F ll A tomatic Determination of the ArterialFully-Automatic Determination of the ArterialFully Automatic Determination of the Arterial Input Function for Dynamic Contrast EnhancedInput Function for Dynamic Contrast-EnhancedInput Function for Dynamic Contrast Enhanced

Pulmonary MR ImagingPulmonary MR ImagingI tit t f M di l I C ti

Pulmonary MR ImagingInstitute for Medical Image Computingg p g

Bremen Germany Peter Kohlmann Hendrik Laue Stefan Krass Heinz-Otto PeitgenBremen, Germany Peter Kohlmann, Hendrik Laue, Stefan Krass, Heinz-Otto Peitgen

tz Introduction x tIntroductionRecent studies have shown that dynamicRecent studies have shown that dynamic

t t h d l MR i icontrast-enhanced pulmonary MR imagingp y g gis an appropriate imaging technique foris an appropriate imaging technique forclinical assessment of lung diseases. Forclinical assessment of lung diseases. For

tit ti l i f l bl dquantitative analysis of pulmonary blood yq y p yflow (PBF) an arterial input function (AIF) of

y flow (PBF), an arterial input function (AIF) of xthe contrast agent (CA) entering the lung is

x Fi 1 O h i d (l f ) l i i i li dthe contrast agent (CA) entering the lung is

i d [1] Th AIF i ll l l t dFig. 1: On the input data (left), several successive image processing steps are applied to

required [1]. The AIF is usually calculatedg O e pu da a ( e ), se e a success e age p ocess g s eps a e app ed o

automatically determine the AIF and to calculate a PBF map (middle & right)q [ ] yfrom a user drawn region of interest (ROI)

automatically determine the AIF and to calculate a PBF map (middle & right).from a user-drawn region-of-interest (ROI)

connected components analysis filters out Resultswithin the feeding artery. Thus, the results of connected components analysis filters outsmaller regions and outputs the largest

Resultswithin the feeding artery. Thus, the results ofth tit ti l i hi hl d d smaller regions and outputs the largestthe quantitative analysis highly depend on

remaining bunch of connected voxels Theq y g y p

the exact location and the size of the ROI In an ongoing study 14 perfusion data setsremaining bunch of connected voxels. Thethe exact location and the size of the ROI, In an ongoing study, 14 perfusion data setslast step is the application of 2Dand the reproducibility of the analysis is from 7 adult male patients were acquiredlast step is the application of 2Dmorphologic closing to fill holes (Fig 2 II)

and the reproducibility of the analysis isli it d Thi k t t ti

from 7 adult male patients were acquired(6 COPD 1 asthma) For each patient emorphologic closing to fill holes (Fig. 2,II).limited. This work presents an automatic (6x COPD, 1x asthma). For each patient wep

method to determine the AIF within the( ) phad data sets from two scans with about 24method to determine the AIF within the had data sets from two scans with about 24

Second refinement step (III): This step isbranching of the pulmonary trunk into left hours in between to investigate theSecond refinement step (III): This step isperformed to remove aorta and left ventricle

branching of the pulmonary trunk into leftd i ht l t (Fi 1)

hours in between to investigate therepeatabilit For all data sets the presentedperformed to remove aorta and left ventricleand right pulmonary artery (Fig. 1). repeatability. For all data sets the presented

voxels Pulmonary artery and in most casesg p y y ( g ) p y p

method correctly identified the branchingvoxels. Pulmonary artery and in most cases method correctly identified the branchingthe right ventricle voxels remain (Fig. 2,III).M t i l d M th d point within the pulmonary artery. Thethe right ventricle voxels remain (Fig. 2,III).The pulmonary artery can be separatedMaterial and Methods point within the pulmonary artery. The

res lts for t o patients are sho n in Fig 3The pulmonary artery can be separated results for two patients are shown in Fig. 3.Material from the aorta by using histogram analysis

p gMaterial from the aorta by using histogram analysisMR perfusion imaging was performed after of the time-to-peak data. In the histogram, A BMR perfusion imaging was performed afteri t i j ti f ti CA

of the time to peak data. In the histogram,the pulmonary artery/right ventricle can be

