Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2010 Nov 29.
Published in final edited form as: Nucl Med Commun. 2009 Sep;30(9):700–705. doi: 10.1097/MNM.0b013e32832ad83d

Impact of image reconstruction on phase analysis of ECG-gated myocardial perfusion SPECT studies

Dianfu Li a,b, Yanli Zhou a,b, Jianlin Feng b, Donglan Yuan b, Kejiang Cao a, Ernest V Garcia c, Ji Chen c
PMCID: PMC2992840  NIHMSID: NIHMS178923  PMID: 19550363

Abstract

Objectives

Phase analysis (SyncTool) has been developed to assess left-ventricular (LV) dyssynchrony from gated myocardial perfusion single-photon emission computed tomography (GSPECT) studies. Conventionally, GSPECT data are reconstructed using filtered backprojection (FBP). This study is intended to determine the impact of various iterative reconstruction methods on SyncTool.

Methods

Thirty consecutive patients, acquired using a Philips CardioMD system, were enrolled in this study. The GSPECT data were reconstructed using FBP, maximum likelihood expectation maximization (MLEM), MLEM with three-dimensional resolution recovery (Astonish), MLEM with Vantage attenuation correction (AC), and MLEM with Vantage AC and three-dimensional Monte Carlo-based scatter correction (ACSC), respectively. The reconstructed data were then submitted to SyncTool to measure LV dyssynchrony (phase standard deviation and histogram bandwidth). The paired t-test was used to compare the LV dyssynchrony indices given by MLEM, Astonish, AC, and ACSC, respectively, with those given by the FBP.

Results

No statistical significance was observed for any comparison between iterative reconstruction methods and the FBP.

Conclusion

Reconstruction methods have insignificant impact on the LV dyssynchrony indices, indicating that the standard FBP reconstruction is sufficient for accurate phase analysis, supporting the widespread clinical use of SyncTool in measuring LV dyssynchrony.

Keywords: gated myocardial perfusion single-photon emission computed tomography, image reconstruction, left-ventricular dyssynchrony, phase analysis

Introduction

Cardiac resynchronization therapy (CRT) is approved for the treatment of patients with end-stage heart failure, depressed left-ventricular (LV) ejection fraction (LVEF) (< 35%) and prolonged QRS complex on the surface ECG (> 120 ms). Improvements from CRT, such as quality of life, functional class, exercise capacity, and LVEF have been demonstrated in multiple studies [1-4]. Notably, two studies have shown survival benefits of CRT [5,6].

Using standard criteria for selecting patients for CRT, approximately 30–40% of patients fail to benefit from CRT [7]. It has been suggested that electrical dyssynchrony as determined by QRS duration may not necessarily represent mechanical dyssynchrony and, therefore, might not represent the best predictor of response to CRT [8-10]. Therefore, cardiac mechanical dyssynchrony has been investigated recently in order to more accurately select patients who would more consistently benefit from CRT. Recent studies have shown that LV mechanical dyssynchrony measured by tissue Doppler imaging (TDI) can predict patient response to CRT [11-13]. However, the Predictors of Response to Cardiac Resynchronization Therapy trial showed that individual TDI measures of mechanical dyssynchrony added little to the ability to predict patient response to CRT. The relatively high intercorelab variability in quantifying the degree of mechanical dyssynchrony was a significant hindrance to the study [14].

Recently, phase analysis (SyncTool, Emory University, Atlanta, Georgia, USA) has been developed for the assessment of LV dyssynchrony from gated single-photon emission computed tomography (GSPECT) myocardial perfusion imaging (MPI) [15]. This technique approximates the variation of LV wall thickness using continuous Fourier harmonic functions so that it has sufficient temporal resolution to analyze LV dyssynchrony [16]. This technique was shown to appropriately discriminate between normal controls and various patient cohorts (left bundle branch block, right bundle branch block, ventricular paced rhythms, and LV dysfunction with LVEF < 40%), who were expected on average to have different degrees of LV dyssynchrony [17]. Moreover, SyncTool showed good correlation with TDI in a study including 75 heart failure patients [18] and good sensitivity and specificity (> 70%) for the prediction of response to CRT in a study including 42 heart failure patients [19].

