University of Basel

Author Of 2 Presentations

Imaging Oral Presentation

FC03.03 - Depicting multiple sclerosis pathology at 160μm isotropic resolution by human whole-brain postmortem 3T magnetic resonance imaging

Speakers
Presentation Number
FC03.03
Presentation Topic
Imaging
Lecture Time
13:24 - 13:36

Abstract

Background

Postmortem magnetic resonance imaging (MRI) of formalin-fixed healthy and diseased human brains with ultra-high spatial resolution has the great potential to depict tissue architecture in fine detail, allowing a deeper understanding of pathological processes. Whole-brain imaging is important since it provides neuroanatomic relationships, reference points across distant brain regions, and a comprehensive view of pathologies affecting the brain. However, ultra-high-resolution whole-brain postmortem MRI is challenging and has been so far almost exclusively performed at 7T with specialized hardware.

Objectives

To develop a 3D isotropic 160µm ultra-high-resolution imaging (URI) approach for human whole-brain ex vivo acquisitions on a standard clinical 3T MRI system. To explore the sensitivity and specificity of the approach to specific pathological features of multiple sclerosis (MS).

Methods

A fixed whole human brain from a patient with secondary progressive MS was investigated. Acquisitions were performed on a clinical 3T Siemens Prismafit MRI system with standard hardware components. URI is based on a gradient echo sequence similar to the 7T approach by Edlow et al. 2019. However, it allows to acquire an isotropic 160µm resolution with low hardware demands and to directly reconstruct the image data on the standard 3T MRI system. URI images display a strong, susceptibility-enhanced tissue contrast.

Results

The reconstructed URI images depicted with remarkable quality the diseased human MS brain at 3T field strength. URI allowed to distinguish fine anatomical details such as the subpial molecular layer, the stria of Gennari as well as some intrathalamic nuclei. Additionally, because of the unprecedented spatial resolution and contrast at 3T, URI permitted to easily identify the presence of subpial lesions, detailed features of intracortical lesions such the presence of incomplete/complete iron rims or patterns of iron accumulation in the entire lesion core in both cortical and white matter lesions (CLs/WMLs), lesions affecting the convoluted layers of the cerebellar cortex and nascent submillimetric CLs/WMLs.

Conclusions

URI provides a comprehensive microscopic insight into the whole-human brain at 3T through the micrometric resolution and a tissue-specific, susceptibility-enhanced contrast. We propose URI as an excellent approach to investigate microscopic brain changes of complex pathologies like MS.

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Machine Learning/Network Science Oral Presentation

PS16.04 - RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesions assessment in multiple sclerosis

Speakers
Presentation Number
PS16.04
Presentation Topic
Machine Learning/Network Science
Lecture Time
13:27 - 13:39

Abstract

Background

In multiple sclerosis (MS), perilesional chronic inflammation appears on in vivo 3T susceptibility-based magnetic resonance imaging (MRI) as non-gadolinium-enhancing paramagnetic rim lesions (PRL). A higher PRL burden has been recently associated with a more aggressive disease course. The visual detection of PRL by experts is time-consuming and can be subjective.

Objectives

To develop a multimodal convolutional neural network (CNN) capable of automatically detecting PRL on 3D-T2*w-EPI unwrapped phase and 3D-T2w-FLAIR images.

Methods

124 MS cases (87 relapsing remitting MS, 16 primary progressive MS and 21 secondary progressive MS) underwent 3T MRI (MAGNETOM Prisma and MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Two neurologists visually inspected FLAIR magnitude and EPI phase images and annotated 462 PRL. 4857 lesions detected by an automatic segmentation (La Rosa et al. 2019) without overlap with PRL were considered non-PRL. The prototype RimNet was built upon two single CNNs, each fed with 3D patches centered on candidate lesions in phase and FLAIR images, respectively. A two-step feature-map fusion, initially after the first convolutional block and then before the fully connected layers, enhances the extraction of low and high-level multimodal features. For comparison, two unimodal CNNs were trained with phase and FLAIR images. The areas under the ROC curve (AUC) were used for evaluation (DeLong et al. 1988). The operating point was set at a lesion-wise specificity of 0.95. The patient-wise assessment was conducted by using a clinically relevant threshold of four rim+ lesions per patient (Absinta et al. 2019).

