Third Period Review Neurological & Neuromuscular Diseases - - PowerPoint PPT Presentation

Third Period Review

Neurological & Neuromuscular Diseases

Brussels – May 29th 2015

Aim ... to make people walk better

By:

• Better understanding
• learning from others

Brussels – 12 May 2016

Gait analysis by motion tracking

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5

3D joint kinematics

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6

Interpretation…

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NND goals

• To develop a repository of gait analysis data of CP children to

enable similarity searches and other probabilistic modelling, to exploit retrospective evidence to the point of care

• To standardize clinical and gait analysis protocols in

paediatrics

• To use those to produce prospective data for modelling in CP
• To develop and validate subject specific modelling of the

musculoskeletal system in relation to gait

• To explore the potential of gait analysis and subject specific

modelling to develop sensitive measures of disease progression in DMD and CMT1

Clinical expectation: Improve walking function

• Patient-specific biophysical modeling

– For detailed insight of different aspects of the disease – Inform treatment decision – Monitor treatment effects

• Big-data infostructure

– For outome prediction – For similarity searches – Baselineinfo for disease progression

Brussels – 12 May 2016

Technical goals biophysical approach

• Construct accurate personalized musculoskeletal

models for NND children

• Driven by the needs in clinical practice
• Muscle lengths

– typical pathological adaptation in CP

• Muscle forces

– typically increased in CP – spasticity – – Decreased in DMD, CMT1

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Technical workflow

MRI images Parameter extraction for musculo-skeletal modelling Physical exam + Gait analysis

Brussels – 12 May 2016

STATISTICAL SHAPE

MODEL

DATAFLOW - EXTENDED

Semantic infrastructure

NND - WP6 Tasks

• T6.1 QA on data collection and protocols

[M1-18]

• T6.2 Gait analysis collection for CP

[M1-36]

• T6.3 Gait analysis collection for DMD & CMT1 [M12-36]
• T6.4 Image acquisition

[M 3-36]

Brussels – May 29th 2015

Data acquisition: consensus and QA

• n data collection and protocols [M1-24]
• A complete description of the protocols used in the clinical institutes (6.1-1)

 Technical Quality Assurance protocols ; Marker placement protocols & Operational protocols and workflow described and compared

• Survey by the partners (6.1-2)

 Inventory performed along 11 gait labs inside + outside EU

• A Consensus Proposal for EU CMA gait labs (6.1-2)

 Consensus protocol finalized amongst clinical partners (next slide)  Final details of protocol + list of outcome parameters for database currently written down

D6.1 : CGA clinical protocols @M18 D6.2 : Standard minimal dataset for data exchange and modeling (including TQA results) @M24

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Overview consensus protocol

1. Standardized patient history

 Clinical patient history and background

2. 3D Clinical gait analysis

 Kinematic data, kinetic data, EMG

3. Standard physical exam

 Joint range of motion, spasticity, bone deformities  Basic strength, selective motor control

4. Walking oxygen consumption data

 Energy expenditure (oxygen uptake) during walking

5. Isometric muscle strength tests using hand-held dynamometry 6. Lower body MRI

 Muscle-tendon lengths, joints rotation centers/axes, muscle volumes, muscle attachment sites and anatomical landmarks

Brussels – 12 May 2016

QA on data collection and protocols

• Reliability measures of the protocols, to assure quantitative levels of reliability

 Technical quality assurance: low level validation of labs performed  Repeatability and inter-rater reliability: currently being tested (one lab done)

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TQA : repeatability analysis

10 20 30 40 50 60 70 80 90 100

• 20

20 40 60 OPBG %stride Angle (°) OP1 OP2 10 20 30 40 50 60 70 80 90 100

• 20

20 40 60 VUA %stride Angle(°) OP1 OP2 10 20 30 40 50 60 70 80 90 100

• 20

20 40 60 KUL %stride Angle (°) OP1 OP2

• In the sagittal plane the repeatability within

laboratory was excellent

• In the frontal and transverse plane the repeatability

was lower than sagittal plane

• CMC for hip rotation was the lowest value, it could

be due to a different marker placement between

• therapists. Comparable values of CMC for second

subjects were found (0.78, 0.83)

Joint Angle Subject #1

CMCw

OPBG

CMCw

KUL

CMCw

VUA

Right Left Right Left Right Left Hip flexion/extension 0.99 0.98 0.99 0.98 0.98 0.98 Hip abduction/adduction 0.89 0.80 0.94 0.96 0.75 0.72 Hip rotation 0.85 0.88 0.80 0.84 0.20 0.21 Knee flexion/extension 0.99 0.98 0.99 0.99 0.97 0.93 Ankle dorsiflexion/plantar 0.94 0.91 0.97 0.95 0.70 0.85 Ankle abduction/adduction 0.83 0.92 0.94 0.96 na na Ankle rotation 0.77 0.93 0.94 0.94 na na Hip moment flexion/extension 0.83 0.89 0.96 0.94 0.80 0.90 Knee moment flexion/extension 0.90 0.90 0.97 0.95 0.60 0.77 Ankle moment dorsiflexion/plantar 0.92 0.96 0.99 0.99 0.99 0.94

