations⁴¹ with echo times of 30, 50 and 80 ms. Two variations of the cMRF sequence were considered and compared: one with a longer acquisition window = 250 ms and 48 undersampled image acquisitions per heartbeat and one with the shorter acquisition windows = 154 ms and 29 undersampled image acquisitions per heartbeat. We will differentiate between the two variants by indicating the acquisition window duration in a subscript.

A low-rank reconstruction was used to reduce the dimensionality of the generated dictionaries and reduce aliasing artifacts in the MRF images^26,27,39. Similar to previous work, this approach compresses the dictionary along the time dimension by calculating the singular value decomposition of the dictionary and retaining the first five singular values. Images in this low-dimensional subspace are reconstructed using nonlinear conjugate gradient descent with both ${l}_{1}$ wavelet regularization and locally low-rank patch regularization with a patch size of 8 × 8. The dictionary took the slice profile and imperfections due to the efficiency of preparation pulses into account³⁰. All reference and cMRF maps were reconstructed in MATLAB 2019a (The MathWorks, Natick, MA, USA; http://www.matlab.com) on a desktop PC with an i9-9900 CPU @3.10 GHz and integrated Intel UHD Graphics 630, and the reconstruction time was recorded.

As recommended in the phantom manual, reference T₁ relaxation times were obtained with an inversion-recovery turbo spin echo (IR-TSE) pulse sequence with nine inversion times (35–3000 ms), TE = 7.9 ms, TR = 4500 ms, and spatial resolution = 1 × 1 mm². A spin-echo (SE) pulse sequence was used to determine reference T₂ values, with 25 echo times (TEs) ranging from 10 to 400 ms, pixel bandwidth = 279 Hz, TR = 5000 ms, spatial resolution = 1 × 1 mm², and slice thickness = 6 mm.

Three clinical routine parametric mapping techniques were also characterized: 5(3)3-MOLLI (21) for T₁ mapping in the native T₁ range, 4(1)3(1)2-MOLLI⁴ for T₁ mapping in the post-contrast T₁ range (both variants used heartbeats for their timing), and T₂-prepared bSSFP for T₂ mapping²⁴. 5(3)3-MOLLI and T₂-prep bSSFP had FOV = 300 × 300 mm², pixel size = 2.0 × 1.6 mm² interpolated to 1.6 × 1.6 mm², slice thickness = 8 mm. 4(1)3(1)2-MOLLI was performed with FOV = 306 × 360 mm², pixel size = 2.1 × 1.4 mm² interpolated to 1.4 × 1.4 mm², slice thickness = 8 mm. Both MOLLI sequences and T₂-prep bSSFP had TE = 1.1 ms. Acquisition window durations for cMRF, 5(5)3-MOLLI, 4(1)3(1)2-MOLLI and T₂-prep bSSFP were 154 ms, 194 ms, 194 ms, and 144 ms, respectively. Routine maps were reconstructed on the scanner.

Multiple synthetic heart-rates were simulated in order to study the influence of heart-rate variability on the cMRF techniques. The R-R intervals varied from 600 to 1300 ms, at 100 ms intervals (8 in total).

Healthy volunteer study

We compared both cMRF versions to 5(3)3-MOLLI and T₂-prepared bSSFP in 9 healthy volunteers. Three short-axis views were acquired with each technique (apex, middle, and base of the left ventricle) with breath holds in end-expiration. All three techniques were acquired with the same parameters used for the phantom study. After manual segmentation, the myocardium was subdivided according to the American Heart Association guidelines²⁹.

Patient study

The cMRF with a shorter acquisition window was inserted in a clinical protocol for myocardial viability assessment in 61 consecutive patients referred for CMR at the Lausanne University Hospital (CHUV).

Patients (age 57 ± 14 years, 28% female, heart rate 66 ± 12 bpm ranging from 52 to 102) were referred for suspected myocarditis (n = 5), pericarditis (n = 4), ischemic cardiomyopathy (CMP, n = 27), dilated CMP (n = 4), hypertrophic CMP (n = 3), inflammatory CMP (n = 2), arrhythmia (n = 3), or other diseases (n = 13). A single cMRF slice was acquired in the same basal short-axis view as a 5(3)3-MOLLI T₁ map and a T₂-prepared bSSFP T₂ map whenever either or both were deemed appropriate by the attending physician, and the clinical protocol allowed for the extra scan time. Similarly, cMRF and 4(1)3(1)2-MOLLI were acquired at the same location 20–25 min after the IV injection of 0.2 mmol/kg body weight GBCA (Gadovist, Bayer Healthcare, Leverkusen, Germany). Post-contrast cMRF T₂ values were obtained but not reported due to low relevance and lack of comparison. The cMRF protocol with shorter acquisition window was kept the same as used for both healthy volunteers and the phantom; the two MOLLI maps were acquired with FOV = 306 × 360 mm², pixel size = 2.0 × 1.4 mm² interpolated to 1.4 × 1.4 mm², and slice thickness = 8 mm, while T₂prep-bSSFP had FOV = 288 × 360 mm², pixel size = 2.5 × 1.9 mm² interpolated to 1.9 × 1.9 mm², and slice thickness = 8 mm.

