Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

Neuroimaging of Parkinson's disease by quantitative susceptibility mapping

Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD. This review elucidates the biochemical underpinnings of brain iron accumulation, details advancements in iron-sensitive MRI technologies, and discusses the role of QSM as a biomarker of iron deposition in PD. Despite considerable progress, several challenges impede its clinical application after a decade of QSM studies. The initiation of multi-site research is warranted for developing robust, interpretable, and disease-specific biomarkers for monitoring PD disease progression.

QSM Throughout the Body

Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied in the brain, but is increasingly utilized outside the brain. QSM relies on the effect of tissue magnetic susceptibility sources on the MR signal phase obtained with gradient echo sequence. However, in the body, the chemical shift of fat present within the region of interest contributes to the MR signal phase as well. Therefore, correcting for the chemical shift effect by means of water-fat separation is essential for body QSM. By employing techniques to compensate for cardiac and respiratory motion artifacts, body QSM has been applied to study liver iron and fibrosis, heart chamber blood and placenta oxygenation, myocardial hemorrhage, atherosclerotic plaque, cartilage, bone, prostate, breast calcification, and kidney stone.

STN-DBS electrode placement accuracy and motor improvement in Parkinson's disease: systematic review and individual patient meta-analysis

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective neurosurgical treatment for Parkinson's disease. Surgical accuracy is a critical determinant to achieve an adequate DBS effect on motor performance. A two-millimetre surgical accuracy is commonly accepted, but scientific evidence is lacking. A systematic review and meta-analysis of study-level and individual patient data (IPD) was performed by a comprehensive search in MEDLINE, EMBASE and Cochrane Library. Primary outcome measures were (1) radial error between the implanted electrode and target; (2) DBS motor improvement on the Unified Parkinson's Disease Rating Scale part III (motor examination). On a study level, meta-regression analysis was performed. Also, publication bias was assessed. For IPD meta-analysis, a linear mixed effects model was used. Forty studies (1391 patients) were included, reporting radial errors of 0.45-1.86 mm. Errors within this range did not significantly influence the DBS effect on motor improvement. Additional IPD analysis (206 patients) revealed that a mean radial error of 1.13±0.75 mm did not significantly change the extent of DBS motor improvement. Our meta-analysis showed a huge publication bias on accuracy data in DBS. Therefore, the current literature does not provide an unequivocal upper threshold for acceptable accuracy of STN-DBS surgery. Based on the current literature, DBS-electrodes placed within a 2 mm range of the intended target do not have to be repositioned to enhance motor improvement after STN-DBS for Parkinson's disease. However, an indisputable upper cut-off value for surgical accuracy remains to be established. PROSPERO registration number is CRD42018089539.

Targeting for stereotactic radiosurgical thalamotomy based on tremor treatment response

OBJECTIVE

Stereotactic radiosurgery (SRS) treats severe, medically refractory essential tremor and tremor-dominant Parkinson disease. However, the optimal target for SRS treatment within the thalamic ventral intermediate nucleus (VIM) is not clearly defined. This work evaluates the precision of the physician-selected VIM target, and determines the optimal SRS target within the VIM by correlation between early responders and nonresponders.

METHODS

Early responders and nonresponders were assessed retrospectively by Elements Basal Ganglia Atlas autocontouring of the VIM on the pre-SRS-treatment 1-mm slice thickness T1-weighted MRI and correlating the center of the post-SRS-treatment lesion. Using pre- and posttreatment diffusion tensor imaging, the fiber tracking package in the Elements software generated tremor-related tracts from autosegmented motor cortex, thalamus, red nucleus, and dentate nucleus. Autocontouring of the VIM was successful for all patients.

RESULTS

Among 23 patients, physician-directed SRS targets had a medial-lateral target range from +2.5 mm to -2.0 mm from the VIM center. Relative to the VIM center, the SRS isocenter target was 0.7-0.9 mm lateral for 6 early responders and 0.9-1.1 mm medial for 4 nonresponders (p = 0.019), and without differences in the other dimensions: 0.2 mm posterior and 0.6 mm superior. Dose-volume histogram analyses for the VIM had no significant differences between responders and nonresponders between 20 Gy and 140 Gy, mean or maximum dose, and dose to small volumes. Tractography data was obtained for 4 patients.

