Movement of the patient during myocardial perfusion SPECT leads to some artifacts that make the interpretation difficult. In this study, myocardial perfusion imaging protocol was performed on a cardiac phantom and SPECT was performed by simulating patient movements. A lesion model with dimensions of 1.2 × 2 × 2 cm was created on the inferoseptal wall of the cardiac phantom. Imaging was done in circular orbits in 64 × 64 matrix and step and shoot mode. First set of images taken with no movement was referred as the reference image. During imaging, patient movement was simulated by moving the phantom in ±X and ±Y directions between the frames starting from 8th frame to 16th frame. At the end of imaging, Bull’s eye maps of images with movement were com-pared with Bull’s eye maps of reference images. Bull’s eye maps were evaluated by an experienced nuclear medicine physician. Shifting patient’s movement in all directions by ±1 and ±2 cm, dis-placed the localization of the lesion mildly and this did not hamper the evaluation. However, movements of ±3 or ±4 cm resulted in artifacts which in turn caused partial or no visualization of the lesion. In motion corrected images, the lesion could be evaluated in ±1 and ±2 cm movements while lesion could not be evaluated in ±3 and ±4 cm movements. As a result, movement greater than ±3 cm causes significant image artifacts and this should be considered as a potential source of error in myocardial perfusion studies.
SPECT systems are based on rotation of one or more cameras around the patients.<0} {0>Detektörün hasta etrafında dönmesiyle belirli açılarda bilgi toplanır, daha sonra bilgisayarda iterative veya backprojection filtreleme teknikleri kullanılarak kesit görüntüleri elde edilir (2, 6, 11).<}0{>Information is gathered in certain angles of rotation of the detector around the patient, then section images are obtained on the computer by using iterative or back projection filtration techniques [1]-[5] .<0} {0>SPECT sistemlerinde projeksiyon dizisindeki görüntüler back projeksiyon denilen algoritma ile yeniden yapılandırılır.<}0{>Images in projection series in SPECT systems are re-structured by an algorithm called back projection.<0} {0>Projeksiyon verileri aksiyal veya transaksiyal olarak ta adlandırılan transvers kesitlerde üretilerek kombine edilebildiği gibi sagital ve koronal kesitlerindeki görüntüler de matematiksel işlemler ile elde edilebilir (12,13,14).<}0{>Projection data can be produced in transverse sections also named as axial or transaxial and images in sagittal or coronal sections can be obtained by mathematical procedures as well [6] -[8] .<0} {0>Myocard perfüzyon SPECT uzun sürmesi yüzünden görüntüleme sırasında hasta daha rahat edebilmek için pozisyonunu değiştirip farklı yönlerde hareket etmektedir.<}0{>Due to long acquisition times in myocardial perfusion SPECT, patients tend to move in different directions by changing position in order to feel comfortable [9] . <0}{0>Miyokard perfüzyon SPECT görüntüleme sırasında hasta hareketi görüntüde artefaktlar meydana getirmektedir.<}0{>However, patient movement during myocardial perfusion SPECT imaging results in image artifacts [1] [2] .<0}{0>Bu çalışmada hasta hareketinin görüntüye etkisi incelenmiştir.<}0{>
{0>SPECT sistemleri bir veya daha fazla kameranın hastanın etrafında dönmesi esasına dayanır.<}0{>{0>SPECT esnasındaki genel hasta hareketleri sabit vücut hareketi, periyodik lokal deformasyonlar ve lokal olmayan deformasyonlardır.<}0{>General patient movements during SPECT include constant body motion, periodic local deformations and non-local deformations. <0}{0>Nefes alma ve kalp çarpıntısı periyodik lokal deformasyonlardır.<}0{>Breathing and heart beat are periodic local deformations. <0}{0>Sabit vücut hareketleri dediğimizde hastayı sabit bir cisim gibi varsayarak düşünmemiz gerekir.<}0{>When the term “constant body motion” is used, the patient should be considered as a fixed substance.<0} {0>Mümkün olabilecek hareket üç boyutta dönme ve çevirme hareketleridir.<}0{>Possible motions include turning and rotating in three dimensions [10] .
{0>Deformasyona neden olan hasta hareketlerinden sabit vücut hareketlerinin etkisinin düzeltilmesi en kolaydır.<}0{>Of all patient movements that cause deformation, it is easier to correct the effects of constant body motions<0} {0>Çünkü çekim süresince hareket etkisi olmaksızın elde edilen görüntülerle kıyaslanarak veri işlenmektedir.<}0{>because data are processed by comparing the images obtained without effect of motion during imaging.