The earlier studies of phase analysis used the GSPECT images reconstructed by filtered backprojection (FBP) –the conventional, most widely used reconstruction method. Iterative reconstruction methods such as maximum likelihood expectation maximization (MLEM), MLEM with three-dimensional (3D) resolution recovery, and attenuation correction (AC) and scatter correction (SC) are now clinically available. These iterative methods have undergone expensive clinical validations and shown to improve the quality of myocardial perfusion images and diagnostic accuracy [20-29]. However, the iterative methods have not yet been validated for GSPECT studies and their value in improving the accuracy of LV function measurement such as LVEF, and LV systolic and diastolic volumes remain unclear. Phase analysis is a count-based method using the partial volume effect. Simply stated, any myocardial wall that has a linear dimension that is less than twice the spatial resolution of an imaging system will exhibit reduced maximal counts in the direction of that dimension in proportion to change in length [30]. This linear proportion between the change in maximal counts and the change in myocardial wall thickness has been demonstrated in a phantom experiment [31]. Therefore, reconstruction may have larger impact on LV dyssynchrony measurement than LV function measurement, as the former is count-based and the latter mainly depends on myocardial wall edge detection. The purpose of this study is to compare various clinically available iterative reconstruction methods with the FBP in measuring LV dyssynchrony.

Materials and methods

Patients

Thirty consecutive patients, acquired using a Philips CardioMD system (Cleveland, Ohio, USA), were enrolled. This cohort included six patients with cardiomyopathy, 10 patients with coronary artery disease, and 14 patients without definite heart disease, and among them nine patients with hypertension. All of the patients underwent rest sestamibi GSPECT MPI. Four of the 30 enrolled patients did not have stress scan because of their severe heart failure, and therefore 26 patients had stress 99mTc sestamibi GSPECT MPI (17 had exercise stress and nine had adenosine stress). The stress and rest scans were performed on separate days with 30 mCi of 99mTc sestamibi injected, respectively. Twenty percent energy windows of approximately 140 and 100 keV were used to acquire the emission and transmission images, respectively. A total of 64 projections (24 s/projection, total acquisition time of 14 min) were obtained over a 180° circular orbit. The GSPECT acquisition acquired 8 frames/cardiac cycle. Data were stored in a 64 × 64 matrix with 6.4 mm/pixel.

Processing

All GSPECT data were reconstructed using the following five methods with their manufacturer recommended parameters.

  1. FBP: Butterworth low-pass filtering with a cut-off frequency of 0.5 Nyquist and an order of 5;

  2. MLEM: 30 iterations, starting from a uniform image;

  3. MLEM with AC (Vantage Pro, Cleveland, Ohio, USA): 30 iterations for emission reconstruction, starting from a uniform imageand 12 iterations for Bayesian transmission reconstruction, starting from a uniform image;

  4. MLEM with AC and SC with 3D Monte Carlo-based scatter modeling (available with the Philips Jetstream workstation, Cleveland, Ohio, USA): 30 iterations, starting from a uniform image;

  5. MLEM with 3D resolution recovery (Astonish, Cleveland, Ohio, USA): 30 iterations, starting from a uniform image.

All reconstructed data were reoriented to generate gated short-axis images and then submitted to phase analysis. Phase standard deviation and phase histogram bandwidth were automatically calculated as quantitative measures of LV dyssynchrony [15]. Figure 1 shows the processing steps for phase analysis.

Fig. 1.

Fig. 1

Open in a new tab

Processing steps for phase analysis. Gated myocardial perfusion single-photon emission computed tomography (GSPECT) myocardial perfusion imaging (MPI) short-axis images served as input. The images underwent three-dimensional (3D) sampling to get regional maximum counts over 600 regions of the left ventricle (LV). The regional maximum counts were proportional to the wall thickness of the region. Regional wall-thickening curves were then constructed and submitted to the Fourier analysis. The phase of the first Fourier harmonic function was used to represent the onset of mechanical contraction. Phase polar map and phase histogram bandwidth were used to quantify LV dyssynchrony. For the patient example shown in this figure, the phase polar map shows a significant phase delay (bright region) at the anterior and apical wall. The location of the phase delay matches well with the perfusion defect shown in the perfusion polar map.

Statistical analysis

Paired t-tests (N = 30 for rest, N = 26 for stress) were used to compare the LV dyssynchrony indices given by MLEM, AC, Astonish, and ACSC with those given by the FBP. Paired t-tests were also used to compare the LV dyssynchrony indices between the rest and exercise stress studies (N = 17) and between the rest and adenosine stress studies (N = 9), respectively. A P value of less than 0.05 was considered statistically significant.