CONCLUSIONS

For tremor control in early responders, the Elements Basal Ganglia Atlas autocontour for the VIM provides the optimal SRS target location that is 0.7-0.9 mm lateral to the VIM center.

Deep brain stimulation in Parkinson's disease: state of the art and future perspectives

For more than 30 years, Deep Brain Stimulation (DBS) has been a therapeutic option for Parkinson's disease (PD) treatment. However, this therapy is still underutilized mainly due to misinformation regarding risks and clinical outcomes. DBS can ameliorate several motor and non-motor symptoms, improving patients' quality of life. Furthermore, most of the improvement after DBS is long-lasting and present even in advanced PD. Adequate patient selection, precise electric leads placement, and correct DBS programming are paramount for good surgical outcomes. Nonetheless, DBS still has many limitations: axial symptoms and signs, such as speech, balance and gait, do not improve to the same extent as appendicular symptoms and can even be worsened as a direct or indirect consequence of surgery and stimulation. In addition, there are still unanswered questions regarding patient's selection, surgical planning and programming techniques, such as the role of surgicogenomics, more precise imaging-based lead placement, new brain targets, advanced programming strategies and hardware features. The net effect of these innovations should not only be to refine the beneficial effect we currently observe on selected symptoms and signs but also to improve treatment resistant facets of PD, such as axial and non-motor features. In this review, we discuss the current state of the art regarding DBS selection, implant, and programming, and explore new advances in the DBS field.

The future perspectives of psychiatric neurosurgery

The future of psychiatric neurosurgery can be viewed from two separate perspectives: the immediate future and the distant future. Both show promise, but the treatment strategy for mental diseases and the technology utilized during these separate periods will likely differ dramatically. It can be expected that the initial advancements will be built upon progress of neuroimaging and stereotactic targeting while surgical technology becomes adapted to patient-specific symptomatology and structural/functional imaging parameters. This individualized approach has already begun to show significant promise when applied to deep brain stimulation for treatment-resistant depression and obsessive-compulsive disorder. If effectiveness of these strategies is confirmed by well designed, double-blind, placebo-controlled clinical studies, further technological advances will continue into the distant future, and will likely involve precise neuromodulation at the cellular level, perhaps using wireless technology with or without closed-loop design. This approach, being theoretically less invasive and carrying less risk, may ultimately propel psychiatric neurosurgery to the forefront in the treatment algorithm of mental illness. Despite prominent development of non-invasive therapeutic options, such as stereotactic radiosurgery or transcranial magnetic resonance-guided focused ultrasound, chances are there will still be a need in surgical management of patients with most intractable psychiatric conditions.

A literature review of magnetic resonance imaging sequence advancements in visualizing functional neurosurgery targets

OBJECTIVE

Historically, preoperative planning for functional neurosurgery has depended on the indirect localization of target brain structures using visible anatomical landmarks. However, recent technological advances in neuroimaging have permitted marked improvements in MRI-based direct target visualization, allowing for refinement of "first-pass" targeting. The authors reviewed studies relating to direct MRI visualization of the most common functional neurosurgery targets (subthalamic nucleus, globus pallidus, and thalamus) and summarize sequence specifications for the various approaches described in this literature.

METHODS

The peer-reviewed literature on MRI visualization of the subthalamic nucleus, globus pallidus, and thalamus was obtained by searching MEDLINE. Publications examining direct MRI visualization of these deep brain stimulation targets were included for review.

RESULTS

A variety of specialized sequences and postprocessing methods for enhanced MRI visualization are in current use. These include susceptibility-based techniques such as quantitative susceptibility mapping, which exploit the amount of tissue iron in target structures, and white matter attenuated inversion recovery, which suppresses the signal from white matter to improve the distinction between gray matter nuclei. However, evidence confirming the superiority of these sequences over indirect targeting with respect to clinical outcome is sparse. Future targeting may utilize information about functional and structural networks, necessitating the use of resting-state functional MRI and diffusion-weighted imaging.