A Bintravenous injection of paramagnetic CA. the pulmonary artery/right ventricle can bej p gThe used MR sequence was a FLASH (fast separated from the aorta/left ventricle due toThe used MR sequence was a FLASH (fast separated from the aorta/left ventricle due tolow-angle shot) – T1-weighted gradient a significantly lower time-to-peak andlow angle shot) T1 weighted gradient

h t h i ith h t titi tia significantly lower time to peak andremoved by thresholding A connectedecho technique with short repetition time removed by thresholding. A connectedq p

and short echo time (voxel size: ~2x2x5 components analysis step results in aand short echo time (voxel size: ~2x2x53

components analysis step results in aC Dmm3, temporal resolution: ~1.3 sec). segmentation mask including pulmonary C Dmm , temporal resolution: 1.3 sec). segmentation mask including pulmonary

artery and right ventricle voxels This maskartery and right ventricle voxels. This maskis applied to the tMIP image and again 2DI II III IV is applied to the tMIP image and again 2DI II III IV median filtering and closing is performed. Fig. 3: Results for baseline (A,C) andmedian filtering and closing is performed. Fig. 3: Results for baseline (A,C) and

corresponding follo p e aminations (B D)corresponding follow-up examinations (B,D)Skeletonization and graph analysis (IV): A

p g p ( )of two patientsSkeletonization and graph analysis (IV): A of two patients

skeleton is extracted from the generatedskeleton is extracted from the generatedimage (Fig 2 IV) The remaining task is toimage (Fig. 2,IV). The remaining task is to Conclusions

Fig 2: Intermediate results of successively identify the branching of the pulmonaryConclusions

Fig. 2: Intermediate results of successively identify the branching of the pulmonaryapplied image-processing techniques. trunk into left and right pulmonary artery This work eliminates the influence of aapplied image processing techniques. trunk into left and right pulmonary artery

(orange sphere) This is achieved by aThis work eliminates the influence of aperson ho dra s the AIF man all on the(orange sphere). This is achieved by a person who draws the AIF manually on the

Image Processing Pipeline graph analysis algorithm which firstp youtcome of quantitative pulmonary perfusionImage Processing Pipeline graph analysis algorithm which first outcome of quantitative pulmonary perfusion

Removal of unlikely voxels (I): Most voxels searches for nodes with exactly three analysis. Hence, a better comparability ofRemoval of unlikely voxels (I): Most voxelsb il l d d F i

searches for nodes with exactly threeedges If more than one of them exists a

analysis. Hence, a better comparability oflongit dinal perf sion e aminations andcan be easily excluded. For noise edges. If more than one of them exists, a longitudinal perfusion examinations andy

suppression a 2D median filter is applied All heuristic approach analyzes the spatialg p

examinations of different patients issuppression a 2D median filter is applied. All heuristic approach analyzes the spatial examinations of different patients isvoxels are excluded which have a very low node positions, the combined lengths of the potentially enabled. This has to be investi-voxels are excluded which have a very low(b k d i ) hi h (f t

node positions, the combined lengths of theedges and the angles between the edges

potentially enabled. This has to be investigated in a f rther st d together ith clinical(background noise) or a very high (fat edges, and the angles between the edges. gated in a further study together with clinical( g ) y g (

tissue) initial signal Next a temporal Appropriate angles which characterize theg y gpartners The automation of the AIF determi-tissue) initial signal. Next, a temporal Appropriate angles which characterize the partners. The automation of the AIF determi-

maximum intensity projection (tMIP) is sought-after branching points were derived nation allows to generate the perfusionmaximum intensity projection (tMIP) isl l t d d l ith l l th

sought after branching points were derivedfrom segmentation masks of the pulmonary

nation allows to generate the perfusionparameter maps in a preprocessing stepcalculated and voxels with a value less than from segmentation masks of the pulmonary parameter maps in a preprocessing step