Results

Phase analysis showed that eight of the 30 patients had severe LV dyssynchrony based on the published normal limits [15]. Both phase standard deviation and histogram bandwidth were greater than 2 standard deviations from the normal mean values. The eight patients with severe LV dyssynchrony included the six patients with cardiomyopathy and two coronary artery disease patients.

The LV dyssynchrony indices given by the FBP and by the iterative reconstruction methods are compared in Fig. 2. None of the comparisons showed statistical significant differences for the stress studies and rest studies. These data showed that phase analysis of GSPECT MPI studies, no matter what reconstruction method was used, produced essentially similar results.

Fig. 2.

Fig. 2

Open in a new tab

Comparisons of the left-ventricular (LV) dyssynchrony indices [phase standard deviation (SD) and histogram bandwidth] given by the iterative reconstruction methods to those given by the filtered backprojection (FBP) of the stress (a) and rest (b) studies. The iterative reconstruction methods compared in this figure include maximum likelihood expectation maximization (MLEM), MLEM with attenuation correction (AC), MLEM with AC and Monte Carlo based scatter correction (ACSC), and MLEM with Astonish resolution recovery (AST). None of the comparisons (paired t-test) show statistical significance, indicating that reconstruction method has insignificance impact on phase analysis.

The LV dyssynchrony indices measured by the FBP were compared between the exercise stress studies and rest studies and between the adenosine stress studies and rest studies. Figure 3 shows the results. The LV dyssynchrony indices measured from the exercise stress studies are systematically smaller than those measured from the rest studies in the same patients. The LV dyssynchrony indices measured from the adenosine stress studies are similar to those measured from the rest studies in the same patients. Similar findings were observed from the data reconstructed by the iterative reconstruction methods.

Fig. 3.

Fig. 3

Open in a new tab

Comparisons of the left-ventricular (LV) dyssynchrony indices [phase standard deviation (SD) and histogram bandwidth, measured by the filtered backprojection (FBP)] between the exercise stress and rest studies and between the adenosine stress and rest studies. The LV dyssynchrony indices measured from the exercise stress studies are systematically smaller than those measured from the rest studies of the same patients. The LV dyssynchrony indices measured from the adenosine stress studies are similar to those measured from the rest studies of the same patients.

Figure 4 shows a patient example. This patient had nonischemic cardiomyopathy with depressed LVEF (LVEF < 35%). The exercise stress and rest GSPECT MPI data were reconstructed using FBP, MLEM, MLEM with 3D resolution recovery, MLEM with AC, and MLEM with AC and SC, respectively, and then submitted to SyncTool. As shown in this figure, all of the reconstruction methods resulted in essentially similar phase histograms, phase standard deviations, and histogram bandwidths. This example also showed that exercise stress had increased LV synchronicity.

Fig. 4.

Fig. 4

Open in a new tab

Patient example. Shown in this figure are phase histograms of exercise stress and rest gated myocardial perfusion single-photon emission computed tomography (GSPECT) myocardial perfusion imaging (MPI) data reconstructed by filtered backprojection (FBP), maximum likelihood expectation maximization (MLEM), MLEM with Astonish three-dimensional resolution recovery (AST), MLEM with attenuation correction (AC), and MLEM with AC and scatter correction (ACSC), respectively. SD, standard deviation.

Discussion

The phase analysis has been developed and validated to measure LV mechanical dyssynchrony from GSPECT MPI data. All of its validations used the GSPECT short-axis images reconstructed by FBP – the standard, most widely used reconstruction method for GSPECT MPI data. The FBP takes minimal computation time and does not require additional transmission scans. Iterative reconstruction methods, especially those with AC, resolution recovery, and scatter correction, have shown to improve image quality and diagnostic accuracy for myocardial perfusion assessment [20-29]. However, as shown in this study the benefits from iterative reconstruction methods were insignificant for LV dyssynchrony assessment although the phase analysis is a count-based technique. This study compared the phase analysis of GSPECT MPI data reconstructed using FBP, MLEM, MLEM with AC, MLEM with ACSC, and MLEM with 3D resolution recovery. All of the reconstruction methods produced essentially similar results. We had shown that compared with existing iterative methods, standard FBP reconstruction is as accurate as iterative methods in measuring phase standard deviation and histogram bandwidth extracted from conventional GSPECT MPI data. These findings support the widespread clinical use of phase analysis in measuring LV dyssynchrony.

