Computer Aided Detection and Measurement of Peripheral Arterial - - PDF document

Computer Aided Detection and Measurement of Peripheral Arterial Diseases from CTA Images

Professor Jamshid Dehmeshki Kingston University, London The University Hospital of Lausanne

Outline

➢ Anatomical overview of Peripheral Arteries Diseases (PAD) ➢ Investigation of PAD in Computed Tomography Angiography (CTA) ➢ Methodology – Automatic Computer Aided Detection (CAD) and Automatic Computer Aided Measurement (CAM) of PAD ➢ Evaluation – computational implementation and evaluation ➢ Conclusıon

• Obstruction of arteries in lower

extremities

• Afflicts more than 2.7 million

people in the U.K and 10 million of Americans per year

What is Peripheral Arterial Disease (PAD)?

Anatomical overview of PAD

Anatomical overview of PAD

➢ PAD has a mortality rate higher than breast cancer . ➢ PAD is a marker of coronary artery disease (CVD) , ischemic heart disease (IHD) and cerebrovascular disease ➢ The disease is undertreated and under detected

Research motivation

Anatomical overview of PAD

Treatment

• Minimizing the risk factors of

atherosclerosis

• Pharmacological therapy
• Antiplatelet, statins

Anatomical overview of PAD

Treatment

• Minimizing the risk factors of

atherosclerosis

• Pharmacological therapy
• Antiplatelet, statins
• Endovascular
• Angioplasty
• Stent placement
• Atherectomy

Anatomical overview of PAD

Treatment

• Minimizing the risk factors
• f atherosclerosis
• Pharmacological therapy
• Antiplatelet, statins
• Endovascular
• Angioplasty
• Stent placement
• Atherectomy
• Surgical
• Endarterectomy
• Peripheral bypass grafting
• Amputation

Anatomical overview of PAD

Imaging techniques

• Duplex ultrasound scanning
• Magnetic resonance angiography (MRA)
• Transcatheter angiography (TCA)
• Computed Tomography Angiography(CTA)

Anatomical overview of PAD

Diagnosis

➢ CTA a common imaging technique for investigation of the disease Current problems

Statement of the problem

➢ Large amount of data

• Confined to two-dimensional

(2D) views ➢ Variety of arteries size and shape, abnormalities ➢ Image quality: parameters of CT scanning, contrast agent, PVA always present Radiologist ➢ High inter-observer and intra-observer variability ➢ Error, fatigue vs. performance ➢ Cost

CTA for PAD

➢A system that can fully detect and measure peripheral arterial diseases in CTA datasets automatically ➢A tool for radiologist to reduce investigation time, human error improving clinical assessment

Scope and aim of the project

CAD - CAM

Proposed Methodology

Methodology

➢ I - the input Dicom image ➢ l f0 - initial foreground value ➢ lb0 - background value ➢ Optimum threshold is calculated iteratively

Methodology

➢ Automatic seed selection ➢ Region growing criteria:

• Connectivity among voxels
• Intensity homogeneity

➢ Over segmentation from previous step (region growing): artery and bone are connected

• Variations in bone density
• Arteries intensity similar with bone
• r soft tissue

Methodology

➢ Erosion is not sufficient to delineate areas of continuity between bone and artery ➢ Use a prior information and spatial information ➢ Anatomical prior: properties of artery and bones

• bones are larger structures
• bones show inhomogeneous

intensity values

• arteries have more

homogeneous intensity(exception: calcifications)

➢ Erosion is not sufficient to delineate areas

• f continuity between bone and artery

➢ Use a prior information and spatial information ➢ Anatomical prior: properties of artery and bones

• bones are larger structures
• bones show inhomogeneous

intensity values

• arteries have more

homogeneous intensity(exception: calcifications)

Methodology

False Positive Reduction

➢ We have extracted the peripheral arteries and we want to investigate the presence of stenosis ➢ Requires a centreline that preserves geometry and topology

Methodology

➢ Centreline contains spurious branches and redundant voxels

Methodology

➢Removal of these unwanted voxels is done using:

• Pruning: A 3D directional connectivity

search to remove branches

• Refinement: remove redundant voxels

➢Create continuous smooth line

• Interpolation using Catmull Rom

splines ➢

➢ The obtained centreline is a discrete representation of voxels

Methodology

➢ Create continuous smooth line

• Interpolation using Catmull

Rom splines ➢ Artery measurements are performed in cross section images ➢ Cross section images are orthogonal planes to the arteries ➢Cross section images are obtained using a sliced- based rendering technique

➢ We have created the cross section images where the measurement can be performed ➢ Area ➢ Equivalent Diameter

