Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

Shamonin, Denis P; Bron, Esther E.; Lelieveldt, Boudewijn P. F.; Smits, Marion; Klein, Stefan; Staring, Marius

doi:10.3389/fninf.2013.00050

Cited by 440 publications

(435 citation statements)

References 47 publications

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“…Additionally, we acquired whole-brain DTI data, fluid-attenuated inversion recovery (FLAIR), and T 1 -weighted (T1W) images. The FLAIR and T1W images were coregistered to the FEXI data using the Elastix software package (40,41). The study was approved by the Regional Ethical Review Board at Lund University, and all subjects gave written informed consent.…”

Section: Methodsological Validationsmentioning

confidence: 99%

Optimal experimental design for filter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

Lampinen

Szczepankiewicz

Westen

et al. 2017

Magnetic Resonance in Med

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Purpose: Filter exchange imaging (FEXI) is sensitive to the rate of diffusional water exchange, which depends, eg, on the cell membrane permeability. The aim was to optimize and analyze the ability of FEXI to infer differences in the apparent exchange rate (AXR) in the brain between two populations. Methods: A FEXI protocol was optimized for minimal measurement variance in the AXR. The AXR variance was investigated by test-retest acquisitions in six brain regions in 18 healthy volunteers. Preoperative FEXI data and postoperative microphotos were obtained in six meningiomas and five astrocytomas. Results: Protocol optimization reduced the coefficient of variation of AXR by approximately 40%. Test-retest AXR values were heterogeneous across normal brain regions, from 0.3 6 0.2 s À1

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Section: Methodsological Validationsmentioning

confidence: 99%

Optimal experimental design for filter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

Lampinen

Szczepankiewicz

Westen

et al. 2017

Magnetic Resonance in Med

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show abstract

“…The image post-processing was performed with software developed in-house using Matlab (Mathworks Inc., El Segundo, CA, USA) integrated with Elastix registration library (26,27). All images were morphed to the space of a reference to obtain anatomically matched T 1ρ -weighed images using a previously developed technique (21).…”

Section: Image Processingmentioning

confidence: 99%

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Pedoia¹,

Russell²,

Randolph³

et al. 2016

Quant. Imaging Med. Surg.

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Background: Quantitative MR, including T 1ρ mapping, has been extensively used to probe early biochemical changes in knee articular cartilage of subjects with osteoarthritis (OA) and others at risk for cartilage degeneration, such as those with anterior cruciate ligament (ACL) injury and reconstruction.However, limited studies have been performed aimed to assess the spatial location and patterns of T 1ρ . In this study we used a novel voxel-based relaxometry (VBR) technique coupled with principal component analysis (PCA) to extract relevant features so as to describe regional patterns and to investigate their similarities and differences in T 1ρ maps in subjects with OA and subjects six months after ACL reconstruction (ACLR).Methods: T 1ρ quantitative MRI images were collected for 180 subjects from two separate cohorts. The OA cohort included 93 osteoarthritic patients and 25 age-matched controls. The ACLR-6M cohort included 52 patients with unilateral ACL tears who were imaged 6 months after ACL reconstruction, and 10 agematched controls. Non-rigid registration on a single template and local Z-score conversion were adopted for T 1ρ spatial and intensity normalization of all the images in the dataset. PCA was used as a data dimensionality reduction to obtain a description of all subjects in a 10-dimensional feature space. Logistic linear regression was used to identify distinctive features of OA and ACL subjects Conclusions: This study is the first example of T 1ρ local/regional pattern analysis and data-driven feature extraction in knees with cartilage degeneration. Our results revealed similarities and differences between OA and ACL relaxation patterns that could be potentially useful to better understand the pathogenesis of posttraumatic cartilage degeneration and the identification of imaging biomarkers for the early stratification of subjects at risk for developing post-traumatic OA.

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“…The method has not been optimized for speed, however, and parallelization can significantly reduce analysis time. 32 …”

Section: Methods Optimizationmentioning

confidence: 99%

Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

et al. 2015

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Abstract. The Alberta Stroke Program Early CT score (ASPECTS) scoring method is frequently used for quantifying early ischemic changes (EICs) in patients with acute ischemic stroke in clinical studies. Varying interobserver agreement has been reported, however, with limited agreement. Therefore, our goal was to develop and evaluate an automated brain densitometric method. It divides CT scans of the brain into ASPECTS regions using atlas-based segmentation. EICs are quantified by comparing the brain density between contralateral sides. This method was optimized and validated using CT data from 10 and 63 patients, respectively. The automated method was validated against manual ASPECTS, stroke severity at baseline and clinical outcome after 7 to 10 days (NIH Stroke Scale, NIHSS) and 3 months (modified Rankin Scale). Manual and automated ASPECTS showed similar and statistically significant correlations with baseline NIHSS (R ¼ −0.399 and −0.277, respectively) and with follow-up mRS (R ¼ −0.256 and −0.272), except for the follow-up NIHSS. Agreement between automated and consensus ASPECTS reading was similar to the interobserver agreement of manual ASPECTS (differences <1 point in 73% of cases). The automated ASPECTS method could, therefore, be used as a supplementary tool to assist manual scoring.

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Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease

Cited by 440 publications

References 47 publications

Optimal experimental design for filter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

Optimal experimental design for filter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

Principal component analysis-T1ρ voxel based relaxometry of the articular cartilage: a comparison of biochemical patterns in osteoarthritis and anterior cruciate ligament subjects

Automated brain computed tomographic densitometry of early ischemic changes in acute stroke

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