Results

RimNet (AUC=0.943) outperformed the phase and FLAIR image unimodal networks (AUC=0.913 and 0.855, respectively, P’s <0.0001). At the operating point, RimNet showed higher lesion-wise sensitivity (70.6%) than the unimodal phase network (62.1%), but lower than the experts (77.7%). At the patient level, RimNet performed with sensitivity of 86.8% and specificity of 90.7%. Individual expert ratings yielded averaged sensitivity and specificity values of 76.3% and 99.4%, respectively.

Conclusions

The excellent performance of RimNet supports its further development as an assessment tool to automatically detect PRL in MS. Interestingly, the unimodal FLAIR network performed reasonably well despite the absence of a paramagnetic rim, suggesting that morphometric features such as volume or shape might be a distinguishable feature of PRL.

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Presenter Of 1 Presentation

Imaging Oral Presentation

FC03.03 - Depicting multiple sclerosis pathology at 160μm isotropic resolution by human whole-brain postmortem 3T magnetic resonance imaging

Speakers
Presentation Number
FC03.03
Presentation Topic
Imaging
Lecture Time
13:24 - 13:36

Abstract

Background

Postmortem magnetic resonance imaging (MRI) of formalin-fixed healthy and diseased human brains with ultra-high spatial resolution has the great potential to depict tissue architecture in fine detail, allowing a deeper understanding of pathological processes. Whole-brain imaging is important since it provides neuroanatomic relationships, reference points across distant brain regions, and a comprehensive view of pathologies affecting the brain. However, ultra-high-resolution whole-brain postmortem MRI is challenging and has been so far almost exclusively performed at 7T with specialized hardware.

Objectives

To develop a 3D isotropic 160µm ultra-high-resolution imaging (URI) approach for human whole-brain ex vivo acquisitions on a standard clinical 3T MRI system. To explore the sensitivity and specificity of the approach to specific pathological features of multiple sclerosis (MS).

Methods

A fixed whole human brain from a patient with secondary progressive MS was investigated. Acquisitions were performed on a clinical 3T Siemens Prismafit MRI system with standard hardware components. URI is based on a gradient echo sequence similar to the 7T approach by Edlow et al. 2019. However, it allows to acquire an isotropic 160µm resolution with low hardware demands and to directly reconstruct the image data on the standard 3T MRI system. URI images display a strong, susceptibility-enhanced tissue contrast.

Results

The reconstructed URI images depicted with remarkable quality the diseased human MS brain at 3T field strength. URI allowed to distinguish fine anatomical details such as the subpial molecular layer, the stria of Gennari as well as some intrathalamic nuclei. Additionally, because of the unprecedented spatial resolution and contrast at 3T, URI permitted to easily identify the presence of subpial lesions, detailed features of intracortical lesions such the presence of incomplete/complete iron rims or patterns of iron accumulation in the entire lesion core in both cortical and white matter lesions (CLs/WMLs), lesions affecting the convoluted layers of the cerebellar cortex and nascent submillimetric CLs/WMLs.

Conclusions

URI provides a comprehensive microscopic insight into the whole-human brain at 3T through the micrometric resolution and a tissue-specific, susceptibility-enhanced contrast. We propose URI as an excellent approach to investigate microscopic brain changes of complex pathologies like MS.

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Author Of 2 Presentations

Imaging Poster Presentation

P0538 - Applying advanced diffusion MRI in MS: a comparison of 20 diffusion MRI models to identify microstructural features of focal damage (ID 1338)

Speakers
Presentation Number
P0538
Presentation Topic
Imaging

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide unprecedented information on the microstructural properties of both healthy and pathological brain tissue.

Nevertheless, it is nowadays challenging to identify the most accurate biophysical model to describe focal microstructural pathology in multiple sclerosis (MS) patients, due to the lack of appropriate comparative studies.

Objectives

To investigate the specificity and sensitivity of 124 independent features derived from 20 diffusion microstructural models to differentiate specific features of tissue alterations in white matter (WM) lesions compared to the surrounding normal-appearing WM (NAWM).

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 years, median Expanded Disability Status Scale (EDSS): 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution isotropic and with the b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep learning network algorithm and manually corrected if required. Voxels of NAWM tissue were randomly chosen outside the lesion masks.