The iterative reconstruction methods analyzed in this study had a major limitation to LV function assessment – the transmission scans were not gated. The AC and ACSC used attenuation maps averaged over the cardiac cycle, as is the current custom. Therefore, the AC and ACSC applied similar count changes on every frame so that their impact on LV dyssynchrony assessment was insignificant. Radionuclide-based transmission imaging has limited count densities. Thus, even if it was made to acquire gated transmission scans, the accuracy of AC and ACSC could be limited because of low count densities in the transmission images. AC and ACSC based on gated computed tomography scans will be possible to improve LV dyssynchrony assessment and remain to be investigated.

This study also showed a trend that GSPECT data acquired from the exercise stress, but not the adenosine stress, could yield more synchronized phase distribution (smaller phase standard deviation and narrower phase histogram) from the phase analysis than those acquired from the rest for the same patients. It is not surprising, because adenosine stress does not increase oxygen demand and its hemodynamic effects are less than exercise stress. In addition, the delay time from stress injection to acquisition was shorter for exercise stress than for adenosine stress, so that the exercise stress study was closer to peak stress than the adenosine stress study. As most of the MPI studies are carried out using the same-day protocol and usually only the stress scans are gated, it is important to relate the LV dyssynchrony indices measured from stress scans to those measured from baseline resting scans. However, considering the limited size of the cohort, this relationship cannot be established from this study and requires further investigation.

Conclusion

The phase analysis of GSPECT MPI data, reconstructed by FBP and iterative methods with compensations for physical factors, produced essentially similar results. Standard FBP reconstruction is as accurate as iterative methods in measuring phase standard deviation and histogram bandwidth extracted from conventional GSPECT MPI data. These findings support the widespread clinical use of phase analysis in measuring LV dyssynchrony.

Acknowledgments

Dr Chen and Dr Garcia received royalties from the sale of the Emory Cardiac Toolbox. The terms of this arrangement have been reviewed and approved by the Emory University in accordance with its conflict-of-interest practice. This study was also a part of the NIH research (1R01HL094438-01).