Some techniques are developed for the quantitative analysis of planar or SPECT myocardial perfusion images.<0}<0} {0>Kantitatif programlar görsel değerlendirmelere göre daha etkindir.<}0{>Quantitative programs may be more efficient compared to visual evaluations. {0>Stres perfüzyon kantitasyonu, iskemi yaygınlığını ve şiddetini değerlendirmek için istirahat perfüzyon ile karşılaştırılır.<}0{>Stress perfusion quantization is compared with resting perfusion in order to evaluate spread and intensity of the ischemia.<0} One approach is to generate a polar map by taking sections from apex of the heart toward base in short axis SPECT. {0>Bu nicel analiz genelde bull’s eye veya polar haritası olarak görüntülenir.<}0{>This quantitative analysis is generally visualized as Bull’s eye or polar map<0} {0>Planar veya SPECT miyokard perfüzyon görüntülerinin kantitatif analizi için bir takım teknikler geliştirilmiştir.<}0{>{0>Yaklaşımlardan biri hastanın sintigrafisinin dairesel bir profil histogramı oluşturularak referans standartla karşılaştırmaktır.<}0{>{0>Başka bir yaklaşım da short axis SPECT tomografilerinden kalbin apex inden bazaline doğru kesitler alınarak bir polar harita oluşturmaktır.<}0{>OneOne <0}{0>Bölge skorlarının miyokard duvarı üzerindeki en büyük voksel değerine normalize edilmesi ve en yüksek voksel değerinin QPS’ de 100’e ayarlanmış olmasından dolayı uniform görüntülerin çoğu ortalama bir bölge skoruna ve küçük standart sapmaya sahip olmaktadır (16,17,18).<}0{>Due to normalizing regional scores to largest voxel value on myocardial wall and largest voxel value adjusted to 100 at Quantitative Perfusion SPECT (QPS), most of the uniform images have an average regional score and a small standard deviation [11] - [13] . <0}
{0><}0{>Some techniques have been developed for the detection and correction of translational patient motion in dynamic and static myocardial SPECT studies [14] - [18] .
The effect of patient movement on lesion detection was examined in this study.<0} {0>Ayrıca, alınan görüntülerin görüntü işleme basamaklarında hareket düzeltme seçeneği de kullanılarak hangi mesafelerdeki hareket etkisini kompanse edebildiği ve derecelendirmede oluşturduğu farklılıklar yorumlanmıştır.<}0{>In additionIIn addition, by using the motion correction option in image processing, ability of compensation of movement effect in various directions and magnitudes was assessed. <}0{>Myocardial perfusion imaging protocol was performed on a cardiac phantom with lesion and SPECT was performed by simulating patient movements.<0} <0}
2. Materials and Methods<0}2.1. Cardiac Insert Phantom
Cardiac insert phantom used in this study had a 8 cm diameter and height and a 0.5 cm wall thickness.<0} Bolus<}100{> (A tissue-equivalent substance) with dimensions of 1.2 × 2.0 × 2.0 cm that mimics a perfusion defect in the phantom image was used. <0}{0><}100{>Cardiac insert phantom was placed in the same coordinate axis in all imaging procedures.<0} {0><}100{>Preparation of cardiac insert phantom took place in laboratory to prevent risk of contamination. <0}The p{0><}100{>hantom was filled with water, and water was mixed until no bubbles existed.<0} In nuclear medicine, clinical practice, 15 - 30 mCi 99mTc MIBI is injected to the patient for myocardial perfusion scintigraphy and the myocardial uptake of 99mTc-sestamibi is 1.2% - 1.5% of the injected dose [19] . {0><}0{>Thus 1 mCi 99mTc was placed in the phantom corresponding to the extracted amount {0><}100{>99 mTc with 1 mCi activity. Experimental setup is shown in Figure <0}1.<0} {0>
{0><}100{>Assuming that patient movement occurs just in the middle of the imaging, the phantom was moved starting from the middle of imaging that is right after the 8th frame with a total of 16 frames, i.e. <0}{0><}100{>During imaging, the phantom was moved from the origin of ±X and ±Y axes in one cm steps up to 4 cm starting between frame 8 and 16 and the effect of this motion at each cm was evaluated quantitatively and visually in comparison to reference the image<0}{0.<}100{>
2.2. Imaging Protocol
Double detector-Philips Forte JETstream AZ SPECT is used with a VXGP collimator. 64 Images were obtained in on 64 × 64 matrix with no zooming. <0}Minimum of {0><}0{>100,000 counts were collected during each acquisition. <0}{0>Imaging was performed by double detectors starting on 45 degrees right anterior oblique and ending at 135 degrees left posterior oblique in a total of 180 degrees in a circular orbit by step and shoot mode. {0><}0{>Energy window was set at 20% for 99 mTc at 140 keV. <0}{0><}100{>Butterworth filter was used for processing.<0}
Experimental setup
2.3. Evaluation of Images
{0><}100{>Images were reconstructed with Philips JETstream Workspace V3.5 version.<0} {0>For interpretation each image, with a motion effect is recorded in data registration form after comparison with reference image according to grading table (Table 1).<0} Site of lesion was determined on image with no motion effect, which we considered as the reference.<0} We created a {0><}100{>reference image with 20 segments in the Bull’s eye map<0} (Figure 2). Blinded image evaluation was performed by independent two Nuclear Medicine Specialist. {0><}100{>Area with maximum activity was considered as 100 and mapping was done using the ratio of the lesion-site to maximum. The study was performed using gamma camera Philips Forte Jet stream AZ SPECT which is applied Cedars-Sinai Motion Correction (MoCo) algorithm.
Mild is described as, low activity up to 3 regions.
<0}Severe is described as, low activity in more than 3 regions.
{<}100{>In order to describe the location of the lesion and its neighborhood in Bull’s eye image, schematic visualization was shown (Figure 3).
