Visibility improvement of  cerebral blood vessels by High Resolution Quantitative Susceptibility Mapping
Yuya Umemoto1, Tomohiro Ueno1, Shin-ichi Urayama2, Toshihiko Aso2, Hidenao Fukuyama2, and Naozo Sugimoto1

1Human Health Sciences, Kyoto University, Kyoto, Japan, 2Human Brain Research, Kyoto University, Kyoto, Japan

Synopsis

In Quantitative Susceptibility Mapping, susceptibility distribution can be obtained by deconvolution of perturbed fields with dipole fields. In our proposed method, High Resolution QSM, we employed densely sampled dipole fields to improve the quality of QSM. To verify the High Resolution QSM, we performed a human study, and acquired QSM input phase data of a healthy human subject. We compared MIP of the High Resolution QSM to that of the tricubically interpolated conventional QSM. In the High Resolution QSM, visibility of several cerebral blood vessels is improved. This means that a susceptibility map with higher spatial resolution is obtained.

PURPOSE

Quantitative Susceptibility Mapping (QSM) can quantify iron accumulation in a brain tissue and cerebral venous oxygen saturation, which will provide important information on neurodegenerative disease and cerebral functions. Moreover, precise local mapping of susceptibility distribution will lead to understanding of the disease and the functions. The susceptibility distribution is obtained by deconvolution of magnetic field perturbations with dipole fields. Since a dipole field has a large change within a small region near the origin, partial volume effects of a dipole field due to digital sampling degrades the quality of a susceptibility map.1 To overcome this limitation, we proposed a new method of QSM, High Resolution QSM, where a densely sampled dipole field was employed. The High Resolution QSM improved the quality of a susceptibility map in numerical simulations2 and phantom experiments.3 In this study, we examined visibility of cerebral blood vessels to verify the High Resolution QSM.

MATERIALS AND METHODS

DATA ACQUISITION: Phase data of a healthy human subject (male, 42 years) was acquired with a 3T whole-body MRI scanner (Tim Trio, Siemens Medical Solutions, Erlangen, Germany) using a 12 channel head coil. A 3D flow compensated single-echo gradient-echo sequence was used to image the subject with parameters: TR/TE = 30/22 ms; FA = 20°; FOV = 230 × 230 × 152 mm3; bandwidth = 90 Hz/voxel; two different isotropic resolutions = 1.2 and 0.6 mm. We calculated a susceptibility map by using the multiple orientation sampling (COSMOS).4 The head was tilted around the left-right axis (13.1°, -6.9° and -23.6°). Each head position was automatically aligned by the scanner software. This study was approved by the Kyoto University Graduate School and Faculty of Medicine, Ethics Committee and informed consent was obtained from the subject.

DATA PROCESSING: Phase data were reconstructed by adding data from each channel without weighting, and unwrapped using the 3D best path phase unwrapping algorithm.5 Background fields were removed by the SHARP method,6 and image registration for different orientations and spatial resolutions was done with SPM12. We created Maximal Intensity Projections (MIPs) of QSM images over the region corresponding to a 15.6 mm-thick sagittal slab centered at the boundary between the two hemispheres in the standard brain.

SUSCEPTIBILITY ESTIMATION: A susceptibility map of the High Resolution QSM was calculated by deconvolution of expanded perturbed fields with twice higher resolution dipole fields. As a result, the High Resolution QSM image had twice smaller voxel size than input data. In conventional QSM, deconvolution of input data with dipole fields which have same resolution as input data was performed. Here, we compared MIPs of the High Resolution QSM image and the conventional QSM image calculated from lower resolution (1.2 mm) data with MIP of the conventional QSM image from higher resolution (0.6 mm) data. The lower resolution conventional QSM image was tricubically interpolated to have twice smaller voxel size.

RESULTS

Susceptibility values of sub-structures of the basal ganglia were consistent with those of previous studies. Figure 1 shows MIPs obtained using the conventional QSM image of the lower resolution (Fig.1(a)), the High Resolution QSM image (Fig.1(b)) and the conventional QSM image of the higher resolution (Fig.1(c)). Vein contrasts are similar in all three MIPs. Frontal part of the internal cerebral vein indicated in red circle, however, has a different appearance: disconnected in Fig.1(a); connected in Fig.1(b), (c). In addition, in several regions indicated by red arrows of Fig.1(a), visibility of small vessels is degraded from those in the same regions of Fig.1(b), (c) such as disconnected, missing and shortening.

DISCUSSION

We applied the High Resolution QSM to a human subject. Several cerebral blood vessels are visualized more clearly. Thus, our proposed method, the High Resolution QSM, improves spatial resolution of a susceptibility map. The visibility improvement of a susceptibility map can be caused by that denser sampled dipole fields in QSM processing reduce partial volume effects due to digital sampling. This visibility improvement will enable to investigate small vessel-related abnormalities such as vascular malformations7 and microinfarcts8. Since the High Resolution QSM only relies on the post software processing, long scan time due to high resolution imaging can be reduced, that is beneficial to all patients.

CONCLUSION

Our proposed method, High Resolution QSM, in which high resolution dipole fields are used, improves the visibility of a susceptibility map of a human brain.

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number 26461825.

References

[1]. Murashima M, et al. Effective Digitized Spatial Size of Unit Dipole Field in Quantitative Susceptibility Mapping. EMBC Conf Proc. 2013; 1049-1052. [2]. Umemoto Y, et al. Effects of Densely Sampled Dipole Field on Quantitative Susceptibility Mapping. EMBC Conf Proc. 2014; 2352-2355. [3]. Umemoto Y, et al. To be submitted. [4]. Liu T, et al. Calculation of Susceptibility Through Multiple Orientation Sampling (COSMOS): A Method for Conditioning the Inverse Problem From Measured Magnetic Field Map to Susceptibility Source Image in MRI. MRM. 2009; 64: 196-204. [5]. Abdul-Rahman HS, et al. Fast and robust three-dimensional best path phase unwrapping algorithm. Appl Opt. 2007; 46(26): 6623-6635. [6]. Schweser F, et al. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?. NeuroImage. 2011; 54: 2789-2807. [7]. Schreiber SJ, et al. Transcranial ultrasonography of cerebral veins and sinuses. European Journal of Ultrasound. 2002; 16: 59-72. [8]. Rooden SV, et al. Increased Number of Microinfarcts in Alzheimer Disease at 7-T MR Imaging. Radiology. 2014; 270(1): 205-211.

Figures

Figure. 1 MIPs of (a) conventional QSM from lower resolution data, (b) High Resolution QSM from lower resolution data and (c) conventional QSM from higher resolution data.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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