Multi-contrast unbiased MRI atlas of a Parkinson’s disease population

Xiao, Yiming; Fonov, Vladimir S.; Bériault, Silvain; Subaie, Fahd Al; Chakravarty, M. Mallar; Sadikot, Abbas F.; Pike, G. Bruce; Collins, D. Louis

doi:10.1007/s11548-014-1068-y

Cited by 76 publications

(63 citation statements)

References 47 publications

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“…We employed a similar technique to that used by Fonov et al [17] and Xiao et al [10] to non-rigidly warp the 7T T2w images for the 12 subjects to a common template space and generate the unbiased average. Prior to averaging, the 7T T2w images were resampled back to 0.6mm isotropic resolution.…”

Section: Generation Of Unbiased Averagementioning

confidence: 99%

“…In a retrospective study, this visualization system generated DBS targets that were as much as 1.3mm closer to the actual surgical targets than DBS targets generated by traditional brain atlases [12]. Similarly, Xiao et al [10] developed several 3T multi-contrast unbiased templates using PD patients that were capable of visualizing both cortical and subcortical structures. Neither of these works utilized ultra-high-field imaging, which is a staple in clinical research.…”

Section: Introductionmentioning

confidence: 99%

“…In a typical DBS operation, the general location of the target structure is estimated by aligning anatomical atlases, usually the Schaltenbrand [8] or Talairach [9], to a patient's pre-operative MR images at the anterior commissure and posterior commissure. The location of the target structure is then fine-tuned intra-operatively by inserting parallel electrodes and recording electrophysiological data [7,10]. The high variability in shape and size of the STN [11] generally results in a greater number of exploratory electrodes being inserted, a longer overall operation time, and a greater risk to the patient.…”

Section: Introductionmentioning

confidence: 99%

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Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

et al. 2016

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Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods T1 and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p < 0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

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Section: Generation Of Unbiased Averagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

et al. 2016

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“…The mesencephalon and sub-cortical regions called basal ganglia and substantia nigra are implicated in Parkinson's disease. The restoration of the uniformity in these brain regions can improve the analysis to characterize the appearance of Parkinson's disease in MRI data [52,53].…”

Section: Experiments With the Brain Images Of Parkinson's Disease Patmentioning

confidence: 99%

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

et al. 2017

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Abstract:The reconstruction of MRI data assumes a uniform radio-frequency field. However, in practice, the radio-frequency field is inhomogeneous and leads to anatomically inconsequential intensity non-uniformities across an image. An anatomic region can be imaged with multiple contrasts reconstructed independently and be suffering from different non-uniformities. These artifacts can complicate the further automated analysis of the images. A method is presented for the joint intensity uniformity restoration of two such images. The effect of the intensity distortion on the auto-co-occurrence statistics of each image as well as on the joint-co-occurrence statistics of the two images is modeled and used for their non-stationary restoration followed by their back-projection to the images. Several constraints that ensure a stable restoration are also imposed. Moreover, the method considers the inevitable differences between the signal regions of the two images. The method has been evaluated extensively with BrainWeb phantom brain data as well as with brain anatomic data from the Human Connectome Project (HCP) and with data of Parkinson's disease patients. The performance of the proposed method has been compared with that of the N4ITK tool. The proposed method increases tissues contrast at least 4.62 times more than the N4ITK tool for the BrainWeb images. The dynamic range with the N4ITK method for the same images is increased by up to +29.77%, whereas, for the proposed method, it has a corresponding limited decrease of −1.15%, as expected. The validation has demonstrated the accuracy and stability of the proposed method and hence its ability to reduce the requirements for additional calibration scans.

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“…The blending weights are computed from the R2* map obtained from the multiecho FLASH sequence, and the estimated feature differences of the T1w and T2*w MRIs. The detailed steps for such fully automatic image fusion method are described in [46]. In Fig.…”

Section: T1-t2* Fusion Mrimentioning

confidence: 99%

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson’s disease

et al. 2014

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Multi-contrast unbiased MRI atlas of a Parkinson’s disease population

Abstract: The proposed unbiased templates are more representative of the population of interest and can benefit both the surgical planning and research of PD.

Cited by 76 publications

References 47 publications

Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

Restoration of Bi-Contrast MRI Data for Intensity Uniformity with Bayesian Coring of Co-Occurrence Statistics

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson’s disease

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