Automatic Removal of Extracardiac Hotspots in

Technetium-99m Myocardial Perfusion SPECT W. H. Tan

#1, G. Coatrieux

*2, R. Besar

#3, B. Solaiman

*4

#Center for Multimedia Security and Signal Processing, Multimedia University

Persiaran Multimedia, 63100 Cyberjaya, Selangor, Malaysia1twhaw@mmu.edu.my3rosli@mmu.edu.my

*LaTIM, INSERM U650, Departement ITI, Institut Telecom Bretagne

Technopôle Brest-Iroise, CS 83818, 29238 Brest Cedex 3, France2 gouenou.coatrieux@telecom-bretagne.eu

4

basel.solaiman@telecom-bretagne.eu

Abstract— In Technitium-99m myocardial perfusion SPECT

(MPS) tomograms, there is usually a substantial radioactive

tracer uptake in the abdominal organs, especially the liver, bowel

and stomach. This extracardiac activity frequently emerges as

areas of intense brightness or hotspots, which hamper efforts in

automatic MPS quantification. Though it would be favourable to

remove the hotspots, their multitudinous appearance and

proximity to the heart have made them difficult to be removed.

In this paper, we propose an image processing technique to

automatically remove the hotspots. Our technique uses the

morphological watershed segmentation to delineate the hotspots

before they are iteratively removed. The proposed technique has

been applied on clinical MPS tomograms in which it has

completely removed the hotspots in 90% of the test data. In

addition, it has also shown to increase the success rate of an

automatic left ventricle detection scheme to 100%.

Keywords— Myocardial perfusion SPECT, extracardiac

hotspots, artifact removal, image processing, watershed

segmentation

I. INTRODUCTION

Single photon emission computed tomography (SPECT) is

an imaging technique based on nuclear medicine. It detects

gamma rays emitted singly by the radiopharmaceutical, which

is injected into the patient and localizes within one or more

organs based on its biochemical properties [1]. The gamma

rays are then detected using either a rotating camera or special

purpose multi-detector device.

Myocardial perfusion SPECT (MPS) is a form of functional

cardiac imaging, employed for the diagnosis of ischemic heart

disease [2]. MPS reveals the distribution of the

radiopharmaceutical, and therefore the relative blood flow to

the different regions of the myocardium. Diagnosis is then

made by comparing a set of stress images to a set of images

taken at rest, based on the principle that there is less blood

flow in diseased myocardium under the condition of stress.

The quantification of MPS images begins with the

generation of transaxial images through the body by using

image reconstruction techniques. The reconstructed images

will then undergo a series of image processing routines to

suppress the noise and enhance the desired features within the

images. As the transaxial images are perpendicular to the long

axis of the patient which varies from patient to patient, it is

customary to reorient them into short-axis images that are

perpendicular to the long axis of the left ventricle (LV) [3]. In

order to represent all areas of the myocardium in a single

image, the cardiac polar map is used. It is generated by

plotting the myocardial circumferential profiles as histogram

values against angular location through a polar coordinate

transformation with increasing radii [4].

Since the quantification of MPS images involves a series of

processes, automation is desired as it will drastically reduce

the labour-intensiveness of the underlying tasks. One of the

essential steps towards the automated approach is the

recognition of the LV in the MPS tomograms. Once the LV is

detected, reorientation and selection of ROI for polar map

generation can be carried out automatically.

In Technitium-99m (99m

Tc) MPS tomograms, automatic LV

recognition is hampered by the presence of the intensely

bright regions or hotspots. Due to the prominent hepatobiliary

excretion of 99m

Hotspots Myocardium

Tc-labelled perfusion agents used in MPS,

there is usually a substantial radioactive tracer uptake in the

liver [5]. Besides, there is also noticeable tracer uptake in the

stomach, bowel and occasionally in the thyroid gland. This

intense extracardiac activity frequently emerges as hotspots

that introduce streaking artefacts in the reconstructed

tomograms, as shown in Fig. 1. The hotspots often appear in

various shapes, sizes as well as intensities, and they are

usually located in close proximity to the heart. Thus, it is

difficult to exclude them from the tomograms and their

presence obscures the recognition of LV and complicates the

subsequent processes in MPS quantification.

Fig. 1. The hotspots and the myocardium in the mid-ventricular slice of a MPS tomogram.

2009 IEEE International Conference on Signal and Image Processing Applications

978-1-4244-5561-4/09/$26.00 ©2009 312

One way to mitigate the effect of these hotspots is by

having attenuation correction using simultaneous

transmission-emission tomography [6]. Nevertheless, this

approach requires dedicated hardware and thus increases the

cost of the equipment. An alternative solution is to remove the

hotspots via image processing techniques. It can be observed

that the high radiotracer uptake gives rise to intensely bright

regions in the reconstructed tomograms. Thus, the hotspots

can be removed by detecting and masking out these high

intensity regions from the three-dimensional MPS tomograms.

In this paper, we propose an image processing technique

based on the morphological watershed segmentation to

automatically detect and remove the hotspots from the MPS

tomograms. This paper is organized as follows. Section II

describes the underpinning theories and methods of the

proposed technique. Section III gives the details of the

experiments and discusses the results. Finally, conclusion is

drawn in Section IV.