3. Results
{0>Çalışmamızda kardiyak insert fantom kullanılarak hareket etkisi olmayan bir görüntü alınmıştır ve bu görüntü referans görüntü olarak adlandırılmıştır (şekil ).<}100{>Te{0>Belirlediğimiz koordinatlarda ve mesafelerde, frame 8’den 16’ya kadar olan görüntüleme süresince hareket ettirilen fantomun meydana getirdiği etkinin yorumlanmasında referans görüntü ile görsel karşılaştırma, uzman bir Nükleer Tıp hekimi tarafından yapılmıştır.<}100{>{0>Referans görüntüde lezyon saat 7 yönünde olup 8, 9 kesit sürmektedir.<}100{>Lesion is localized at 7 o’clock position.<0} The reference image is shown in Figure 2.
In early frame for example frame 9 no lesion was visible at the true lesion site but additional lesion was seen Bull’s eye map without motion correction (Figure 4). True lesion exists with additional lesions with motion correction effect (Figure 5). Graphic 1 shows uptake in the lesion according to the reference without motion correction for frame 9, Graphic 2 shows uptake in the lesion according to the reference with motion correction for frame 9,
For late frame for example frame 15 no lesion was visible at the true lesion site but additional lesion was seen Bull’s eye map without motion correction (Figure 6). True lesion partially exists with additional lesions with motion correction (Figure 7). Graphic 3 shows uptake in the lesion according to the reference without motion correction for frame 15, Graphic 4 shows uptake in the lesion according to the reference with motion correction for frame 15.
Tables show grading with motion correction (Table 2) and without motion correction (Table 3).
In movement in −X1 and +X1, the lesion was on the real localization site but additional lesions was seen in early and late frames.
In movement −X2, the lesion was partially on the real localization site but additional lesions were seen in neighbor regions.
In movement +X2, the lesion was not seen on the real localization site but additional lesions were seen in neighbor regions.
Bull’s eye map of the reference image (no motion effect)
{0>Hafif şiddette ek lezyon var<}100{>Mild additional lesion<0}
{0>1B<}100{>1B<0}
{0>Ağır şiddette ek lezyon var<}100{>Severe additional lesion<0}
{0>1C<}100{>1C<0}
2. {0>Lezyon yok<}100{>No lesion is visible at the true lesion site with<0}
{0>Ek lezyon yok<}100{>No additional lesion<0}
{0>2A<}100{>2A<0}
{0>Hafif şiddette ek lezyon var<}100{>Mild additional lesion<0}
{0>2B<}100{>2B<0}
{0>Ağır şiddette ek lezyon var<}100{>Severe additional lesion<0}
{0>2C<}100{>2C<0}
3. {0>Lezyon kısmen var<}100{>True lesion partially exists with<0}
{0>Ek lezyon yok<}100{>No additional lesion<0}
{0>3A<}100{>3A<0}
{0>Hafif şiddette ek lezyon var<}100{>Mild additional lesion<0}
{0>3B<}100{>3B<0}
{0>Ağır şiddette ek lezyon var<}100{>Severe additional lesion<0}
{0>3C<}100{>3C<0}
Grading with motion correction
{0>FRAME 8<}100{>FRAME 8<0}
{0>FRAME 9<}100{>FRAME 9<0}
{0>FRAME 10<}100{>FRAME 10<0}
{0>FRAME 11<}100{>FRAME 11<0}
{0>FRAME 12<}100{>FRAME 12<0}
{0>FRAME 13<}100{>FRAME 13<0}
{0>FRAME 14<}100{>FRAME 14<0}
{0>FRAME 15<}100{>FRAME 15<0}
{0>FRAME16<}100{>FRAME16<0}
{0>+Y1<}100{>+Y1<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>+Y2<}100{>+Y2<0}
{0>2C<}100{>2C<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>2A<}100{>2A<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>+Y3<}100{>+Y3<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>3C<}100{>3C<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>2A<}100{>2A<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>+Y4<}100{>+Y4<0}
2B
{0>3C<}100{>3C<0}
{0>1A<}100{>1A<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
−Y1<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>3C<}100{>3C<0}
−Y2<0}
{0>1B<}100{>1B<0}
{0>1A<}100{>1A<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
−Y3<0}
{0>1B<}100{>1B<0}
{0>1A<}100{>1A<0}
{0>1A<}100{>1A<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>3C<}100{>3C<0}
{0>1A<}100{>1A<0}
{0>1B<}100{>1B<0}
−Y4<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>1C<}100{>1C<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>+X1<}100{>+X1<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>2A<}100{>2A<0}
{0>2A<}100{>2A<0}
{0>1B<}100{>1B<0}
{0>+X2<}100{>+X2<0}
{0>2B<}100{>2B<0}
{0>2C<}100{>2C<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>+X3<}100{>+X3<0}
{0>2C<}100{>2C<0}
{0>2C<}100{>2C<0}
{0>2C<}100{>2C<0}
{0>1C<}100{>1C<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>+X4<}100{>+X4<0}
{0>3C<}100{>3C<0}
{0>1C<}100{>1C<0}
{0>1C<}100{>1C<0}
{0>3B<}100{>3B<0}
{0>3C<}100{>3C<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
−X1<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
−X2<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>1B<}100{>1B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>-X3<}100{>−X3<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>3B<}100{>3B<0}
{0>2B<}100{>2B<0}
{0>1B<}100{>1B<0}
{0>2B<}100{>2B<0}
