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BackgroundIn pulmonary malignancies pre-interventional 3D estimation of relative lobar perfusion is established to predict post-interventional functional outcome particularly in patients with borderline lung function. Aim was to test whether quantification from SPECT-scanners (non-hybrid) is as accurate as from SPECT/CT-scanners (hybrid) when using dedicated software.MethodsSixty-one patients suffering from pulmonary tumours underwent lung SPECT/CT using Tc-99m MAA to predict postoperative residual lung function prior to surgical treatment. Quantification was done using “HERMES Hybrid 3D–Lung Lobe Quantification”. In the hybrid approach SPECT and combined lowdoseCT/diagnosticCT were used. In the non-hybrid approach SPECT and diagnosticCTs were used, lowdoseCTs were omitted. Bland Altman analysis was done to test for agreement.ResultsThree hundred five lobes were quantified. Evaluation time was 6:37 ± 0.55 min (hybrid) versus 6:34 ± 0.51 min (non-hybrid). Mean lobar value was 20.0 ± 10.5% (range from 0 to 55%) for hybrid and 20.0 ± 10.6% (range from 0 to 58%) for the non-hybrid approach, mean absolute difference was 1.31%, no significant differences were found when analysing all values (p > 0.9). Correlation was excellent (R = 0.984, slope of the regression line 1.001 (p < 0.0001)). Intraclass correlation coefficient was 0.9843. Bland Altman limits were -3.67% and 3.67%.ConclusionExcellent concordance was found for 3D-quantification of relative lung perfusion when comparing a hybrid vs. non-hybrid approach. Using sophisticated software combining the generally available diagnosticCT and conventional SPECT-data reliable results for lobar perfusion can be obtained without the need for costly investment of SPECT/CT systems for this clinical question.

Purpose There is proven evidence for the importance of myocardial perfusion-single-photon emission computed tomography (SPECT) with computerised determination of summed stress and rest scores (SSS/SRS) for the diagnosis of coronary artery disease (CAD). SSS and SRS can thereby be calculated semi-quantitatively using a 20-segment model by comparing tracer-uptake with values from normal databases (NDB). Four severity-degrees for SSS and SRS are normally used: <4, 4–8, 9–13, and ≥14. Manufacturers’ NDBs (M-NDBs) often do not fit the institutional (I) settings. Therefore, this study compared SSS and SRS obtained with the algorithms Quantitative Perfusion SPECT (QPS) and 4D-MSPECT using M-NDB and I-NDB. Methods I-NDBs were obtained using QPS and 4D-MSPECT from exercise stress data (450 MBq 99m Tc-tetrofosmin, triple-head-camera, 30 s/view, 20 views/head) from 36 men with a low post-stress test CAD probability and visually normal SPECT findings. Patient group was 60 men showing the entire CAD-spectrum referred for routine perfusion-SPECT. Stress/rest results of automatic quantification of the 60 patients were compared to M-NDB and I-NDB. After reclassifying SSS/SRS into the four severity degrees, kappa (κ) values were calculated to objectify agreement. Results Mean values (vs M-NDB) were 9.4 ± 10.3 (SSS) and 5.8 ± 9.7 (SRS) for QPS and 8.2 ± 8.7 (SSS) and 6.2 ± 7.8 (SRS) for 4D-MSPECT. Thirty seven of sixty SSS classifications (κ = 0.462) and 40/60 SRS classifications (κ = 0.457) agreed. Compared to I-NDB, mean values were 10.2 ± 11.6 (SSS) and 6.5 ± 10.4 (SRS) for QPS and 9.2 ± 9.3 (SSS) and 7.2 ± 8.6 (SRS) for 4D-MSPECT. Forty four of sixty patients agreed in SSS and SRS (κ = 0.621 resp. 0.58). Conclusion Considerable differences between SSS/SRS obtained with QPS and 4D-MSPECT were found when using M-NDB. Even using identical patients and identical I-NDB, the algorithms still gave substantial different results.

