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Joseph Kuchling

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3 papers

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YNICL Journal 2021 Journal Article

Lateral geniculate nucleus volume changes after optic neuritis in neuromyelitis optica: A longitudinal study

Athina Papadopoulou
Frederike C. Oertel
Claudia Chien
Joseph Kuchling
Hanna G. Zimmermann
Nadja Siebert
Seyedamirhosein Motamedi
Marcus D' Souza

OBJECTIVES: Lateral geniculate nucleus (LGN) volume is reduced after optic neuritis (ON) in neuromyelitis optica spectrum disorders (NMOSD). We aimed at a longitudinal assessment of LGN volume in NMOSD. METHODS: Twenty-nine patients with aquaporin 4-IgG seropositive NMOSD (age: 47.8 ± 14.6 years (y), female: n = 27, history of ON (NMO-ON): n = 17, median time since ON: 3[1.2-12.1]y) and 18 healthy controls (HC; age: 39.3 ± 15.8y; female: n = 13) were included. Median follow-up was 4.1[1.1-4.7]y for patients and 1.7[0.9-3.2]y for HC. LGN volume was measured using a multi-atlas-based approach of automated segmentation on 3 Tesla magnetic resonance images. Retinal optical coherence tomography and probabilistic tractography of the optic radiations (OR) were also performed. RESULTS: ; t = -3.6, p = 0.036). CONCLUSION: Although LGN volume is reduced after ON in NMOSD, this volume loss is not progressive over longer follow-up or independent of ON. Thus, our findings -at least in this relatively small cohort- do not support occult neurodegeneration of the afferent visual pathway in NMOSD.

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YNICL Journal 2019 Journal Article

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Fabian Eitel
Emily Soehler
Judith Bellmann-Strobl
Alexander U. Brandt
Klemens Ruprecht
René M. Giess
Joseph Kuchling
Susanna Asseyer

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge.

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YNICL Journal 2018 Journal Article

Comparison of probabilistic tractography and tract-based spatial statistics for assessing optic radiation damage in patients with autoimmune inflammatory disorders of the central nervous system

Joseph Kuchling
Yael Backner
Frederike C. Oertel
Noa Raz
Judith Bellmann-Strobl
Klemens Ruprecht
Friedemann Paul
Netta Levin

Background: Diffusion Tensor Imaging (DTI) can evaluate microstructural tissue damage in the optic radiation (OR) of patients with clinically isolated syndrome (CIS), early relapsing-remitting multiple sclerosis and neuromyelitis optica spectrum disorders (NMOSD). Different post-processing techniques, e.g. tract-based spatial statistics (TBSS) and probabilistic tractography, exist to quantify this damage. Objective: To evaluate the capacity of TBSS-based atlas region-of-interest (ROI) combination with 1) posterior thalamic radiation ROIs from the Johns Hopkins University atlas (JHU-TBSS), 2) Juelich Probabilistic ROIs (JUEL-TBSS) and tractography methods using 3) ConTrack (CON-PROB) and 4) constrained spherical deconvolution tractography (CSD-PROB) to detect OR damage in patients with a) NMOSD with prior ON (NMOSD-ON), b) CIS and early RRMS patients with ON (CIS/RRMS-ON) and c) CIS and early RRMS patients without prior ON (CIS/RRMS-NON) against healthy controls (HCs). Methods: Twenty-three NMOSD-ON, 18 CIS/RRMS-ON, 21 CIS/RRMS-NON, and 26 HCs underwent 3 T MRI. DTI data analysis was carried out using JUEL-TBSS, JHU-TBSS, CON-PROB and CSD-PROB. Optical coherence tomography (OCT) and visual acuity testing was performed in the majority of patients and HCs. Results: = 0.094). No significant differences between CIS/RRMS-ON and HC were detected by any of the methods. Conclusions: All DTI post-processing techniques facilitated the detection of OR damage in patient groups with severe microstructural OR degradation. The comparison of distinct disease groups by use of different methods may lead to different - either false-positive or false-negative - results. Since different DTI post-processing approaches seem to provide complementary information on OR damage, application of distinct methods may depend on the relevant research question.

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