{0>1A<}100{>1A<0}
−X4<0}
{0>2B<}100{>2B<0}
{0>2C<}100{>2C<0}
{0>3B<}100{>3B<0}
{0>1C<}100{>1C<0}
{0>3C<}100{>3C<0}
{0>3B<}100{>3B<0}
{0>3A<}100{>3A<0}
{0>2B<}100{>2B<0}
{0>2B<}100{>2B<0}
Grading without motion correction
FRAME 8
FRAME 9
FRAME 10
FRAME 11
FRAME 12
FRAME 13
FRAME 14
FRAME 15
FRAME16
+Y1
3B
3B
3B
3B
2B
2B
3B
3C
2B
+Y2
2B
2B
3B
2B
3B
2C
3B
3C
3B
+Y3
3C
1C
3C
2B
2B
2C
2B
2C
2C
+Y4
2C
1C
3C
2B
3B
2C
2C
1B
3B
−Y1
3B
1B
3B
1B
3B
1B
2B
3B
3B
−Y2
3B
1B
2B
2B
2B
2B
2B
3B
1B
−Y3
3C
3C
1C
2C
3C
2C
2B
3C
2B
−Y4
3C
1C
1C
3C
3C
2B
2B
1B
3C
+X1
2B
2B
2B
2B
2B
3B
2B
2B
1B
+X2
2C
3C
2C
2B
3B
2B
2B
2B
1B
+X3
3C
2C
1C
3C
1C
3B
2B
2B
3B
+X4
3C
2C
1B
3B
3B
3B
2B
2B
3B
−X1
3B
3B
3B
3B
3B
1B
3B
2B
2B
−X2
2B
2B
2B
2B
2B
3B
3B
1B
3B
−X3
2B
2B
1C
1C
1C
3B
3B
2B
1B
−X4
2B
1C
1C
1C
1C
3B
3C
3B
3B
In movement −X3 the lesion was seen partially on the real localization site but additional lesions were seen in neighbor regions in the early frames. In the late frames, lesion was seen on the real localization site but additional lesions were seen in neighbor regions.
In movement +X3, the lesion was not seen on the real localization site but additional lesions were seen in neighbors regions in early frame, in the late frames, lesion was seen on the real site but additional lesions were seen in neighbor regions.
In movement −X4 and +X4, the lesion was partially on the real localization site or not but additional lesions were seen in neighbor regions.
In movement −Y1 and +Y1, the lesion was partially on the real localization site but additional lesions were seen in neighbor regions.
In movement −Y2, the lesion was on the real localization site but additional lesions were seen in neighbor regions.
In movement +Y2, the lesion was not on the real localization site in early frames, but additional lesions were seen in neighbor regions in late frames.
In movement −Y3 the lesion was partially on the real localization site but additional lesions were seen in neighbors regions.
In movement +Y3, the lesion was not on the real localization site but additional lesions were seen in early frames but the lesion was on the real localization site with additional lesions in late frames.
In movement +Y4 the lesion was not seen on the real localization site but additional lesions were seen in neighbors regions in the early frames. The lesion was seen on the real localization site and additional lesions were seen in the neighbor regions in the late frames.
In movement −Y4 the lesion was seen on the real localization site but additional lesions were seen in neighbors regions in the early and late frames.
4. Discussion
{0>Miyokard perfüzyon SPECT görüntülenmesi sırasında hastaların yaklaşık % 25’i hareket ettiği tespit edilmiştir.<}0{>It is reported that about 25% of the patients move during myocardial perfusion SPECT imaging.<0} {0>Görüntüleme devam ederken hastanın hareketi perfüzyon defektleri içeren görüntüler meydana getirir.<}0{>As imaging takes place, patient’s movement can produce false perfusion defects or hide the present perfusion defects.<0} {0>Uzun süren görüntüleme zamanları ile hasta yatak üzerinde kıpırdanmaya başlamaktadır(27).<}0{>With a long duration of imaging, the patient tends to move on the imaging bed [18] .<0} {0>Hasta hareketinin saptanması ve düzeltilmesi SPECT görüntülemede son derece önemli olup düzeltilmemesi durumunda değerlendirmenin güvenilirliğini azaltacaktır.<}0{>Detection and correction of patient movement are of utmost importance in SPECT imaging and motion correction should be performed to obtain correct slices. Various programs and techniques were developed in order to correct these defects [15] - [17] .<0} <0}
{0>SPECT görüntüleme esnasındaki olası hasta hareketleri sabit vücut hareketi, periyodik lokal deformasyonlar ve lokal olmayan deformasyonlar olmak üzere üç grupta toplanabilir.<}0{>Possible patient movements during SPECT imaging include constant body motion, periodic local deformations and non-local deformations.<0} {0>Sabit vücut hareketi üç boyutta yer değiştirme hareketidir.<}0{>Constant body motion is movement in three dimensions.<0} {0>Kalp ritmi ve solunum ile gerçekleşen hareketlerin oluşturduğu artefaktlar periyodik lokal deformasyon başlığında incelenebilir.<}0{>Artifacts caused by movements due to cardiac contractions and breathing can be classified as periodic local deformations.<0} {0>Lokal olmayan deformasyonlar ise görüntüleme masasında daha rahat yatabilmek için hastanın olduğu yerde yaptığı kıvrılma ve bükülme hareketleridir (15).<}0{>Non-local deformations are the patient’s curling, and bending movements during the process [10] . <0}
{0>Kovalski ve arkadaşları solunuma bağlı artefaktların incelenmesi ile yaptıkları çalışmada artefakt yüzdesini, 20 hastanın her biri için Bull’s Eye haritasında hareket düzeltmesi yapılmadan önce ortalama % 3.75 olarak tespit etmişlerdir.<}0{>In a study on artifacts caused by breathing, Kovalski et al. found that rate of artifacts for each of 20 patients was 3.75% on average before correction.<0} {0>Hareket düzeltmesinden sonra ise bu oranın % 1.58’e azaldığını gözlemlemişlerdir (18,28,29).<}0{>They observed that after motion correction this rate decreased to 1.58% [13] [20] [21] .<0}
