Ultra-High Field Template-Assisted Target Selection for Deep Brain Stimulation Surgery

Evaluating the role of 7-Tesla magnetic resonance imaging in neurosurgery: Trends in literature since clinical approval

BACKGROUND

After approval for clinical use in 2017, early investigations of ultra-high-field abdominal magnetic resonance imaging (MRI) have demonstrated its feasibility as well as diagnostic capabilities in neuroimaging. However, there are no to few systematic reviews covering the entirety of its neurosurgical applications as well as the trends in the literature with regard to the aforementioned application.

AIM

To assess the impact of 7-Tesla MRI (7T MRI) on neurosurgery, focusing on its applications in diagnosis, treatment planning, and postoperative assessment, and to systematically analyze and identify patterns and trends in the existing literature related to the utilization of 7T MRI in neurosurgical contexts.

METHODS

A systematic search of PubMed was conducted for studies published between January 1, 2017, and December 31, 2023, using MeSH terms related to 7T MRI and neurosurgery. The inclusion criteria were: Studies involving patients of all ages, meta-analyses, systematic reviews, and original research. The exclusion criteria were: Pre-prints, studies with insufficient data (e.g., case reports and letters), non-English publications, and studies involving animal subjects. Data synthesis involved standardized extraction forms, and a narrative synthesis was performed.

RESULTS

We identified 219 records from PubMed within our defined period, with no duplicates or exclusions before screening. After screening, 125 articles were excluded for not meeting inclusion criteria, leaving 94 reports. Of these, 2 were irrelevant to neurosurgery and 7 were animal studies, resulting in 85 studies included in our systematic review. Data were categorized by neurosurgical procedures and diseases treated using 7T MRI. We also analyzed publications by country and the number of 7T MRI facilities per country was also presented. Experimental studies were classified into comparison and non-comparison studies based on whether 7T MRI was compared to lower field strengths.

CONCLUSION

7T MRI holds great potential in improving the characterization and understanding of various neurological and psychiatric conditions that may be neurosurgically treated. These include epilepsy, pituitary adenoma, Parkinson's disease, cerebrovascular diseases, trigeminal neuralgia, traumatic head injury, multiple sclerosis, glioma, and psychiatric disorders. Superiority of 7T MRI over lower field strengths was demonstrated in terms of image quality, lesion detection, and tissue characterization. Findings suggest the need for accelerated global distribution of 7T magnetic resonance systems and increased training for radiologists to ensure safe and effective integration into routine clinical practice.

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging datasets and templates. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (N = 132 subjects for a total of 15,232 fiducials) and 14 brain templates (4,288 fiducials), totalling more than 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1 - 2 mm (using more than 45,000 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure allowing for facile incorporation into neuroimaging analysis pipelines.

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson's disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducial (AFID) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinson's disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

HybraPD atlas: Towards precise subcortical nuclei segmentation using multimodality medical images in patients with Parkinson disease

Human brain atlases are essential for research and surgical treatment of Parkinson's disease (PD). For example, deep brain stimulation for PD often requires human brain atlases for brain structure identification. However, few atlases can provide disease-specific subcortical structures for PD, and most of them are based on T1w and T2w images. In this work, we construct a HybraPD atlas using fused quantitative susceptibility mapping (QSM) and T1w images from 87 patients with PD. The constructed HybraPD atlas provides a series of templates, that is, T1w, GRE magnitude, QSM, R2*, and brain tissue probabilistic maps. Then, we manually delineate a parcellation map with 12 bilateral subcortical nuclei, which are highly related to PD pathology, such as sub-regions in globus pallidus and substantia nigra. Furthermore, we build a whole-brain parcellation map by combining existing cortical parcellation and white-matter segmentation with the proposed subcortical nuclei map. Considering the multimodality of the HybraPD atlas, the segmentation accuracy of each nucleus is evaluated using T1w and QSM templates, respectively. The results show that the HybraPD atlas provides more accurate segmentation than existing atlases. Moreover, we analyze the metabolic difference in subcortical nuclei between PD patients and healthy control subjects by applying the HybraPD atlas to calculate uptake values of contrast agents on positron emission tomography (PET) images. The atlas-based analysis generates accurate disease-related brain nuclei segmentation on PET images. The newly developed HybraPD atlas could serve as an efficient template to study brain pathological alterations in subcortical regions for PD research.

Image Guidance in Deep Brain Stimulation Surgery to Treat Parkinson's Disease: A Comprehensive Review

Deep brain stimulation (DBS) is an effective therapy as an alternative to pharmaceutical treatments for Parkinson's disease (PD). Aside from factors such as instrumentation, treatment plans, and surgical protocols, the success of the procedure depends heavily on the accurate placement of the electrode within the optimal therapeutic targets while avoiding vital structures that can cause surgical complications and adverse neurologic effects. Although specific surgical techniques for DBS can vary, interventional guidance with medical imaging has greatly contributed to the development, outcomes, and safety of the procedure. With rapid development in novel imaging techniques, computational methods, and surgical navigation software, as well as growing insights into the disease and mechanism of action of DBS, modern image guidance is expected to further enhance the capacity and efficacy of the procedure in treating PD. This article surveys the state-of-the-art techniques in image-guided DBS surgery to treat PD, and discusses their benefits and drawbacks, as well as future directions on the topic.

BRAHMA: Population specific T1, T2, and FLAIR weighted brain templates and their impact in structural and functional imaging studies

Differences in brain morphology across population groups necessitate creation of population-specific Magnetic Resonance Imaging (MRI) brain templates for interpretation of neuroimaging data. Variations in the neuroanatomy in a genetically heterogeneous population make the development of a population-specific brain template for the Indian subcontinent imperative. A dataset of high-resolution 3D T1, T2-weighted, and FLAIR images acquired from a group of 113 volunteers (M/F - 56/57, mean age-28.96 ± 7.80 years) are used to construct T1, T2-weighted, and FLAIR templates, collectively referred to as Indian Brain Template, "BRAHMA". A processing pipeline is developed and implemented in a MATLAB based toolbox for template construction and generation of tissue probability maps and segmentation atlases, with additional labels for deep brain regions such as the Substantia Nigra generated from the T2-weighted and FLAIR templates. The use of BRAHMA template for analysis of structural and functional neuroimaging data obtained from Indian participants, provides improved accuracy with statistically significant results over that obtained using the ICBM-152 (International Consortium for Brain Mapping) template. Our results indicate that segmentations generated on structural images are closer in volume to those obtained from registration to the BRAHMA template than to the ICBM-152. Furthermore, functional MRI data obtained for Working Memory and Finger Tapping paradigms processed using the BRAHMA template show a significantly higher percentage of the activation area than ICBM-152 in relevant brain regions, i.e. the left middle frontal gyrus, and the left and right precentral gyri, respectively. The availability of different image contrasts, tissue maps, and segmentation atlases makes the BRAHMA template a comprehensive tool for multi-modal image analysis in laboratory and clinical settings.

A framework for evaluating correspondence between brain images using anatomical fiducials

Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration. Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.