References

  • 1.Abraham WT, Hayes DL. Cardiac resynchronization therapy for heart failure. Circulation. 2003;108:2596–2603. doi: 10.1161/01.CIR.0000096580.26969.9A. [DOI] [PubMed] [Google Scholar]
  • 2.Higgins SL, Hummel JD, Niazi IK, Giudici MC, Worley SJ, Saxon LA, et al. Cardiac resynchronization therapy for the treatment of heart failure in patients with intraventricular conduction delay and malignant ventricular tachyarrhythmias. J Am Coll Cardiol. 2003;42:1454–1459. doi: 10.1016/s0735-1097(03)01042-8. [DOI] [PubMed] [Google Scholar]
  • 3.Young JB, Abraham WT, Smith AL, Leon AR, Lieberman R, Wilkoff B, et al. Combined cardiac resynchronization and implantable cardioversion defibrillation in advanced chronic heart failure: the MIRACLE ICD Trial. JAMA. 2003;289:2685–2694. doi: 10.1001/jama.289.20.2685. [DOI] [PubMed] [Google Scholar]
  • 4.McAlister F, Ezekowitz J, Wiebe N, Rowe B, Spooner C, Crumley E, et al. Cardiac resynchronization therapy for congestive heart failure. Evid Rep Technol Assess (Summ) 2004;106:1–8. [PMC free article] [PubMed] [Google Scholar]
  • 5.Bristow MR, Saxon LA, Boehmer J, Krueger S, Kass DA, De Marco T, et al. Cardiac-resynchronization therapy with or without an implantable defibrillator in advanced chronic heart failure. N Engl J Med. 2004;350:2140–2150. doi: 10.1056/NEJMoa032423. [DOI] [PubMed] [Google Scholar]
  • 6.Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, et al. The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med. 2005;352:1539–1549. doi: 10.1056/NEJMoa050496. [DOI] [PubMed] [Google Scholar]
  • 7.Leclercq C, Kass DA. Retiming the failing heart: principles and current clinical status of cardiac resynchronization. J Am Coll Cardiol. 2002;39:194–201. doi: 10.1016/s0735-1097(01)01747-8. [DOI] [PubMed] [Google Scholar]
  • 8.Auricchio A, Stellbrink C, Block M, Sack S, Vogt J, Bakker P, et al. Effect of pacing chamber and atrioventricular delay on acute systolic function of paced patients with congestive heart failure. The Pacing Therapies for Congestive Heart Failure Study Group. The Guidant Congestive Heart Failure Research Group. Circulation. 1999;99:2993–3001. doi: 10.1161/01.cir.99.23.2993. [DOI] [PubMed] [Google Scholar]
  • 9.Brecker SJ, Xiao HB, Sparrow J, Gibson DG. Effects of dual-chamber pacing with short atrioventricular delay in dilated cardiomyopathy. Lancet. 1992;340:1308–1312. doi: 10.1016/0140-6736(92)92492-x. [DOI] [PubMed] [Google Scholar]
  • 10.Leclercq C, Faris O, Tunin R, Johnson J, Kato R, Evans F, et al. Systolic Improvement and mechanical resynchronization does not require electrical synchrony in the dilated failing heart with left bundle-branch block. Circulation. 2002;106:1760–1763. doi: 10.1161/01.cir.0000035037.11968.5c. [DOI] [PubMed] [Google Scholar]
  • 11.Achilli A, Sassara M, Ficili S, Pontillo D, Achilli P, Alessi C, et al. Long-term effectiveness of cardiac resynchronization therapy in patients with refractory heart failure and narrow QRS. J Am Coll Cardiol. 2003;42:2117–2124. doi: 10.1016/j.jacc.2003.08.024. [DOI] [PubMed] [Google Scholar]
  • 12.Bax JJ, Bleeker GB, Marwick TH, Molhoek SG, Boersma E, Steendijk P, et al. Left ventricular dyssynchrony predicts response and prognosis after cardiac resynchronization therapy. J Am Coll Cardiol. 2004;44:1834–1840. doi: 10.1016/j.jacc.2004.08.016. [DOI] [PubMed] [Google Scholar]
  • 13.Bax JJ, Marwick TH, Molhoek SG, Bleeker GB, van Erven L, Boersma E, et al. Left ventricular dyssynchrony predicts benefit of cardiac resynchronization therapy in patients with end-stage heart failure before pacemaker implantation. Am J Cardiol. 2003;92:1238–1240. doi: 10.1016/j.amjcard.2003.06.016. [DOI] [PubMed] [Google Scholar]
  • 14.Chung ES, Leon AR, Tavazzi L, Sun JP, Nihoyannopoulos P, Merlino J, et al. Results of the predictors of response to CRT (PROSPECT) trial. Circulation. 2008;117:2608–2616. doi: 10.1161/CIRCULATIONAHA.107.743120. [DOI] [PubMed] [Google Scholar]
  • 15.Chen J, Garcia EV, Folks RD, Cooke CD, Faber TL, Tauxe EL, Iskandrian AE. Onset of left ventricular mechanical contraction as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging: Development of a diagnostic tool for assessment of cardiac mechanical dyssynchrony. J Nucl Cardiol. 2005;12:687–695. doi: 10.1016/j.nuclcard.2005.06.088. [DOI] [PubMed] [Google Scholar]
