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# Automatic detection of patient-specific perfusion abnormalities in Arterial Spin Labeling

Camille Maumet

► To cite this version:

Camille Maumet. Automatic detection of patient-specific perfusion abnormalities in Arterial Spin Labeling. EU COST Arterial Spin Labeling in Dementia (AID) meeting, Oct 2012, Amsterdam, Netherlands. 2012. [hal-04091254](https://hal-inria HAL Id: hal-04091254)

https://inria.hal.science/hal-04091254

Submitted on 7 May 2023

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Distributed under a Creative Commons Attribution 4.0 International License
Automatic detection of patient-specific perfusion abnormalities in Arterial Spin Labeling
COST AID meeting

Camille Maumet

October 15th, 2012

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Inserm

Institut national
de la santé et de la recherche médicale

UNIVERSITÉ DE

RENNES 1

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CHU
Rennes

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VisAGes
1 Context

2 Method

3 Results
1 Context

2 Method

3 Results
Objective

**Quantitative identification of voxelwise patient-specific perfusion abnormalities.**

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Objective

**Quantitative identification of voxelwise patient-specific perfusion abnormalities.**

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Challenges:

* Low signal-to-noise ratio of the Q2TIPS PASL sequence.

* Presence of artefacts.
1 Context

2 Method

* Statistical analysis

* Validation

3 Results
1. Context

2. Method

* Statistical analysis

* Validation

3. Results
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Method

Originality of our approach:

* Explicit modelling of both **intra-subject** and **inter-subject** variances.

* **No pre-smoothing** of the data: a **contrary approach**.
## Intra-subject variance models variability across repetitions

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## Examples

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**Inter-subject** variance models variability across subjects

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Inter-subject standard deviation (35 healthy subjects)

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## Model of normal perfusion:

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Patient map

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Mean across subjects

Inter-subject variance

Intra-subject variance

## Detection of hypoperfusions and hyperperfusions:

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Voxel-based probabilities

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Detections

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Known as a mixed-effect general linear model.
## Mixed-effect General Linear Model:

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## Mixed-effect a *contrario* approach:

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From voxel-based to region-based probabilities:

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1. Voxel-based
probability

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2. Thresholding

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3. Rare event count per region

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4. Region-based
probability
1. Context

2. Method

* Statistical analysis

* Validation

3. Results
Step 1 – Segmentation of the tumor:

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Step 2 – Combination with T2 perfusion information:

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Step 3 – Visual assessment and manual corrections by a clinician
1 Context

2 Method

3 Results
"Ground truth"

Detections

Patient 016

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Patient 010

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"Ground truth"

Detections

Patient 020

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Patient 006

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Quantitative results on a dataset including 22 patients and 35 controls:

<table><thead><tr><th></th><th>Acontrario<br>Mixed-effects</th></tr></thead><tbody><tr><td>Specificity (controls)</td><td>0.99</td></tr><tr><td>Specificity (patients)</td><td>0.93</td></tr><tr><td>Sensitivity (hyper)</td><td>0.82</td></tr></tbody></table>
## Effect of intra-subject variance on the detections:

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## Quantitative results (22 patients, 35 controls):

<table><thead><tr><th></th><th>A contrario<br>Mixed-effects</th><th>A contrario<br>Random-effects</th></tr></thead><tbody><tr><td>Specificity (controls)</td><td>0.99</td><td>0.92</td></tr><tr><td>Specificity (patients)</td><td>0.93</td><td>0.56</td></tr><tr><td>Sensitivity (hyper)</td><td>0.82</td><td>0.86</td></tr></tbody></table>
A contrario approach and Mixed-effect General linear model detections:

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## Conclusions:

* Importance of intra-subject variance in the detection of patient-specific perfusion abnormalities.

* Good sensitivity and specificity of the proposed approach.

* Region-based a *contrario* probabilities outperform the standard voxel-based probabilities.

More details on the *a contrario* approach: C. Maumet, P. Maurel, J.-C. Ferré, and C. Barillot, "A comprehensive framework for the detection of individual brain perfusion abnormalities using arterial spin labeling" MICCAI 2012, pp. 542–549.
Q & A