Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology

Fushimi, Yasutaka; Nakajima, Satoshi; Sakata, Akihiko; Okuchi, Sachi; Otani, Sayo; Nakamoto, Yuji

doi:10.1002/jmri.29010

Cited by 5 publications

(2 citation statements)

References 107 publications

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“…Apart from non-invasive mapping of relaxation times like T1, T2 and T2* or diffusion-based parameters, characterization of the magnetic susceptibility distribution using MRI, commonly referred to as quantitative susceptibility mapping (QSM), has attracted considerable interest, particularly for characterizing iron and calcium accumulations ( Schweser et al, 2010 ), myelination ( Li et al, 2011 ) and venous vasculature ( Fan et al, 2014 ; Ward et al, 2018 ) in the human brain. Thus, the specific information that can be evaluated with QSM is useful for many neurological and psychological applications ( Fushimi et al, 2023 ). In QSM, the susceptibility distribution is deduced via sophisticated processing from raw gradient-recalled echo phase images commonly considering the corresponding magnitude images as source for prior information.…”

Section: Introductionmentioning

confidence: 99%

Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution

Graf,

Wohlgemuth,

Deistung

2024

Front. Neurosci.

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Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-to-susceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pre-training on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data.

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Section: Introductionmentioning

confidence: 99%

Incorporating a-priori information in deep learning models for quantitative susceptibility mapping via adaptive convolution

Graf,

Wohlgemuth,

Deistung

2024

Front. Neurosci.

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“…The patient presented with a severe left-hand tremor with significant hypertrophy of the right thalamus. To target the Vim within the hypertrophic thalamus, we referred to quantitative susceptibility mapping (QSM) images [17][18][19] and the atlas-to-patient warped Vim thalamus images. 20,21 The patient underwent radiofrequency (RF) thalamotomy using the modified right Vim target.…”

mentioning

confidence: 99%

Quantitative susceptibility mapping and a nonlinearly transformed atlas for targeting the ventral intermediate nucleus of the thalamus in a patient with tremor and thalamic hypertrophy: illustrative case

Ohtsuki,

Sawada,

Yoshizaki

et al. 2024

Journal of Neurosurgery: Case Lessons

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BACKGROUND The ventral intermediate nucleus (Vim) of the thalamus is a surgical target for treating various types of tremor. Because it is difficult to visualize the Vim using standard magnetic resonance imaging, the structure is usually targeted based on the anterior and posterior commissures. This standard targeting method is practical in most patients but not in those with thalamic asymmetry. The authors examined the usefulness of quantitative susceptibility mapping (QSM) and transformed Vim atlas images to estimate the Vim localization in a patient with tremor and significant thalamic hypertrophy. OBSERVATIONS A 51-year-old right-handed female had experienced a predominant left-hand action tremor for 6 years. Magnetic resonance imaging showed significant hypertrophy of the right thalamus and caudal shift of the thalamic ventral border. The authors referred to the QSM images to localize the decreased susceptibility area within the lateral ventral thalamic nuclei to target the Vim. In addition, the nonlinearly transformed Vim atlas images complemented the imaging-based targeting. The radiofrequency thalamotomy at the modified Vim target relieved the tremor completely. LESSONS A combination of QSM and nonlinear transformation of the thalamic atlas can be helpful in the targeting method of the Vim for tremor patients with thalamic asymmetry.

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