25% of the maximum are excluded from artery form a pool of thoracic data setsp p p p g pduring data import25% of the maximum are excluded from artery form a pool of thoracic data sets. during data import.

further processing. Also, all voxels with anfurther processing. Also, all voxels with anl l t k i th i l (ti t AIF Definition and Parameter Map Acknowledgmentearly or late peak in the signal (time-to- AIF Definition and Parameter Map Acknowledgment

Th k t d b th C ty p g (

peak) are excluded Finally all voxels with Calculation The work was supported by the Competencepeak) are excluded. Finally, all voxels with CalculationNetwork Asthma/COPD (www asconet net) funded

a peak-signal to baseline-signal ratio less The detected branching position is used toNetwork Asthma/COPD (www.asconet.net) fundedby the German Federal Ministry of Education anda peak signal to baseline signal ratio less

th 4 l d d Th i i t fThe detected branching position is used todefine the AIF Currently a circular region

by the German Federal Ministry of Education andthan 4, are excluded. The remaining part of define the AIF. Currently, a circular region Research (FKZ 01GI0881-0888). Patient data isg pan exemplary data set is shown in Fig 2 I around this position which includes 32

Research (FKZ 01GI0881 0888). Patient data iscourtesy of University Hospital Heidelberg andan exemplary data set is shown in Fig. 2,I. around this position which includes 32

3courtesy of University Hospital Heidelberg and

voxels (ca. 610 mm3) is taken into account. University Hospital Mainz.Fi t fi t t (II) Th i t

voxels (ca. 610 mm ) is taken into account.The AIF consists of the mean values of

University Hospital Mainz.First refinement step (II): The previous step The AIF consists of the mean values of

Referencesp ( ) p pnarrows down the remaining voxels to aorta these voxels in every time step The Referencesnarrows down the remaining voxels to aorta these voxels in every time step. The

[1] Risse F (2009) MR Perfusion in the Lung In: Kauczor HUand pulmonary artery voxels with the corresponding AIF curve is shown in Fig. 1

[1] Risse F. (2009) MR Perfusion in the Lung. In: Kauczor HU(ed) MRI of the Lung Springer Berlin Heidelberg pp 25 34and pulmonary artery voxels with the

t d h t h b Tcorresponding AIF curve is shown in Fig. 1(top right) Having the perfusion data set

(ed) MRI of the Lung. Springer, Berlin Heidelberg, pp 25-34connected heart chambers. To remove (top right). Having the perfusion data set [2] Ohno Y. et al. (2004) Quantitative Assessment of Regional

noise and smaller blood vessels first a 2D and the derived AIF quantitative perfusion[ ] ( ) g

Pulmonary Perfusion in the Entire Lung Using Three-noise and smaller blood vessels, first a 2D and the derived AIF, quantitative perfusion Pulmonary Perfusion in the Entire Lung Using ThreeDimensional Ultrafast Dynamic Contrast-Enhanced Mag-

median filter is applied. Successively, 2D parameter maps can be calculated with the Dimensional Ultrafast Dynamic Contrast-Enhanced Mag-netic Resonance Imaging: Preliminary Experience in 40median filter is applied. Successively, 2D

h l i i i f d N tparameter maps can be calculated with themethods described e g by Ohno et al [2]

netic Resonance Imaging: Preliminary Experience in 40S bj J M R I i 20(3) 353 365morphologic erosion is performed. Next, a methods described e.g. by Ohno et al. [2]. Subjects. J Magn Reson Imaging 20(3):353-365p g p

xxxxxxC t tContact:

EUROPEAN UNION: Dr. Peter Kohlmann Fraunhofer MEVIS Investing in your future Phone: +49-421-218 59241 Universitaetsallee 29Investing in your futureEuropean Regional Development Fund

Phone: +49-421-218 59241peter kohlmann@mevis fraunhofer de

Universitaetsallee 2928359 Bremen GermanyEuropean Regional Development Fund peter.kohlmann@mevis.fraunhofer.de 28359 Bremen, Germany