WP6 Tasks

• T6.1 QA on data collection and clinical protocols [M 1-18]
• T6.2 Gait analysis collection for CP [M 1-36] *
• T6.3 Gait analysis collection for DMD and CMT [M12 - M44]
• T6.4 Image acquisition [M03 - M36] *

* Deliverable 6.3 (first 130 CP patients) was due March 2016

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T6.2 – T6.3 Gait analysis collection

NND VUMC – 6 May 2016 Patient Reference Complete Acquired GOAL TOTAL OVERALL 43 50 Total CP prospective extended 7 10 Total CP prospective clinical 36 40 Total TD reference data 14 20

Brussel 12 May 2016

NND KUL –6 May 2016 Patient Reference Complete Acquired GOAL TOTAL OVERALL 451 490 Total CP prospective extended 6 10 Total CP prospective clinical 27 40 Total CP retrospective 400+ 400 Total DMD T0 9 10 Total DMD T1 2 10 Total CMT T0 7 10 Total CMT T1 10 NND OPBG – 3 May 2016 Patient Reference Complete Acquired GOAL TOTAL OVERALL

222 290

Total CP prospective extended

8 10

Total CP prospective clinical

33 40

Total CP retrospective

150 200

Total DMD T0

9 10

Total DMD T1

7 10

Total CMT T0

10 10

Total CMT T1

5 10

(Healthy MRI

24 22

Issues / Corrective actions

Data acquisition is slightly behind (89%) does NOT affect the workflow of follow up partners (WP11, WP16) Actions:

• OPBG: finalize CPretro (0616), complete CPprosp, CPext, DMD and CMT
• KUL: complete CPprosp, CPext, DMD T0 and CMT T0, and T1
• VUmc : complete CPprosp, CPext (after sorting out MRI)
• Perturbation experiments

Gait Perturbations

Patient measurements Application Scenario : Similarity search

MD-Paedigree database

Conversion to standard DB format

MRI Physical exam Gait Analysis information O2 Patient information Gait Analysis trial data Anamnesis

Similar case

exploratory phase similarity search (November 2015 – February 2016)

Brussels – May 29th 2015

Gait patterns from 357 patients (children with Cerebral Palsy ) from KULeuven, involving 1731 trials

WP11 – Modelling pipe-line

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

WP11 – Modelling pipe-line

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

MRI segmentation

• Dr. Maria Costa/Siemens/2016

From MRI images… …to individual bone and muscle models Quantitative evaluation shows similar segmentation quality in both healthy and ill cases.

WP11 – Modelling pipe-line

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

Patient-Specific Complete Anatomical Model: Morphing of Template to MRI Segmentation

Patient-Specific Complete Anatomical Model: Morphing of Template to MRI Segmentation

Patient-Specific Complete Anatomical Model: Geometric Parameters Extraction

WP11 – Modelling pipe-line

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

• Use data from either FC or MRI

– Unified file format (.MPF)

• Scale segments

– Relative joint center distance – Either FC or MRI

• Proportional mass distribution

– Weight from FC

• Patient specific OpenSIM

model

OpenSIM: personalize segment length + mass

Scaled musculoskeletal models Functional joint center calibration

Brussels – May 29th 2015

Constructing a scaled model

WP11 – Modelling pipe-line

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

• Functional calibration (video)

Visualization done using MITK MITK .org

Brussels – May 29th 2015

Visualization done using MITK MITK .org

Brussels – May 29th 2015

Pre-processing: Generation of Patient-specific Model

Personalized OpenSim Model

• Create and apply Range of

Motion file

• Calculate muscle moment

arms in OpenSIM

• Derive polynomials
• Applied with motiondata in

personalized CGA

Siemens USFD Motek TU Delft TUD MRI Segmented 3D Models OpenSim Model Personalized Human Body Model Functional Calibration

MD-Paedigree/NND wp11 workflow

Complete Anatomical Model

WP11 – Modelling pipe-line

Deliverables

• D11.2 Development of novel scaling method
• D11.3 Adaption of existing musculoskeletal

model

Final year

• Use pipeline to create personalized models

– Fill database for probabilistic modelling

• Task 11.4 - Design of models driven by the

dynamics of gait perturbations

Gait perturbations

Gait perturbations

Analyzing human motion

Muscle Excitation Muscle Excitation Muscle Activation Muscle Activation Muscle Force Muscle Force Joint Moments Joint Moments External Reaction Forces External Reaction Forces Motion Motion

Forward simulation Forward simulation Inverse simulation Inverse simulation

Limited model validation potential Strong model validation potential

Example: OpenSim optimization

Example: weak GAS + SOL

Example: short HAM

Model validation (WP12)

• Use personalized models from WP11 as input

– Geometry – Muscle strength & Muscle attachments – Spasticity and involuntary synergy model

• Simulate treatment and predict outcome

– Surgery (tendon transfer, femoral derotation, …) – Physiotherapy / strength training – Botulinum toxin injection

• Compare predictive simulations to gait data from WP6

– Kinematic & kinetic validation (joint angles & moments) – EMG

• Shift of PM’s

Product development

Product development: augmented reality