CONCLUSIONS

Specialized MRI sequences have enabled considerable improvement in the visualization of common deep brain stimulation targets. With further validation of their ability to improve clinical outcomes and advances in imaging techniques, direct visualization of targets may play an increasingly important role in preoperative planning.

MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (χ₁₃, χ₂₃ and χ₃₃) and the acquired single-orientation phase. The convolutional neural networks are embedded into the physical model to learn a regularization term containing prior information. χ₃₃ and phase induced by χ₁₃ and χ₂₃ terms were used as the labels for network training. Quantitative evaluation metrics were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

Multiecho complex total field inversion method (mcTFI) for improved signal modeling in quantitative susceptibility mapping

PURPOSE

Typical quantitative susceptibility mapping (QSM) reconstruction steps consist of first estimating the magnetization field from the gradient-echo images, and then reconstructing the susceptibility map from the estimated field. The errors from the field-estimation steps may propagate into the final QSM map, and the noise in the estimated field map may no longer be zero-mean Gaussian noise, thus, causing streaking artifacts in the resulting QSM. A multiecho complex total field inversion (mcTFI) method was developed to compute the susceptibility map directly from the multiecho gradient echo images using an improved signal model that retains the Gaussian noise property in the complex domain. It showed improvements in QSM reconstruction over the conventional field-to-source inversion.

METHODS

The proposed mcTFI method was compared with the nonlinear total field inversion (nTFI) method in a numerical brain with hemorrhage and calcification, the numerical brains provided by the QSM Challenge 2.0, 18 brains with intracerebral hemorrhage scanned at 3T, and 6 healthy brains scanned at 7T.

RESULTS

Compared with nTFI, the proposed mcTFI showed more accurate QSM reconstruction around the lesions in the numerical simulations. The mcTFI reconstructed QSM also showed the best image quality with the least artifacts in the brains with intracerebral hemorrhage scanned at 3T and healthy brains scanned at 7T.

CONCLUSION

The proposed multiecho complex total field inversion improved QSM reconstruction over traditional field-to-source inversion through better signal modeling.

Direct visualization of deep brain stimulation targets in patients with Parkinson's disease via 3-T quantitative susceptibility mapping

BACKGROUND

The direct visualization of brain nuclei on magnetic resonance (MR) images is important for target localization during deep brain stimulation (DBS) in patients with Parkinson's disease (PD). We demonstrated the superiority of 3-T high-resolution submillimeter voxel size quantitative susceptibility mapping (QSM) for delineating the subthalamic nucleus (STN) and the globus pallidus internus (GPi).

METHODS

Preoperative 3-T QSM and T2 weighted (T2w) images were obtained from ten patients with PD. Qualitative visualization scores were analyzed by two neurosurgeons on both images using a 4-point and 5-point scale, respectively. Images were also compared with regard to contrast-to-noise ratios (CNRs) and edge detection power for the STN and GPi. The Wilcoxon rank-sum test and the signed-rank test were used to compare measurements between the two images.

RESULTS

Visualization scores for the STN and GPi, the mean CNR of the STN relative to the zona incerta (ZI) and the substantia nigra, and the mean CNR of the GPi relative to the internal capsule (IC) and the globus pallidum externum, were significantly higher on QSM images than on T2w images (P < 0.01). The edge detection powers of the STN-ZI and GPi-IC on QSM were significantly larger (by 2.6- and 3.8-fold, respectively) than those on T2w images (P < 0.01). QSM detected asymmetry of the STN in two patients.

CONCLUSIONS

QSM images provided improved delineation ability for the STN and GPi when compared to T2w images. Our findings are important for patients with PD who undergo DBS surgery, particularly those with asymmetric bilateral nuclei.