{0>Philippe ve arkadaşları hasta hareketini algılamak için görsel takip sistemi kurmuşlardır.<}0{>Philippe et al. set up a visual follow up system in order to perceive patient’s movement.<0} {0>Eksternal bir cihazla (visual tracking system, VTS) SPECT sırasında hastanın hareketi saptanmış, ikinci olarak veriler proses edilerek hasta hareketiyle ilgili durum saptanmış ve üçüncü olarak da 1, 2 ve 5 mm’lik hareket etkisi ile oluşan artefaktlarda hareket düzeltmesi yapılarak optik kamera görüntülenmesi ile SPECT arasında bir eşleme oluşturulmuştur (21,22).<}0{>Patient’s movement during SPECT was detected with an external equipment (visual tracking system, VTS), secondly, condition related to patient motion was identified by processing the data and thirdly, a synchronization was created between optic camera imaging and SPECT by correcting movement in artifacts [16] .
{0>Germano ve arkadaşları 201Tl ile yapılan SPECT çalışmalarında, 6.5 mm’den küçük hareketlerin etkisinin düzeltilmesine gerek olmadığını ve görüntülemenin ortasında aksiyal yönde ve 6.5 mm hareket için ise %5-%40 arasında harekete bağlı false pozitif değerlendirmeler rapor etmişlerdir (25).<}0{>Germano et al. reported that there was no need to correct movements less than 6.5 mm and false-positive findings between 5% and 40% was also reported for a 6.5 mm simulated axial motion in the middle of 201Tl SPECT studies [18] .<0} {0>Glenn ve arkadaşları, 6- 8 mm aralığına gerçeleşen hareket ile oluşan artefaktın orta şiddetle düzeltilebilir hatalara neden olduğunu ve 8 mm’den büyük hareketler ile oluşan artefaktların ise klinik olarak ciddi artefaktlara yol açtığını belirtmişlerdir (30).<}0{>Glenn et al. reported that artifact occurring due to a movement of 6 - 8 mm produced moderate and correctable errors while artifacts caused by movements bigger than 8 mm resulted in severe errors that might affect clinical interpretation [22] .<0}
{0>Cooper ve arkadaşları, vertikal yöndeki hareketin lateral yöndekine göre daha fazla artefakt oluşturduğunu tespit etmişlerdir.<}0{>Cooper et al. found that vertical motion produced more artifacts than lateral motion.<0} {0>Çalışmalarında, 6.5 mm’lik hasta hareketinin dedekte edilebildiğini ancak 13 mm veya daha fazla olan hareketlerde klinik açıdan önem taşıyan artefaktların oluştuğunu rapor etmişlerdir (26).<}0{>They reported that patient movement of 6.5 mm can be detected, but clinically significant artifacts occur in movements equal to or bigger than 13 mm [23] . <0}{0>Matsumoto ve arkadaşları, tek ve çift başlı gama kamera ile yaptıkları çalışmada hareketin görüntüye etkisi ile birlikte hareketin farklı kameralardaki etkilerini de incelemişlerdir.<}0{>In a study done with single and double-headed gamma cameras, Matsumo et al. investigated the effects of motion in different cameras.<0} {0>Sayısal olarak, 6.4 mm hareket etkisi altında sol ventriküldeki ortalama defekt yüzdesini Tek ve çift başlı gama kameralarda sırasıyla %0.6 ve %0.8 olarak bulmuşlardır.<}0{>They found that average defect rates in the left ventricle with 6.4 mm of movement in single and double headed gamma cameras were 0.6% and 0.8%, respectively.<0} {0> 12.8 mm hareket etkisi altında sol ventriküldeki ortalama defekt yüzdesini Tek ve çift başlı gama kameralarda sırasıyla %3.8 ve %5.7 olarak bulmuşlardır.<}83{>They also reported that average defect rates in the left ventricle with 12.8 mm of movement in single and double headed gamma cameras were 3.8% and 5.7%, respectively.<0} {0> 19.2 mm hareket etkisi altında ise sol ventriküldeki ortalama defekt yüzdesini tek ve çift başlı gama kameralarda sırasıyla %8.1 ve %11.8 olarak bulmuşlardır.<}88{>Average defect rates in the left ventricle with 19.2 mm of movement in single and double headed gamma cameras were 8.1% and 11.8%, respectively [10] .<0} {0>Bu değerler ile hastanın hareket miktarı ile perfüzyon defekt miktarı orantılı olduğu sonucuna varılmıştır.<}0{>These findings suggest that there is a correlation between amount of patient motion and that of perfusion defect.<0} {0>Çift başlı kameralarda oluşan artefakt yüzdesinin teorik olarak fazla çıkmasının sebebi aynı anda birçok projeksiyon elde etmesinden kaynaklanmaktadır.<}0{>The reason for the percentage of artifacts originating from double headed cameras are theoretically higher is that several projections are obtained simultaneously.<0} {0>Öte yandan tek detektörlü kameraların da aynı sayıda sayım almaları gerektiğinden görüntüleme zamanları uzamaktadır, bu durum ise hasta hareketi ile oluşan artefaktlara neden olurken hassasiyeti azaltmaktadır (31).<}0{>On the other hand, since single detector cameras need to collect the same number of counts, acquisition time for these cameras is longer and this condition diminishes sensitivity while producing artifacts due to patient motion [10] [24] - [26] . <0}{0>William ve arkadaşları, fantom kullanarak 201Tl ile yaptıkları kardiyak görüntüleme çalışmasında, hareket sonucu oluşan artefaktların başarıyla elemine edildiğini, 36 farklı hasta çalışmasında ise görüntü kalitesinin yarıdan fazla iyileştirdiğini belirtmişlerdir (32).<}0{>William et al. reported that artifacts resulting from motion can be eliminated successfully in a cardiac imaging study done with 201Tl using phantom and they also noted that image quality improved by more than 50% in a study involving 36 different patients [27] .<0}
