Introduction

Cardiac amyloidosis (CA) is the condition in which amyloid fibrils occur in the extracellular space of heart, characterized by the aggregation and deposition of misfolded protein¹. Cardiac magnetic resonance imaging has increasingly been a central role in non-invasive diagnosis of CA due to the provided tissue characterization, including evaluation of native T1 signal, assessment of late gadolinium enhancement (LGE), extracellular volume (ECV) and feature tracking measurement².
Recently, diffusion weighted imaging (DWI) using multiple b values, permitting imaging of intravoxel incoherent motion (IVIM) in tissues, can provide microstructural details with respect to water molecular motion between myocytes, microcirculation and vascular flow^3,4. At present, the validation and feasibility of IVIM for assessing acute myocardial infarction and hypertrophic cardiomyopathy has been proven previously^5,6, while leaving a gap in other diseases.
In this study, we hypothesized that CA patients, relative to healthy controls, would exhibit different diffusion behaviors using IVIM, because of the fibrils deposition，and also postulated that IVIM parameters would correlate with the feature tracking and native T1 relaxation time, reflecting amyloid burden and thus being reliable biomarkers in the assessment of CA.

Materials and Methods

This clinical study was approved by the local Institution Review Board. Written informed consent was obtained from all subjects.
Subjects
9 patients (54±5 years, 6 male) and 21 healthy (52±11 years, 14 male) were prospectively recruited in this study. All patients have histologically proven amyloidosis (positive Congo red staining of abdominal fat, kidney or bone marrow biopsies) and echocardiographic findings or biomarker typical for cardiac involvement.
MRI experiment
Cardiac Magnetic Resonance imaging (CMR) was performed on a 3.0-Tesla scanner (GE750, Milwaukee, WI, USA) with 8-channel cardiac coil employed. Cine imaging, pre/post T1 mapping, DWI-IVIM (b-values 0, 50, 100, 200, 400, and 600s/mm²), and LGE imaging were applied for heart imaging in axial view. Detailed scan parameters were shown in Table 1.
Data analysis
One senior cardiovascular radiologist was employed to measure all IVIM images twice within a 1-month interval. The intra-observer agreement was test using both sets of measurements. The mean results were further obtained and used for data analysis. Additionally, to test inter-observer agreement, one measurement of randomly chosen data from 6 subjects was also acquired from another radiologist with relevant expertise. Using bi-exponential model⁷, IVIM derived parameters of true diffusion ADC_slow, pseudo diffusion ADC_fast and perfusion fraction F were obtained accordingly. Other data analyses, including feature tracking and native T1 mapping, were performed using cvi42 (Circle Cardiovascular Imaging, Calgary, Canada) by the employed senior radiologist. The region of interest in all MR data was limited to the mid-ventricle area and separated into 6 segments. All patients were subdivided into LGE(+) and LGE(−) groups based on the imaging enhancement on LGE images.
Statistical analysis
All statistical analyses were performed in SPSS software (SPSS V23.0, IBM SPSS Inc). Interobserver and intra-observer reliability of the IVIM image parameters was assessed by using intraclass correlation coefficient (ICC) analysis. Unpaired two-tailed t-test was applied to compare IVIM associated parameters in each mid-ventricle segment between healthy and patients. Pearson’s correlation analysis was employed to evaluate the relationship between IVIM image parameters and peak strain or native T1. P value < 0.05 was considered the significant threshold.

Results

In total, 153 segments from patients and healthy controls were acquired with complete IVIM associated parameters, strain and native T1. Fig.1 showed IVIM images from short-axis view. 9 CA patients were further divided into 24 LGE(+) and 19 LGE(−) segments.
Intra- and inter-observer agreement of all IVIM parameters measurement were 0.93, 0.84 for ADC_slow, 0.92, 0.87 for ADC_fast and 0.94, 0.91 for F, respectively.
Compared with healthy controls, CA patients showed significantly higher ADC_slow (2.50±0.81×10^-3mm²/s vs 2.17± 0.74 ×10^-3mm²/s, p=0.017), while comparable ADC_fast (0.18±0.08mm²/s vs 0.20±0.08mm²/s, p=0.084) and F (0.45±0.12 vs 0.43±012, p= 0.277) were observed between CA and healthy segments.
In additionally, LGE(−) segments exhibited an increased ADC_slow (2.55±0.78×10^-3mm²/s) than normal segments (p=0.044)(Fig. 2A). LGE(+) segments exhibited a decreased ADC_fast (0.16±0.08mm²/s) than healthy segments (p=0.02)(Fig. 2B). Using Pearson correlation analysis, only ADC_fast in LGE(+) CA group showed significant correlation with peak strain or native T1, respectively (all p<0.05; Fig. 3). A representative case of a healthy control and a CA patient is displayed in Fig. 4.

Discussion

This study investigated the clinical value of cardiac DWI-IVIM in the evaluation of CA patients and assessed the feasibility of IVIM for detecting hypoperfusion caused by amyloid deposition. IVIM derived parameters of ADC_slow and ADC_fast were found to be significantly altered in CA patients relative to healthy controls. Especially, amyloid burden severity could be quantified using ADC_fast, showing significant relationship with peak strain and T1 values of CA patients. Future research may identify amyloidosis specific patterns of IVIM parameters, which might be an advantage over rather unspecific parameters such as native T1 that shows elevated value in multiple cardiac diseases.

Conclusion

In conclusion, Cardiac DWI-IVIM has demonstrated the clinical value in the investigation of cardiac microcirculation. The derived ADC_slow and ADC_fast parameters may have potential to assess the perfusion status in amyloidosis regions for CA patients.

Acknowledgements

No acknowledgement found.

References

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