II. METHODOLOGY

The solution adopted in our work consists of delineating the

hotspots by making use of three-dimensional morphological

watershed segmentation. The regions corresponding to the

hotspots are then iteratively removed depending on their

spatial location within the MPS tomograms.

A. Morphological Watershed Segmentation

Segmentation by morphological watersheds was first

proposed by Beucher and Lentuéjoul [7]. Since its debut,

morphological watershed segmentation has gained much

popularity due to its attractive attributes: natural borders and

elegant combination of edge- and region-based segmentation.

Over the years, several implementations have been proposed

[8], [9]. In this paper, we adopt and extend the approach

proposed by Tan et al. [10], which has been derived from the

rainfalling simulation [9].

In this approach, a linking scheme is defined to recursively

link each pixel in the opposite direction of the strongest edge

in its neighbourhood. By using this scheme, every pixel in the

image is gradually linked to a regional minimum. The pixels

that have been linked to the same regional minimum are in the

same catchment basin. Adjacent catchment basins are

separated by the watershed lines, which coincide with the

strongest edges between them. Thus, the catchment basins

represent dissimilar regions in the image, with the watershed

lines acting as the borders between neighbouring regions.

Consider the synthetic test image with two bright blobs,

one at the upper half and one at the lower half of the image, as

shown in Fig. 2(a). When morphological watershed

segmentation is applied, the image is divided into two regions,

the left and the right halves, corresponding to the left and right

regional minima in the image. Fig. 2(b) depicts the image’s

topographical profile, with the watershed line (hashed)

dividing the image into the left and right regions. This

example illustrates that morphological watershed

segmentation does not segment the image into the upper and

lower halves according to the position of the bright blobs.

Next, let us invert the image by using the following function:

'( , ) max ( ) ( , )P x y P P x y! " (1)

where P(x, y) is the pixel at location (x, y) of the input image,

P’(x, y) is the pixel at location (x, y) of the inverted image and

max(.) is a function which returns the maximum intensity

value of the image. Fig. 2(c) shows the inverted image of Fig.

2(a). When morphological watershed segmentation is applied

on this image, it is divided into two regions as well. The upper

region corresponds to the regional minimum at the bottom of

the inverted upper blob, while the lower region corresponds to

the regional minimum at the bottom of the inverted lower blob.

The topographical profile of the inverted image is shown in

Fig. 2(d) with the watershed line (hashed) dividing the image

into the upper and lower regions. Thus, to delineate the image

according to the location of the bright regions, the image

should be inverted before morphological watershed

segmentation is applied.

(a) Test image.

(b) Topographical profile of the test image.

(c) Inverted test image.

(d) Topographical profile of the inverted test image. Fig. 2. Test images and their topographical profile.

313

The approach proposed in [10] is only applicable to two-

dimensional images. In this work, we extend it to three-

dimensional image volumes such as MPS tomograms. The

pseudocode of the three-dimensional morphological

watershed segmentation is outlined below:

v D# $!

% &

% &% &arg min

w n vL v w

$!

! ! !

! ! !

While % & % &% &L v L L v'! ! !! !

% & % &% &L v L L v!! ! !! !

v D# $!

% & % &S v L v!

! !! !

where

% &, ,v x y z!! : running voxel;

% &, ,w x y z!! : neighbour of running voxel;

% &( ), , | 0 ,0 ,0x y zD x y z x N y N z N! * + * + * + : input domain;

% & % &(, , , , | 1 1, 1 1,n x y z i j k D x i x y j y! $ " * * , " * * ,!

)1 1z k z" * * , : neighbourhood of v!

;

% &, ,L x y z!

: linked volume;

% &, ,S x y z!

: segmented volume

B. Proposed Hotpot Removal Technique

To segment the hotspots due to extracardiac activity, the

MPS tomogram is first inverted. Gaussian filtering with a

scale of - = 1 is then applied to smoothen the volume as well

as to remove regions with homogenous intensity [11]. Next,

the aforementioned three-dimensional morphological

watershed segmentation is applied on the inverted MPS

tomograms. This demarcates the bright structures within the

tomogram, including the hotspots. To detect the region of the

most prominent hotspot, the position of its epicentre which

corresponds to brightest voxel is determined and the

watershed region in which it resides is found. Once the region

is known, the hotspot is removed from the MPS tomogram by

setting the intensity of all voxels in that region to zero. As

there are usually more than one hotspots, these processes are

repeated until all the hotspots are removed. However, doing so

may also accidentally remove the myocardium, which is also a

bright structure. To overcome this, we consider the spatial

distribution of the human organs within the MPS volume. In

this case, the transaxial slices in a MPS tomogram are

separated into three parts, as shown in Fig. 3. The slices in the

first and third tierces are categorized as the extracardiac slices,

while those in the second tierce are categorized as the

myocardial slices since the myocardium can be found in these

slices. As a result, a bright region will be removed only if its

epicentre is currently the brightest voxel and it is located in

the extracardiac slices. This process is iteratively conducted

until the brightest voxel is no longer found in the extracardiac

slices. The whole flow of our approach is illustrated in Fig. 4.