Methodology

➢ Maxima (pmax)and Minima (pmin) are chosen ➢ A stenotic area is automatically identified if

Methodology

➢ The vessel profile is a 1D representation of

the measurements ➢ Looking for significant extrema points

➢ A MAP-MRF expectation maximization method is used ➢Partial volume effect is a mixture of tissues in one pixel ➢ In the current context is mixture between the peripheral artery and surrounding tissue

Methodology

Methodology

➢ Finding the contribution of each tissue in one pixel would provide an accurate measurement of area/diameter

➢ By using an a priori penalty as a spatial constraint within a

Markov Random field framework we can model the tissue mixture ➢ The EM algorithm is used to estimate the tissue mixture

➢ A new measurement is performed

taking into account the partial volume effect, reflected by the determined percentage contribution of each tissue , λ, in one pixel

Methodology

➢ Stenosis is measured based on a reference measurement in a healthy artery distal or proximal to a stenotic site and the measurement in the most severe site of stenosis

➢ Degree of stenosis is determined according to the area percentage, by a scale 5 point :

• 0 healthy artery,
• 1 (1-49%)
• 2 (50-69%)
• 3 (70-99%) and
• 4 occlusion

➢ Length of stenosis shows severity of the disease and is expressed through two categories:

• short (< 1cm), 1-3 cm, 3-5 cm, 5-10 cm
• long (>10)

Methodology

Real patient data

➢ Twenty CTA datasets provided by CHUV, Lausanne but 18 were used ➢ GE Multi-slice helical CT ➢ Different sizes from 512 x 512 x 900 to 512 x 512 x 1050 ➢ Resolution: 0.703125 x 0.703125 x 1.25

Phantom data

➢ Mimics the shape and attenuation properties of the artery ➢ Diameter ranges from 1 to 8 mm ➢ Stenosis is simulated ➢ Different acquisition protocols (10)

Evaluation

Threshold

➢ Applied on 18 datasets ➢ Optimal threshold: 140-200 HU Results ➢ Visual evaluation showed that all arterial segments were present

Evaluation

Region Growing

➢ Applied on 18 datasets ➢ 6 neighbourhood connectivity system Results ➢ The arteries were extracted correctly in 2 datasets ➢ In 16 datasets the extracted arteries were connected to the bone

Evaluation

Evaluation

Vessel profile analysis

➢ Applied on 15 segmented data, each partitioned in 35 segments (total

• f 525 segments)

➢ Ground truth: 149 segments with stenosis: 132 soft plaque, 17 calcifications Parameters ➢ The width of the 1D Gaussian filter was set to 9 ➢ The parameter k in the discrete curve analysis was fixed to 20 ➢ The parameter θs was set to 0.55

Evaluation

Results ➢ The method identified 116 stenosis caused by soft plaque ➢ 33 stenotic areas were missed (17 calcified) ➢ Identified 15 false positives stenoses

Evaluation

Vessel profile analysis

➢ The peripheral arteries were extracted with a sensitivity of 83.3% ➢ The undetected stenosis in the calcified arterial segments influences the sensitivity

Evaluation

A MAP-MRF method for partial volume effect correction

➢ Evaluated on phantom data and compared to threshold method Parameters ➢ A MAP-MRF method

• Number of tissues, K=2
• Degree of penalty parameter, β was set to 0.85
• Mean and variance for artery class: µ1= 200, ν1=100
• Mean and variance for surrounding tissue: µ2= 60, ν1=10
• EM convergence parameter δ was set to 0.05

➢ Threshold method

• Threshold value was selected 200 HU

Evaluation

A MAP-MRF method for partial volume effect correction

➢ Evaluated on phantom data and compared to threshold method Results ➢ The measurement of the simulated artery in the phantom, using MAP- MRF method showed a percentage error of 8%.

Evaluation

➢ A fully automatic CAD-CAM system for detection and measurement of

peripheral arterial diseases in CTA images has been developed and implemented ➢ Results for stenosis detection showed a sensitivity of 78% and specificity of 96% (15 false positive in all datasets), with an increase in sensitivity (to 88%) if calcified areas were excluded

Conclusıon

➢ The partial volume effect was corrected and CAM component showed an average errot of 8% when evaluated on phantom data ➢ The computation time for processing 1000 slices is 8 min, while radiologist examination is 1- 4 hours

➢ Computational improvement

• Reducing computation time
• Reducing rigidity of the algorithmic pipeline

➢ Identification and quantification of stenoses in calcified areas ➢ Functionality for CAD system to deal with other pathologies (occlusions and aneurysm ) ➢ Additional anatomical a priori information

Future work

Thank you!