A Phase 2 Randomized Trial of Asleep versus Awake Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease

OBJECTIVE

Asleep deep brain stimulation (DBS) for Parkinson's disease (PD) is being performed more frequently; however, motor outcomes and safety of asleep DBS have never been assessed in a prospective randomized trial.

METHODS

We conducted a prospective, randomized, noncomparative trial to assess the motor outcomes of asleep DBS. Leads were implanted in the subthalamic nucleus (STN) according to probabilistic stereotactic coordinates with a surgical robot under O-arm© imaging guidance under either general anesthesia without microelectrode recordings (MER) (20 patients, asleep group) or local anesthesia with MER and clinical testing (9 patients, awake group).

RESULTS

The mean motor improvement rates on the Unified Parkinson's Disease Rating Scale Part III (UPDRS-3) between OFF and ON stimulation without medication were 52.3% (95% CI: 45.4-59.2%) in the asleep group and 47.0% (95% CI: 23.8-70.2%) in the awake group, 6 months after surgery. Except for a subcutaneous hematoma, we did not observe any complications related to the surgery. Three patients (33%) in the awake group and 8 in the asleep group (40%) had at least one side effect potentially linked with neurostimulation.

CONCLUSIONS

Owing to its randomized design, our study supports the hypothesis that motor outcomes after asleep STN-DBS in PD may be noninferior to the standard awake procedure.

Improved depiction of subthalamic nucleus and globus pallidus internus with optimized high-resolution quantitative susceptibility mapping at 7 T

The subthalamic nucleus (STN) and globus pallidus internus (GPi) are commonly used targets in deep-brain stimulation (DBS) surgery for the treatment of movement disorders. The success of DBS critically depends on the spatial precision of stimulation. By taking advantage of good contrast between iron-rich deep-brain nuclei and surrounding tissues, quantitative susceptibility mapping (QSM) has shown promise in differentiating the STN and GPi from the adjacent substantia nigra and globus pallidus externus, respectively. Nonlinear morphology-enabled dipole inversion (NMEDI) is a widely used QSM algorithm, but the image quality of reconstructed susceptibility maps relies on the regularization parameter selection. To date, few studies have systematically optimized the regularization parameter at the ultra-high field of 7 T. In this study, we optimized the regularization parameter in NMEDI to improve the depiction of STN and GPi at different spatial resolutions at both 3 T and 7 T. The optimized QSM images were further compared with other susceptibility-based images, including T2*-weighted (T2*w), R2*, susceptibility-weighted, and phase images. QSM showed better depiction of deep-brain nuclei with clearer boundaries compared with the other methods, and 7 T QSM at 0.35 × 0.35 × 1.0 mm³ demonstrated superior performance to the others. Our findings suggest that optimized high-resolution QSM at 7 T allows for improved delineation of deep-brain nuclei with clear and sharp borders between nuclei, which may become a promising tool for DBS nucleus preoperative localization.

Deep Cerebellar Nuclei Segmentation via Semi-Supervised Deep Context-Aware Learning from 7T Diffusion MRI

Deep cerebellar nuclei are a key structure of the cerebellum that are involved in processing motor and sensory information. It is thus a crucial step to accurately segment deep cerebellar nuclei for the understanding of the cerebellum system and its utility in deep brain stimulation treatment. However, it is challenging to clearly visualize such small nuclei under standard clinical magnetic resonance imaging (MRI) protocols and therefore precise segmentation is not feasible. Recent advances in 7 Tesla (T) MRI technology and great potential of deep neural networks facilitate automatic patient-specific segmentation. In this paper, we propose a novel deep learning framework (referred to as DCN-Net) for fast, accurate, and robust patient-specific segmentation of deep cerebellar dentate and interposed nuclei on 7T diffusion MRI. DCN-Net effectively encodes contextual information on the patch images without consecutive pooling operations and adding complexity via proposed dilated dense blocks. During the end-to-end training, label probabilities of dentate and interposed nuclei are independently learned with a hybrid loss, handling highly imbalanced data. Finally, we utilize self-training strategies to cope with the problem of limited labeled data. To this end, auxiliary dentate and interposed nuclei labels are created on unlabeled data by using DCN-Net trained on manual labels. We validate the proposed framework using 7T B0 MRIs from 60 subjects. Experimental results demonstrate that DCN-Net provides better segmentation than atlas-based deep cerebellar nuclei segmentation tools and other state-of-the-art deep neural networks in terms of accuracy and consistency. We further prove the effectiveness of the proposed components within DCN-Net in dentate and interposed nuclei segmentation.