{0>±1 ve ±2 cm’lik hareketlerin, hareket düzeltmesi kullanıldığında lezyonun yeri ve şiddeti açısından referans görüntüye yakın görüntülerle karşılaşılmıştır.<}0{>In our study, whenIn our study, when motion correction was applied, movements of ±1 and ± 2 cm were compared with images corresponding reference image.<0}{0>Önemli olan kısım, vertikal ve lateral eksenlerdeki hareketin görüntüye etkisi farklı şiddetlerde gözlenmiş olmasıdır.<}0{>Artifacts Image artifacts produced by movement in vertical and lateral axis were different in severity.<0} {0>Diğer literatür çalışmalarında da belirtildiği gibi vertikal eksendeki artefakt şiddeti daha baskın bulunmuştur.<}0{>As stated in the literature, severity of artifact in vertical axis was more prominent.<0}
{0>Bütün framelerde, ±3 cm’lik hareketlerde farklı şiddetlerde olmakla beraber distorsiyon veya distorsiyon başlangıcı gözlenmiştir.<}0{>Distortion or early distortion was observed in all frames at ± 3 cm motions, albeit different in severity.<0} {0>Derecesi değerlendirilemez olarak belirlediğimiz görüntüler hareket düzeltme seçeneği kullanıldıktan sonra process yapıldığında çoğu referansa yakın, bazıları ise referans ile aynı dereceye sahip görüntüler elde edilmiştir.<}0{>The images that we considered as unusable were processed by motion correction, images near equivalent to reference image were obtained.<0} {0>Görüntü değerlendirilmesinde en önemli kısımlardan biri, frame sayısı arttıkça şiddeti azalan distorte görüntülerin ve hareket düzeltme seçeneğinin etkinliğinin artmasıdır.<}0{>{0>±4 cm’lik hareketler ise tüm eksenlerde distorte görüntülere neden olmuştur.<}0{>Motion of ± 4 cm resulted in distorted images in all axis.<0} {0>Lezyonun yeri ve şiddeti takip edilememiş, görüntülerde ek konturlarla karşılaşılmıştır.<}0{>Location and severity of the lesion could not be defined and severe distortions were seen.<0} {0>Eksenin yönü önem arz etmeden tüm görüntülerin distorte olduğu sonucuna varılmıştır.<}0{>It was concluded that at 4 cm all images were distorted with no relation to axis direction<0}{0>Hareket düzeltme seçeneğinin kullanıldığı process işlemlerinde dahi beklenen etkinlik sağlanamamıştır.<}0{>even if even if motion correction was used.<0} {0>Bazı görüntülerde ise hareket düzeltme seçeneği sistem tarafından hareket miktarı çok fazla olduğu için kullanılamamıştır.<}0{>In some images, motion correction could not be applied because of excessive movement.<0}
As a result, {0>Çalışmada SPECT görüntülerinde hareket düzeltme tekniği kullanılarak rekonstrükte edilen görüntülerin ±1 ve ± 2 cm hareket etkisi ile alınan görüntülerin tamamında; ± 3 cm hareket etkisi ile alınan görüntülerin bir kısmında referans yakın görüntü kalitesi olduğu saptanmıştır.<}0{>{0>Çalışmamızda, farklı yönlerdeki hareketlerin farklı etkiler meydana getirdiği gözlenmiştir.<}0{>we observed that {0>Özellikle ± 1 cm ve ± 2 cm hareket etkisiyle gerçekleşen görüntülemenin son framelerine doğru meydana gelen hasta hareketi görüntüde tolere edilebilir artefaktlara neden olurken hareket düzeltmesi kullanılarak gerçekleştirilen process işlemleri ile görüntülerin referans görüntüye yaklaştığı saptanmıştır.<}0{>patient’s motion of ± 1 cm and ± 2 cm that occurs towards last frames of the imaging produces tolerable artifacts in the assessment of the image, movement greater than ± 3 cm causes significant image artifacts and this should be considered as a potential source of error in myocardial perfusion studies. In motion corrected images, the lesion could be evaluated in ±1 and ± 2 cm movements while lesion could not be evaluated in ±3 and ± 4 cm movements.<0} {0>Sonuç olarak ±3 cmden fazla hareket klinik olarak önemli görüntü artefaktlarına neden olur ve miyokard perfüzyon çalışmalarında potansiyel hata kaynağı olarak gözönünde bulundurulmalıdır.<}0{>
5. Conclusions
{0>SPECT görüntüleme sırasındaki hasta hareketi perfüzyon defektleri içeren görüntüler meydana getirebilir.<}0{> {0>SPECT görüntüleme sırasındaki hasta hareketi perfüzyon defektleri içeren görüntüler meydana getirebilir.<}0{>Patient motion during myocardial perfusion SPECT imaging studies can produce an image with artifacts mimicking perfusion defects leads to false-positive findings.
