# Automatic detection of perfusion abnormalities based on an Arterial Spin Labeling template

Camille Maumet, Pierre Maurel, Jean-Christophe Ferré, Christian Barillot

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To cite this version:

Camille Maumet, Pierre Maurel, Jean-Christophe Ferré, Christian Barillot. Automatic detection of perfusion abnormalities based on an Arterial Spin Labeling template. ISMRM Scientific Workshop: Perfusion MRI 2012, Oct 2012, Amsterdam, Netherlands. , 2012. [hal-04091251](https://hal-inria HAL Id: hal-04091251)

https://inria.hal.science/hal-04091251

Submitted on 7 May 2023

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Distributed under a Creative Commons Attribution 4.0 International License
Automatic detection of perfusion abnormalities
based on an Arterial Spin Labeling template

ISMRM Scientific Workshop: Perfusion MRI 2012

Camille Maumet, Pierre Maurel, Jean-Christophe Ferré,
Christian Barillot

October 13th, 2012

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Inserm

Institut national
de la santé et de la recherche médicale

UNIVERSITÉ DE
RENNES

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CHU
rennes

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

* Ground Truth estimation

3 Results
1 Context

2 Method

* Statistical analysis

* Ground Truth estimation

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 **contrario approach**.
Intra-subject variance models variability across repetitions

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Repetition 1

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Repetition 2

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Repetition 3

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Repetition 4

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Repetition r

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Mean

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Standard
deviation

## 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

$$ \sim \mathcal{N}(\mu, \sigma^2) + \text{Intra-subject variance} $$

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Mean across subjects

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Inter-subject variance

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Intra-subject variance

## Detection of hypoperfusions and hyperperfusions:

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Voxel-based probabilities

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A contrario region-based probabilities

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Detections

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1. Context

2. Method

* Statistical analysis

* Ground Truth estimation

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><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></table>
## Conclusions:

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

* Good sensitivity and specificity of the proposed approach.

## Future work:

* Relationship between the level of CBF in identified abnormalities and the grade of the tumor.

* Application in other pathological contexts.

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