A Comparative Evaluation of Cortical Thickness Measurement Techniques

P.A. Bromiley, M.L.J. Scott, and N.A. ThackerImaging Science and Biomedical Engineering

University of Manchester

Introduction

• The cerebral cortex:– largest part of the brain– highly convoluted 2D sheet of neuronal tissue– laminar structure– min. thickness ~2mm (calcarine sulcus) – max. thickness ~4mm (precentral gyrus)– av. thickness ~3mm

Introduction

• Volume measurements are well established– e.g. dementia, ageing

• Thickness provides additional information– correlations with Alzheimer’s, Williams syndrome,

schizophrenia, fetal alcohol syndrome…

Introduction

• Free from region definition

v

t

Introduction

• More robust to misregistration

– volume error misregistration

v1 v2

Introduction

• More robust to misregistration

– median thickness error t / n

Introduction

• Two approaches:– model based (e.g. ASP, McDonald et al. 2000)

• fit deformable model to inner surface• expand to reach outer surface• measure distance between corresponding vertices

– data-driven• use edge detection to find inner surface• find 3D normal• search along normal for another edge

The problem…

• Partial volume effect may obscure outer surface

(from McDonald et al. 2000)

Model Bias

• Impose constraints the force spherical topology and force the models into thin sluci:

– distance between vertices on inner and outer surfaces

– surface self proximity– may introduce bias– takes ages to run

The TINA Cortical Thickness Algorithm

• Scott et al., MIUA 2005– find inner surface– search along 3D normal– process edges, dips found

AIM

• Can data driven techniques be as accurate as model-based ones?

• Can we find evidence of model bias?

Evaluation

• 119 normal subjects, 52 male, age 19-86 (μ=70.3)– T1-weighted IR scans: suppresses inhomogeneity

Evaluation

• Meta-studies:– youngest 13 compared to Kabani et al. manual and

automatic (model based)– precentral gyrus thickness vs. age compared to 8

previous publications for all 119 subjects…if we can see aging, we can see disease

Comparison to Kabani et al.

Comparison to Kabani et al.

Comparison to Kabani et al.

• From error propagation, expected error on an individual ~0.1mm

• Mean differences– present study: –0.21 +/- 0.22 mm– Kabani et al.: 0.61 +/- 0.43 mm– => mostly group variability

• No evidence of systematic error• Data-driven technique has ~2x lower random

errors

Precentral Gyrus Study

• Meta-study incorporating 635 subjects:

Reference No. Age range (years) Algorithm typeKabani et al. (2001) 40 18-40 Model basedVon Economo (1929) - 30-40 Manual measurementSowell et al. (2004) 45 5-11 Intensity basedTosun et al. (2004) 105 59-84 Model basedFischl et al. (2005) 30 20-37 Model basedThompson et al. (2005) 40 18-48 Intensity basedMacDonald et al. (2000) 150 18-40 Model basedSalat et al. (2004) 106 18-93 Model basedPresent study 119 19-86 Intensity based

Precentral Gyrus Study

• Colourmap representations– error estimation is not possible– bias from inflated/non-inflated representations

(from Fischl et. al., 2000)

Precentral Gryus Study

Conclusions

• Results from all other studies are consistent– random errors dominated by natural variation

• Data-driven cortical thickness measurement– free from model bias– order of magnitude faster – at least as accurate

…compared to model-based techniques• Bias may have been seen in the Salat et al. results?

– don’t use prior measurement to make measurement