Utilization of Quantitative Susceptibility Mapping for Direct Targeting of the Subthalamic Nucleus During Deep Brain Stimulation Surgery

Rasouli, Jonathan; Ramdhani, Ritesh A.; Panov, Fedor; Dimov, Alexey; Zhang, Yan; Cho, Catherine; Wang, Yi; Kopell, Brian H.

doi:10.1093/ons/opx131

Cited by 35 publications

(29 citation statements)

References 22 publications

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“…12,16,18,33,39,42 One of the sources for these inconsistences is thought to be the inhomogeneous distribution of iron within the STN, 13,16 with the lowest iron concentration typically observed in the dorsolateral region usually targeted in PD patients due to its correspondence to the sensorimotor area. 34,36,41 This heterogeneous iron distribution in the STN is blurred on T2w MRI (Fig. 3).…”

Section: Discussionmentioning

confidence: 93%

“…25 Due to the sensitivity of QSM to the presence of biometals, this technique has been used extensively in studying the iron-rich structures of deep gray matter. Particularly, QSM has been successfully used in STN targeting, 34 where its application resulted a single intraoperative MER in over 85% of cases. The comparative analyses of QSM and T2w presented here suggest that susceptibility mapping is better suited for high-resolution preoperative STN imaging.…”

Section: Discussionmentioning

confidence: 99%

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High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping

Dimov

Gupta

Kopell

et al. 2019

Journal of Neurosurgery

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OBJECTIVE Faithful depiction of the subthalamic nucleus (STN) is critical for planning deep brain stimulation (DBS) surgery in patients with Parkinson's disease (PD). Quantitative susceptibility mapping (QSM) has been shown to be superior to traditional T2-weighted spin echo imaging (T2w). The aim of the study was to describe submillimeter QSM for preoperative imaging of the STN in planning of DBS. METHODS Seven healthy volunteers were included in this study. T2w and QSM were obtained for all healthy volunteers, and images of different resolutions were reconstructed. Image quality and visibility of STN anatomical features were analyzed by a radiologist using a 5-point scale, and contrast properties of the STN and surrounding tissue were calculated. Additionally, data from 10 retrospectively and randomly selected PD patients who underwent 3-T MRI for DBS were analyzed for STN size and susceptibility gradient measurements. RESULTS Higher contrast-to-noise ratio (CNR) values were observed in both high-resolution and low-resolution QSM images. Inter-resolution comparison demonstrated improvement in CNR for QSM, but not for T2w images. QSM provided higher inter-quadrant contrast ratios (CR) within the STN, and depicted a gradient in the distribution of susceptibility sources not visible in T2w images. CONCLUSIONS For 3-T MRI, submillimeter QSM provides accurate delineation of the functional and anatomical STN features for DBS targeting.

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Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 99%

High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping

Dimov

Gupta

Kopell

et al. 2019

Journal of Neurosurgery

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“…However, the distinction among the neighborhood structures is often unclear on the clinical MRI, and the STN appears in only one or two slices, thereby resulting in sub-optimal targeting within the 3D STN (positive effects), and relative to adjacent structures of the STN (potential negative/side effects). There are efforts underway to directly visualize the STN via MRI reconstruction methods such as susceptibility weighted phase imaging or quantitative susceptibility mapping (QSM) (Chandran, Bynevelt, & Lind, 2015; Liu et al, 2013; Rasouli et al, 2017). Moreover, recent studies have proposed automatic methods to segment the STN on these contrast enhanced MR sequences (Garzón, Sitnikov, Bäckman, & Kalpouzos, 2017; Milletari et al, 2017; Visser, Keuken, Forstmann, & Jenkinson, 2016).…”

Section: Introductionmentioning

confidence: 99%

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Kim¹,

Duchin²,

Shamir³

et al. 2018

Human Brain Mapping

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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

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“…This could enable the standard clinical use of QSM in assessing microbleeds ( and Huntington Disease (8), Multiple Sclerosis (17,73,74), Amyotrophic Lateral Sclerosis (75), Wilson Disease (13) or more general in diseases with a dysregulation of iron metabolism (12,76,77). QSM could also help in localizing targets, such as the Subthalamic Nucleus, for Deep Brain Stimulation Surgery (78).…”

Section: Discussionmentioning

confidence: 99%

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

Rasmussen¹,

Kristensen²,

Blendal³

et al. 2018

Preprint

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Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements. The mapping of magnetic susceptibility in vivo has gained broad interest in several fields of science and medicine because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium. Thereby, QSM can also reveal pathological changes of these key components in devastating diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. As QSM requires the solution of an ill-posed field-tosource-inversion, current techniques utilize manual optimization of regularization parameters to balance between smoothing, artifacts and quantification accuracy. We trained a fully convolutional deep neural network -DeepQSM -to invert the magnetic dipole kernel convolution. This network is capable of solving the ill-posed field-to-source inversion on real-world in vivo MRI phase data without the need for manual parameter tuning, which proves that this network has generalized the underlying mathematical principle of the dipole inversion. We demonstrate that DeepQSM's susceptibility maps enable identification of deep brain substructures that are not visible in MRI phase data and provide information on their respective magnetic tissue properties. We illustrate DeepQSM's clinical relevance in a patient with multiple sclerosis showing its sensitivity to white matter lesions. In summary, DeepQSM can be used to determine the composition of myelin sheets of nerve fibers in the brain, and to assess quantitative information on iron homeostasis and its dysregulation, and will subsequently contribute to a better understanding of these biological processes in health and disease.

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Utilization of Quantitative Susceptibility Mapping for Direct Targeting of the Subthalamic Nucleus During Deep Brain Stimulation Surgery

Cited by 35 publications

References 22 publications

High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping

High-resolution QSM for functional and structural depiction of subthalamic nuclei in DBS presurgical mapping

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

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