In the patients where the native and post-contrast acquisitions were successful with both cMRF and routine techniques, the synthetic extracellular volume (ECV) was also computed:

$$\begin{array}{c}ECV=\left(1-\mathrm{Hct}\right) \cdot \frac{\left[\Delta {R}_{1}myocardium\right]}{\left[\Delta {R}_{1bloodpool}\right]},\end{array}$$

(1)

where $\Delta {R}_{1}={R}_{1}^{post{\text{-}}contrast}-{R}_{1}^{pre{\text{-}}contrast}$, and ${R}_{1}=1/{T}_{1}$. The value of the hematocrit (Hct) was estimated according to the empirical relationship found by Treibel et al. for MOLLI³⁵:

$$\begin{array}{c}Hc{t}_{MOLLI}=\left(866.0 \cdot \frac{1}{{T}_{1bloodpool}}\right)-0.1232.\end{array}$$

(2)

The heartrate of each patient was recorded for linear regression with the pre- and post-GBCA relaxation times.

Statistical analysis

All statistical analyses were performed with GraphPad Prism 8 (GraphPad Software, La Jolla, CA, USA; http://www.graphpad.com/scientific-software/prism/) and MATLAB. In vitro, we first computed the mean values and the standard deviation for each considered vial in the phantom. As a measure of accuracy, a Bland–Altman analysis and linear regression against reference values were performed for each technique, with a selected representative heart-rate of 60 bpm. In order to assess the influence of variable heart rates, we computed the coefficient of variation (CoV, standard deviation divided by the mean value) between scans acquired at different synthetic R-R intervals. Multiple linear regressions (one per heart-rate) were also computed for each technique, and Analysis of Covariance (ANCOVA) was used to test whether the slopes differed significantly.

In vivo, precision was defined per subject and segment and the coefficient of variation was again used as a measure of precision. When comparing the average measurements across multiple subjects, the standard deviations of the mean values were reported as absolute values (i.e. in milliseconds). Five datasets from healthy volunteers were segmented by a second observer, in order to assess the inter-observer variation that might be caused by differences in the segmentation. The resulting whole-heart distributions were compared by paired Student’s t-tests. Paired Student’s t-tests were also used to measure the agreement between cMRF and routine techniques. In healthy volunteers, since three slices from the same subject do not represent statistically independent variables, and three techniques were compared, a Bonferroni correction for multiple comparisons was used. When comparing global averages across the whole volunteer population, the three different slices were always averaged first, in order to generate one per-subject point. In all t-tests, P < 0.05 indicated statistical significance. Box and whiskers plots were reported to provide a graphical representation of the results from the multiple t-tests across the myocardial segments.

Data availability

The datasets used and analyzed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank Dr. Hélène Lajous, PhD (funded by SNSF grant 205321_182602) for providing the spin-echo T₂ reference values for the phantom.

Funding

This study was funded by grants from the Swiss Heart Foundation and the Swiss National Science Foundation (grant number 32003B_182615) to RBvH.

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Authors and Affiliations

Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Rue de Bugnon 46, BH08.084, 1011, Lausanne, Switzerland
Simone Rumac, David Rodrigues & Ruud B. van Heeswijk
Division of Cardiology, Cardiovascular Department, Cardiac MR Center, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
Anna Giulia Pavon & Juerg Schwitter
Department of Radiology, University of Michigan, Ann Arbor, MI, USA
Jesse I. Hamilton & Nicole Seiberlich
Faculty of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland
Juerg Schwitter

Authors

Simone Rumac
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Anna Giulia Pavon
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Jesse I. Hamilton
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David Rodrigues
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Nicole Seiberlich
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Juerg Schwitter
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Ruud B. van Heeswijk
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Contributions

R.B.v.H., J.S. and N.S. designed the study. J.I.H. and N.S. developed and implemented the pulse sequence and image reconstruction algorithm. J.S. and A.P. recruited the patients and performed their diagnoses. R.B.v.H. supervised the analysis of MRI results and the manuscript draft. S.R. acquired the phantom and volunteer data. D.R. and A.P. acquired the patient data. S.R. analyzed the results, performed the statistical analysis and initially drafted the manuscript. All authors participated in manuscript drafting and editing and approved the final version of the submitted manuscript.

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Correspondence to Ruud B. van Heeswijk.

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Competing interests

JS receives research support by Bayer Healthcare, Switzerland. The other authors declare no potential competing interests.

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Rumac, S., Pavon, A.G., Hamilton, J.I. et al. Cardiac MR fingerprinting with a short acquisition window in consecutive patients referred for clinical CMR and healthy volunteers. Sci Rep 12, 18705 (2022). https://doi.org/10.1038/s41598-022-23573-3

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Received: 10 June 2022
Accepted: 02 November 2022
Published: 04 November 2022
DOI: https://doi.org/10.1038/s41598-022-23573-3

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