The following microstructural models were applied: DTI, Non-parametric DTI, DKI, Ball and Stick, Ball and Sticks, Ball and Rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI, NODDIDA, SMT, MCMDI, CHARMED, IVIM, sIVIM, Microstructure Fingerprinting, Microstructure Bayesian, DIAMOND, and DIAMOND isotropic-restricted.

The classification was performed using logistic regression on 300’000 voxels, equally divided in lesion and NAWM voxels. Features were scored according to the Area Under the Curve (AUC), sensitivity, and specificity.

Results

The intra-axonal signal fraction of the Microstructure Bayesian approach scored maximum with AUC=0.87, for threshold=0.5 sensitivity=0.79, sensitivity=0.83. AUC = 0.86 were attributed to the intra-axonal signal fraction of Ball and rockets, NODDI-Watson, AMICO-NODDI, NODDI-Bingham, SMT-NODDI and the extra-axonal perpendicular signal fraction of the Microstructure Bayesian approach. Low AUC scores (<0.75) were achieved by DTI and parameters not related to signal fractions, e.g. orientation dispersion.

Conclusions

Among available microstructural models, the Microstructure Bayesian appeared to best differentiate voxels with microstructural damage in WM lesions compared to NAWM. Very similar, albeit slightly lower accuracy, was achieved by NODDI-based models. In general, models with estimates intra-axonal signal fraction tend to perform better in this type of classification, showing that intra-axonal component may be the dominant factor in distinguishing the two types of tissue. Further analysis will explore the advantage of including combinations of independent features.

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Imaging Poster Presentation

P0647 - Studying intralesional axonal damage in MS white matter lesions with diffusion MRI biophysical models (ID 694)

Abstract

Background

Advanced diffusion-weighted MRI (DW-MRI) sequences, in combination with biophysical models, provide new information on the microstructural properties of the tissue.

Objectives

To investigate the differences in intra-axonal signal fraction (IASF) between perilesional normal-appearing white matter (pl-NAWM), white matter lesions (WML) without (rim-) and with paramagnetic rim (rim+) comparing eight biophysical diffusion models.

Methods

The study included 102 MS patients: RRMS: 66%, SPMS: 18%, PPMS: 16%, mean age 46±14; female 64%, disease duration 12.16±18.18 yrs, median EDSS: 2.5.

DW-MRI data were acquired with 1.8mm isotropic resolution and b-values [0, 700, 1000, 2000, 3000] s/mm2.

Lesion masks were generated with a deep-learning-based method and manually corrected if required; pl-NAWM was defined as a region of 3-voxels around each WML; 225 paramagnetic rim lesions were manually identified based on 3D EPI and 2330 were labelled as rim-.

The following microstructural models were applied: Ball and Stick, Ball and Rockets, AMICO-NODDI, SMT-NODDI, MCMDI, NODDIDA, CHARMED, Microstructure Bayesian approach.

Delta (WML - pl-NAWM) was calculated for each WML, and one-side Mann Whitney U was used to compare the delta between models, followed by Bonferroni to correct for multiple testing.

Mean difference and Cohen's d was used to assess differences between lesions with extensive axonal damage (rim+) and other WML (rim-).

Results

All models applied in this study reported low IASF in rim+ WML, medium IASF in rim- WML and relatively high IASF in pl-NAWM. However, a broad spectrum of IASF values was identified from the different models: relatively simple models such as Ball and Stick and CHARMED, showed low delta IASF within lesions, while MCMDI models reported the highest significant difference compared to other models (p<0.0001). The comparison between WML and pl-NAWM mean IASF across models showed that MCDMI exhibited the highest difference (mean 0.13, Cohen’s d 1.34). AMICO-NODDI and SMT-NODDI showed close results (mean difference 0.12/0.12 and Cohen’s d 1.46/1.51).

The models best discriminating IASF between rim+ and rim- lesions were MCMDI and NODIDDA (mean 0.08/0.07, Cohen’s d -0.69/-0.70).

Conclusions

We compared eight WM diffusion models for assessment of intralesional axonal damage in MS patients. The comparison between WML and pl-NAWM showed that robustness of the method, identified with SMT-based and NODDI-based models, it is crucial. For the comparison between lesions with a high level of damage (rim +) and other WML, the diffusivity estimation appeared to play an important role. The method which appeared both robust and able to estimate the diffusivity of the tissue was MCMDI, which performed best in both cases.

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