  • 16.Chen J, Faber TL, Cooke CD, Garcia EV. Temporal resolution of multi-harmonic phase analysis of ECG-gated myocardial perfusion SPECT studies. J Nucl Cardiol. 2008;15:383–391. doi: 10.1016/j.nuclcard.2008.02.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Trimble MA, Smalheiser S, Bornes-neto S, Chen J, Honeycutt EF, Shaw LK, et al. Evaluation of left ventricular mechanical dyssynchrony as determined by phase analysis of ECG-gated myocardial perfusion SPECT imaging in patients with left ventricular dysfunction and conduction disturbances. J Nucl Cardiol. 2007;14:298–307. doi: 10.1016/j.nuclcard.2007.01.041. [DOI] [PubMed] [Google Scholar]
  • 18.Henneman MM, Chen J, Ypenburg C, Dibbets P, Stokkel M, van der Wall EE, et al. Phase analysis of gated myocardial perfusion SPECT compared to tissue Doppler imaging for the assessment of left ventricular dyssynchrony. J Am Coll Cardiol. 2007;49:1708–1714. doi: 10.1016/j.jacc.2007.01.063. [DOI] [PubMed] [Google Scholar]
  • 19.Henneman MM, Chen J, Dibbets P, Stokkel M, Bleeker GB, Ypenburg C, et al. Can LV dyssynchrony as assessed with phase analysis on gated myocardial perfusion SPECT predict response to CRT? J Nucl Med. 2007;48:1104–1111. doi: 10.2967/jnumed.107.039925. [DOI] [PubMed] [Google Scholar]
  • 20.Ficaro EP, Fessler JA, Shreve PD, Kritzman JN, Rose PA, Corbett JR, et al. Simultaneous transmission/emission myocardial perfusion tomography. Circulation. 1996;93:463–473. doi: 10.1161/01.cir.93.3.463. [DOI] [PubMed] [Google Scholar]
  • 21.Prvulovich EM, Lonn AHR, Bomanji JB, Jarritt PH, Ell PJ. Effect of attenuation correction on myocardial thallium-201 distribution in patients with a low likelihood of coronary artery disease. EurJ Nucl Med. 1997;24:266–275. doi: 10.1007/BF01728762. [DOI] [PubMed] [Google Scholar]
  • 22.Kluge R, Sattler B, Seese A, Knapp WH. Attenuation correction by simultaneous emission-transmission myocardial single-photon emission tomography using a technetium-99m labelled radiotracer: impact on diagnostic accuracy. Eur J Nucl Med. 1997;24:1107–1114. doi: 10.1007/BF01254241. [DOI] [PubMed] [Google Scholar]
  • 23.Chouraqui P, Livschitz S, Sharir T, Wainer N, Wilk M, Moalem I, et al. Evaluation of an attenuation correction method for thallium-201 myocardial perfusion tomographic imaging of patients with low likelihood of coronary artery disease. J Nucl Cardiol. 1998;5:369–377. doi: 10.1016/s1071-3581(98)90142-8. [DOI] [PubMed] [Google Scholar]
  • 24.Gallowitsch HJ, Sykora J, Mikosch P, Kresnik E, Unterweger O, Molnar M, et al. Attenuation-corrected thallium-201 single-photon emission tomography using a gadolinium-153 moving line source: clinical value and the impact of attenuation correction on the extent and severity of perfusion abnormalities. Eur J Nucl Med. 1998;25:220–228. doi: 10.1007/s002590050220. [DOI] [PubMed] [Google Scholar]
  • 25.Gallowitsch HJ, Unterweger O, Mikosch P, Kresnik E, Sykora J, Grimm G, et al. Attenuation correction improves the detection of viable myocardium by thallium-201 cardiac tomography in patients with previous myocardial infarction and left ventricular dysfunction. Eur J Nucl Med. 1999;26:459–466. doi: 10.1007/s002590050411. [DOI] [PubMed] [Google Scholar]
  • 26.Hendel RC, Berman DS, Cullom SJ, Follansbee W, Heller GV, Kiat H, et al. Multicenter clinical trial to evaluate the efficacy of correction for photon attenuation and scatter in SPECT myocardial perfusion imaging. Circulation. 1999;99:2742–2749. doi: 10.1161/01.cir.99.21.2742. [DOI] [PubMed] [Google Scholar]
  • 27.Links JM, Becker LC, Rigo P, Taillefer R, Hanelin L, Anstett F, et al. Combined corrections for attenuation, depth-dependent blur, and motion in cardiac SPECT: a multicenter trial. J Nucl Cardiol. 2000;7:414–425. doi: 10.1067/mnc.2000.108350. [DOI] [PubMed] [Google Scholar]
  • 28.Shotwell M, Singh BM, Fortman C, Bauman BD, Lukes J, Gerson MC. Improved coronary disease detection with quantitative attenuation-corrected Tl-201 images. J Nucl Cardiol. 2002;9:52–61. doi: 10.1067/mnc.2002.119252. [DOI] [PubMed] [Google Scholar]
  • 29.Links JM, DePuey G, Taillefer R, Becker LB. Attenuation correction and gating synergistically improve the diagnostic accuracy of myocardial perfusion SPECT. J Nucl Cardiol. 2002;9:183–187. doi: 10.1067/mnc.2002.120163. [DOI] [PubMed] [Google Scholar]
  • 30.Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. Comput Assist Tomogr. 1979;3:299–308. doi: 10.1097/00004728-197906000-00001. [DOI] [PubMed] [Google Scholar]
  • 31.Galt JR, Garcia EV, Robbins WL. Effects of myocardial wall thickness on SPECT quantification. IEEE Trans Med Imaging. 1990;9:144–150. doi: 10.1109/42.56338. [DOI] [PubMed] [Google Scholar]

RESOURCES