The impact of various types and degrees of patient motion evaluated for the lesion is even more crucial.<0} {0>Oluşan defektlerin düzeltilmesi için çeşitli program ve teknikler geliştirilmiş ve geliştirilmektedir.<}0{>{0>Çalışmamızda hareket ile oluşan artefaktın hareket düzeltme programları ile ne kadar düzeltilebileceği araştırılmıştır.<}0{>Patient’s motion occurs at last frames of the imaging process that can be tolerable artifacts in the image. {0>Hasta hareketinin meydana getirdiği artefaktlar beyin, akciğer gibi diğer sintigrafilerde de araştırılabilir, hareket düzeltmenin etkisi değerlendirilebilir.<}0{>
<0}AllAll of the images reconstructed by motion correction technique had better quality. Shifting patient’s movement in all directions by ±1 and ± 2 cm , displaced the localization of the lesion mildly and this did not hamper the evaluation.<0} {0>Ancak ±3 veya ±4’er cm’lik hareketlerin oluşturduğu artefakt nedeniyle lezyonun kısmen izlendiği ya da izlenemediği görülmüştür.<}0{>However, movements of ±3 or ± 4 cm resulted in artifacts which in turn caused partial or no visualization of the lesion.
ReferencesWheat, J.M. and Currie, G.M. (2004) Incidence and Characterization of Patient Motion in Myocardial Perfusion SPECT: Part 1. Journal of Nuclear Medicine Technology, 32, 60-65.Nadine, S. and Andrew, W. (2011) Introduction to Medical Imaging. Physics, Engineering and Clinical Applications, 94-114.Tsui, B.M.W. (1992) Reconstruction and Filtering Methods for Quantitative Cardiac SPECT Imaging Cardiovascular Nuclear Medicine and MRI. Developments in Cardiovascular Medicine, 128, 29-45. http://dx.doi.org/10.1007/978-94-011-2666-3_2Elschot, M., Nijsen, J.F.W., Dam, A.J. and de Jong, H.W.A.M. (2011) Quantitative Evaluation of Scintillation Camera Imaging Characteristics of Isotopes Used in Liver Radioembolization. http://dx.doi.org/10.1371/journal.pone.0026174Thomas, A., Brian, G., Mouaz, A. and Dennis, A. (2010) Single Photon-Emission Computed Tomography. Journal of the American College of Cardiology, 2-10.Lin, G.S., Hines, H.H., Grant, G., Taylor, K. and Ryals, C. (2006) Automated Quantification of Myocardial Ischemia and Wall Motion Defects by Use of Cardiac SPECT Polar Mapping and 4-Dimensional Surface Rendering. Journal of Nuclear Medicine Technology, 34, 13-17.Zakavi, S.R., Zonoozi, A., Kakhki, V.D., Hajizadeh, M., Momennezhad, M. and Ariana, K. (2006) Image Reconstruction Using Filtered Backprojection and Iterative Method: Effect on Motion Artifacts in Myocardial Perfusion SPECT. Journal of Nuclear Medicine Technology, 34, 220-223.Fokas, A.S., Iserles, A. and Marinakis, V. (2006) Reconstruction Algorithm for Single Photon Emission Computed Tomography and Its Numerical Implementation. Journal of the Royal Society Interface, 22, 45-54. http://dx.doi.org/10.1098/rsif.2005.0061Anagnostopoulos, C., Neill, J., Reyes, E. and Prvulovich, E. (2012) Myocardial Perfusion Scintigraphy: Technical Innovations and Evolving Clinical Applications. Heart, 98, 353-359. http://dx.doi.org/10.1136/heartjnl-2011-300678Matsumoto, N., Berman, D.S., Kavanagh, P.B., Gerlach, J., Hayes, S.W., Lewin, H.C., Friedman, J.D. and Germano, G.. (2001) Quantitative Assessment of Motion Artifacts and Validation of a New Motion-Correction Program for Myocardial Perfusion SPECT. Journal of Nuclear Medicine, 42, 687-694.Holly, T.A., Abbott, B.G., Al-Mallah, M., Calnon, D.A., Cohen, M.C., DiFilippo, F.P., et al. (2010) Single Photon-Emission Computed Tomography. Journal of Nuclear Cardiology, 17, 941-973. http://dx.doi.org/10.1007/s12350-010-9246-yBai, C.Y., Conwell, R., Kindem, J., Babla, H., Gurley, M., De