Tierce 1

Tierce 2

Tierce 3

Fig. 3. Division of transaxial MPS slices into three tierces.

Gaussian

filtering

Invert MPS

tomogram

Start

End

Morphological

watershed

segmentation

Locate highest

intensity voxel

in MPS

tomogram

Is it in the

extracardiac

slices?

Set all voxels

in its region

to 0

Read MPS

tomogram

Update

MPS

tomogram

Yes

No

Fig. 4. Flowchart of the processes in the proposed technique.

III. EXPERIMENT AND DISCUSSION

In order to evaluate its effectiveness, the proposed solution

was applied for hotspot removal on 50 sets of MPS

tomograms. These tomograms were smoothed with a two-

dimensional Butterworth filter (order=5, cutoff=0.25Nyquist),

as routinely done for MPS studies and reconstructed over 180.

(45. RAO to LPO) with ramp filter and filtered

backprojection. Our procedure (see Fig. 4) was applied on

each set independently.

From these experiments, it can be observed that the

proposed technique has successfully removed all hotspots for

45 over 50 sets of tomograms used in the experiment. Hence,

it has achieved a success rate of 90% in complete hotspot

removal. To illustrate the obtained results, a sample of the

tomogram before and after hotspot removal is shown in Fig. 5.

In all five cases for which our technique failed, the remaining

hotspots have lower intensity than the myocardium. Hence,

they were not removed. Nevertheless, their presence would

not considerably hamper the subsequent processes in MPS

quantification due to their lower intensity.

314

(a) Before hotspot removal.

(b) After hotspot removal.

Fig. 5. A sample result illustrating the effectiveness of the proposed technique.

To exemplify its effectiveness, the proposed technique was

compared to the conventional gray-level thresholding

technique. In this case, the gray-level threshold was set to the

95th percentile of the cumulative frequency distribution of the

gray-level values in the reconstructed MPS tomogram. For all

voxels with gray-level values above the threshold, their gray-

level values were set to zero. To avoid accidental removal of

the myocardium, the thresholding scheme was only applied in

the extracardiac slices, similar to the approach discussed in

Section II. Fig. 6 depicts a sample of the original

reconstructed MPS tomogram, the result after hotspot removal

via thresholding and the result after hotspot removal with the

proposed technique. With the thresholding based approach,

residues of the hotspots can still be seen in Slice 15 to 28 of

the resulting tomogram, as shown in Fig. 6(b). These residues

were not removed via thresholding as they were located in the

myocardial slices. Conversely, the hotspots were completely

removed using the proposed technique, as illustrated in Fig.

6(c). Even though some parts of the hotspots span into the

myocardial slices, they were removed by the proposed

technique as they were located in the same three-dimensional

watershed segments. Thus, the hotspots will be removed as

long as their epicentres (brightest voxels) are located in the

extracardiac slices.

(a) Original reconstructed MPS tomogram

(b) After hotspot removal via thresholding

315

(c) After hotspot removal with the proposed technique

Fig. 6. Results of hotspot removal via thresolding and the proposed technique.

To illustrate how the proposed technique may assist in

automated MPS processes, we applied an automatic LV

detection scheme, described in [12], on the same sets of MPS

tomograms. The LV detection rate before and after hotspot

removal was then compared. Prior to hotspot removal, the LV

detection scheme had successfully detected LV in 46 sets of

the tomograms. It failed in four sets of tomograms due to

hotspots’ obscuration. Once our procedure had been applied,

LV detection was successfully conducted in all 50 sets of the

tomograms. A sample of the results in which the LV was

successfully detected after the hotspot removal is shown in

Fig. 7. By improving the LV detection rate, it would facilitate

automatic LV reorientation and ROI selection for polar map

generation, which would lead to better MPS quantification.

(a) Unsuccessful LV detection before hotspot removal.

(b) Successful LV detection after hotspot removal.

Fig. 7. A sample result depicting the improvement in LV detection with the

proposed technique.

IV. CONCLUSION

Extracardiac activity is a common occurrence in 99m

ACKNOWLEDGMENT

Tc

MPS. It is induced by excessive radioactive tracer uptake in

the abdominal organs, especially the liver, bowel and stomach.

This activity frequently emerges as hotspots in the MPS

tomograms. Due to their intense brightness, the hotspots often

severely hinder automatic MPS quantification. In this paper,

we have proposed an image processing technique to

automatically remove the hotspots from MPS tomograms

based on the morphological watershed segmentation and MPS

slice categorization. The efficiency of the proposed approach

has been underlined on 50 sets of clinical MPS tomograms

with a successful rate of 90%. Furthermore, we have also

illustrated how this approach can be used to improve the

detection rate of an automatic LV detection scheme, which is

a critical step towards successful MPS quantification.

The authors would like to thank Dr. Lee Boon Nang from

Department of Nuclear Medicine, Hospital Kuala Lumpur,

Malaysia for providing the cardiac SPECT tomograms for use

in this work.

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