Iron concentration linked to structural connectivity in the subthalamic nucleus: implications for deep brain stimulation

OBJECTIVE

The objective of this study was to investigate the relationship between iron and white matter connectivity in the subthalamic nucleus (STN) in patients undergoing deep brain stimulation (DBS) of the STN for treatment of Parkinson's disease.

METHODS

Nine Parkinson's disease patients underwent preoperative 3T MRI imaging which included acquisition of T1-weighted anatomical images along with diffusion tensor imaging (DTI) and quantitative susceptibility mapping (QSM). MR tractography was performed for the seed voxels located within the STN, and the correlations between normalized QSM values and the STN's connectivity to a set of a priori chosen regions of interest were assessed.

RESULTS

A strong negative correlation was found between STN connectivity and QSM intensity for the thalamus, premotor, motor, and sensory regions, while a strong positive correlation was found for frontal, putamen, and brain stem areas.

CONCLUSIONS

Quantitative susceptibility mapping not only accurately delineates the STN borders but is also able to provide functional information about the STN functional subdivisions. The observed iron-to-connectivity correlation patterns may aid in planning DBS surgery to avoid unwanted side effects associated with DBS.

Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML).

OBJECTIVE

To validate the clinical application accuracy of the 7 T-ML method by comparing it with identification of the STN based on intraoperative microelectrode recordings.

METHODS

Sixteen PD patients who underwent microelectrode-recordings guided STN DBS were included in this study (30 implanted leads and electrode trajectories). The length of the STN along the electrode trajectory and the position of its contacts to dorsal, inside, or ventral to the STN were compared using microelectrode-recordings and the 7 T-ML method computed based on the patient's clinical 3T MRI.

RESULTS

All 30 electrode trajectories that intersected the STN based on microelectrode-recordings, also intersected it when visualized with the 7 T-ML method. STN trajectory average length was 6.2 ± 0.7 mm based on microelectrode recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We observed a 93% agreement regarding contact location between the microelectrode-recordings and the 7 T-ML method.

CONCLUSION

The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.

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

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.

Deep Brain Stimulation for Parkinson Disease

Parkinson disease (PD) is the second most common neurodegenerative disorder and affects more than 1 million individuals in the United States. Deep brain stimulation (DBS) is one form of treatment of PD. DBS treatment is still evolving due to technological innovations that shape how this therapy is used.

Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T

The ventro-intermediate nucleus (Vim), as part of the motor thalamic nuclei, is a commonly used target in functional stereotactic neurosurgery for treatment of drug-resistant tremor. As it cannot be directly visualized on routinely used magnetic resonance imaging (MRI), its clinical targeting is performed using indirect methods. Recent literature suggests that the Vim can be directly visualized on susceptibility-weighted imaging (SWI) acquired at 7 T. Our work aims to assess the distinguishable Vim on 7 T SWI in both healthy-population and patients and, using it as a reference, to compare it with: (1) The clinical targeting, (2) The automated parcellation of thalamic subparts based on 3 T diffusion MRI (dMRI), and (3) The multi-atlas segmentation techniques. In 95.2% of the data, the manual outline was adjacent to the inferior lateral border of the dMRI-based motor-nuclei group, while in 77.8% of the involved cases, its ventral part enclosed the Guiot points. Moreover, the late MRI signature in the patients was always observed in the anterior part of the manual delineation and it overlapped with the multi-atlas outline. Overall, our study provides new insight on Vim discrimination through MRI and imply novel strategies for its automated segmentation, thereby opening new perspectives for standardizing the clinical targeting.