Los Santos II, R., et al. (2010) Phantom Evaluation of a Cardiac SPECT/VCT System that Uses a Common Set of Solid-State Detectors for both Emission and Transmission Scans. Journal of Nuclear Cardiology, 17, 459-469. http://dx.doi.org/10.1007/s12350-010-9204-8Kovalski, G., Israel, O., Keidar, Z., Frenkel, A., Sachs, J. and Azhari, H. (2007) Correction of Heart Motion Due to Respiration in Clinical Myocardial Perfüsion SPECT Scans Using Respiratory Gating. Journal of Nuclear Medicine, 48, 630-636. http://dx.doi.org/10.2967/jnumed.106.037390Wheat, J.M. and Currie, G.M. (2004) Incidence and Characterization of Patient Motion in Myocardial Perfusion SPECT: Part 1. Journal of Nuclear Medicine Technology, 32, 60-65.Schumacher, H. and Fisher, B. (2007) A New Approach Motion Correction in SPECT Imaging. In: Bildverarbeitung für die Medizin, 51-55.Bruyant, P.P., Gennert, M.A., Speckert, G.C., Beach, R.D., Morgenstern, J.D., Kumar, N., Nadella, S. and King, M.A. (2005) A Robust Visual Tracking System for Patient Motion Detection in SPECT: Hardware Solutions. IEEE Transactions on Nuclear Science, 52, 1288-1294. http://dx.doi.org/10.1109/TNS.2005.858208Gennert, M.A., Bruyant, P.P., Narayanan, M.V. and King, M.A. (2003) Assessing a System to Detect Patient Motion in SPECT Imaging Using Stereo Optical Cameras. IEEE Nuclear Science Symposium Conference Record, 3, 1567-1570.Germano, G., Chua, T., Kavanagh, P.B., Kiat, H. and Berman, D.S. (1993) Detection and Correction of Patient Motion in Dynamic and Static Myocardial SPECT Using a Multi-Detector Camera. Journal of Nuclear Medicine, 34, 1349-1355.Baggish, A.L. and Boucher, C.A. (2008) Radiopharmaceutical Agents for Myocardial Perfusion Imaging. Circulation, 118, 1668-1674. http://dx.doi.org/10.1161/CIRCULATIONAHA.108.778860Livieratos, L., Rajappan, K., Stegger, L., Schafers, K., Biley, D.L. and Camici, P.G. (2006) Respiratory Gating of Cardiac PET Data in List Mode Acquisition. European Journal of Nuclear Medicine and Molecular Imaging, 33, 584-588. http://dx.doi.org/10.1007/s00259-005-0031-0Wang, Y., Riederer, S. and Ehman, R. (1995) Respiratory Motion of the Heart: Kinematics and the Implications for the Spatial Resolution in Respiratory Coronary Imaging. Magnetic Resonance in Medicine, 33, 713-719. http://dx.doi.org/10.1002/mrm.1910330517Wells, R.G., Ruddy, T.D., DeKemp, R.A., DaSilva, J.N. and Beanlands, R.S. (2010) Single-Phase CT Aligned to Gated PET for Respiratory Motion Correction in Cardiac PET/CT. Journal of Nuclear Medicine, 51, 1182-1190. http://dx.doi.org/10.2967/jnumed.109.070011Cooper, J., Neuman, P. and McCandless, B. (1992) Effect of Patient Motion on Tomographic Myocardial Perfusion Imaging. Journal of Nuclear Medicine, 33, 1566-1571.Rahmim A. ,et al. (2005)Advanced Motion Correction Methods in PET Iranian Journal of Nuclear Medicine 13, 1-17.Schafers, K. and Stegger, L. (2008) Combined Imaging of Molecular Function and Morphology with PET/CT and SPECT/CT: Image Fusion and Motion Correction. Basic Research in Cardiology, 103, 191-199. http://dx.doi.org/10.1007/s00395-008-0717-0Massardo, T., Jaimovich, R., Faure, R., Munoz, M., Alay, R. and Gatica, H. (2010) Motion Correction and Myocardial Perfusion SPECT Using Manufacturer Provided Software. Does It Affect Image Interpretation? European Journal of Nuclear Medicine and Molecular Imaging, 37, 758-764. http://dx.doi.org/10.1007/s00259-009-1290-yGeckle, W.J., Frank, T.L., Links, J.M. and Becker, L.C. (1988) Correction for Patient and Organ Movement in SPECT: Application to Exercise Thallium-201 Cardiac Imaging. Journal of Nuclear Medicine, 29, 441-450.