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

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 T2 W 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.

Patents on Quantitative Susceptibility Mapping (QSM) of Tissue Magnetism

BACKGROUND

Quantitative susceptibility mapping (QSM) depicts biodistributions of tissue magnetic susceptibility sources, including endogenous iron and calcifications, as well as exogenous paramagnetic contrast agents and probes. When comparing QSM with simple susceptibility weighted MRI, QSM eliminates blooming artifacts and shows reproducible tissue susceptibility maps independent of field strength and scanner manufacturer over a broad range of image acquisition parameters. For patient care, QSM promises to inform diagnosis, guide surgery, gauge medication, and monitor drug delivery. The Bayesian framework using MRI phase data and structural prior knowledge has made QSM sufficiently robust and accurate for routine clinical practice.

OBJECTIVE

To address the lack of a summary of US patents that is valuable for QSM product development and dissemination into the MRI community.

METHOD

We searched the USPTO Full-Text and Image Database for patents relevant to QSM technology innovation. We analyzed the claims of each patent to characterize the main invented method and we investigated data on clinical utility.

RESULTS

We identified 17 QSM patents; 13 were implemented clinically, covering various aspects of QSM technology, including the Bayesian framework, background field removal, numerical optimization solver, zero filling, and zero-TE phase.

CONCLUSION

Our patent search identified patents that enable QSM technology for imaging the brain and other tissues. QSM can be applied to study a wide range of diseases including neurological diseases, liver iron disorders, tissue ischemia, and osteoporosis. MRI manufacturers can develop QSM products for more seamless integration into existing MRI scanners to improve medical care.

Clinical Integration of Quantitative Susceptibility Mapping Magnetic Resonance Imaging into Neurosurgical Practice

OBJECTIVE

To introduce quantitative susceptibility mapping (QSM), a novel magnetic resonance imaging sequence, to the field of neurosurgery.

METHODS

QSM is introduced both in its historical context and by providing a brief overview of the physics behind the technique tailored to a neurosurgical audience. Its application to clinical neurosurgery is then highlighted using case examples.

RESULTS

QSM offers a quantitative assessment of susceptibility (previously considered as an artifact) via a single, straightforward gradient echo acquisition. QSM differs from standard susceptibility weighted imaging in its ability to both quantify and precisely localize susceptibility effects. Clinical applications of QSM are wide reaching and include precise localization of the deep nuclei for deep brain stimulation electrode placement, differentiation between blood products and calcification within brain lesions, and enhanced sensitivity of cerebral micrometastasis identification.

CONCLUSIONS

We present this diverse range of QSM's clinical applications to neurosurgical care via case examples. QSM can be obtained in all patients able to undergo magnetic resonance imaging and is easily integratable into busy neuroradiology programs because of its short acquisition time and straightforward, automated offline postprocessing workflow. Clinical integration of QSM may help clinicians better identify and characterize neurosurgical lesions, thereby improving patient care.

Awake versus Asleep Deep Brain Stimulation Surgery: Technical Considerations and Critical Review of the Literature

Advancements in neuroimaging have led to a trend toward direct, image-based targeting under general anesthesia without the use of microelectrode recording (MER) or intraoperative test stimulation, also referred to as “asleep” deep brain stimulation (DBS) surgery. Asleep DBS, utilizing imaging in the form of intraoperative computed tomography (iCT) or magnetic resonance imaging (iMRI), has demonstrated reliable targeting accuracy of DBS leads implanted within the globus pallidus and subthalamic nucleus while also improving clinical outcomes in patients with Parkinson’s disease. In lieu, of randomized control trials, retrospective comparisons between asleep and awake DBS with MER have shown similar short-term efficacy with the potential for decreased complications in asleep cohorts. In lieu of long-term outcome data, awake DBS using MER must demonstrate more durable outcomes with fewer stimulation-induced side effects and lead revisions in order for its use to remain justifiable; although patient-specific factors may also be used to guide the decision regarding which technique may be most appropriate and tolerable to the patient.