Gated myocardial perfusion SPECT allows calculation of end-diastolic and end-systolic volumes (EDV and ESV, respectively) and left ventricular ejection fraction (LVEF). The quantification algorithms QGS (quantitative gated SPECT), 4D-MSPECT, and CARE heart show a good correlation with cardiac MRI. Nevertheless, differences in contour finding suggest algorithm-specific effects if heart axes vary. The effect of tilting heart axes on gated SPECT was quantified as a possible source of error. Sixty men underwent gated SPECT (450 MBq of (99m)Tc-tetrofosmin or sestamibi, 8 gates/cycle). After correct reorientation (R(0)), datasets were tilted by 5 degrees , 10 degrees , 15 degrees , 20 degrees , 30 degrees , and 45 degrees along both long axes (R(5), R(10), R(15), R(20), R(30), and R(45), respectively). EDV, ESV, and LVEF were calculated using QGS, 4D-MSPECT, and CARE heart. Because a 15 degrees tilt could be a maximum possible misreorientation in routine, R(0) and R(15) results were analyzed in detail. Absolute-difference values between results of tilted and correctly reoriented datasets were calculated for all tilts and algorithms. QGS and CARE heart succeeded for R(0) and R(15) in all cases, whereas 4D-MSPECT failed to find the basal plane in 1 case (patient B). R(2) values between paired R(15)/R(0) results were 0.992 (QGS), 0.796 (4D-MSPECT; R(2) = 0.919 in n = 59 after exclusion of the failed case), and 0.916 (CARE heart) for EDV; 0.994 (QGS), 0.852 (4D-MSPECT; R(2) = 0.906 in n = 59), and 0.899 (CARE heart) for ESV; and 0.988 (QGS), 0.814 (4D-MSPECT; R(2) = 0.810 in n = 59), and 0.746 (CARE heart) for LVEF. Concerning all levels of misreorientation, 1 patient was excluded for all algorithms because of multiple problems in contour finding; additionally for 4D-MSPECT patient B was excluded. In the 45 degrees group, QGS succeeded in 58 of 59 cases, 4D-MSPECT in 58 of 58, and CARE heart in 33 of 59. Mean absolute differences for EDV ranged from 5.1 +/- 4.1 to 12.8 +/- 10.5 mL for QGS, from 6.7 +/- 6.3 to 34.2 +/- 20.7 mL for 4D-MSPECT, and from 5.4 +/- 5.6 to 25.2 +/- 16.1 mL for CARE heart (tilts between 5 degrees and 45 degrees ). Mean absolute differences for ESV ranged from 4.1 +/- 3.7 to 8.0 +/- 9.4 mL for QGS, from 5.6 +/- 8.0 to 10.0 +/- 10.5 mL for 4D-MSPECT, and from 5.4 +/- 5.6 to 25.5 +/- 16.1 mL for CARE heart. Mean absolute differences for LVEF ranged from 1.1% +/- 1.0% to 2.2% +/- 1.8% for QGS, from 4.0% +/- 3.5% to 8.0% +/- 7.1% for 4D-MSPECT, and from 3.4% +/- 2.9% to 9.2% +/- 6.0% for CARE heart. Despite tilted heart axes, QGS showed stable results even when using tilts up to 45 degrees . 4D-MSPECT and CARE heart results varied with reorientation of the heart axis, implying that published validation results apply to correctly reoriented data only.

Aim To define the prognostic impact of stress myocardial perfusion scintigraphy (MPS) in patients with angiographic exclusion of significant coronary artery disease. Methods Angiographic and MPS databases were matched to define patients without significant coronary artery disease by quantitative angiography (diameter stenosis <50%) who underwent stress MPS and coronary angiography within a time period of 3 months. A total of 118 patients were identified and followed for a mean of 6.3 ± 1.2 years for death, a composite of death, myocardial infarction, bypass surgery, or percutaneous coronary intervention [MAE]) as well as occurrence of symptoms (angina or dyspnoe class CCS II to IV). Stress and rest MPS (using 99m Tc-MIBI or tetrofosmin) were analyzed by quantitative perfusion SPECT (QPS) for summed stress and rest scores (SSS/SRS). Results There were 16 deaths, 29 MAE, and 76 patients with MAE or significant symptoms during follow-up. Significant differences in SSS were found between patients who died (9.5 ± 6.9 vs. 5.4 ± 5.6, P  = 0.012), had MAE (8.7 ± 7.2 vs. 5.2 ± 5.0, P  = 0.010), or had MAE or significant clinical symptoms (7.2 ± 7.1 vs. 4.6 ± 6.2, P  = 0.042) compared to those without the respective event. Logistic regression analysis demonstrated SSS to be a predictor of death (OR = 1.074 [95% CI: 1.004-1.149], P  = 0.026) and MAE (OR = 1.087 [95% CI: 1.004-1.181], P  = 0.027). Conclusions In patients without significant angiographic coronary artery disease, the result of stress MPS is a predictor of long-term prognosis. Quantitative analysis of MPS allows definition of patients with a higher likelihood to develop clinical events or symptoms.

[...]stress perfusion CMR alone has already been compared with FFR and shown to have an even better diagnostic accuracy than that reported by Greenwood and colleagues (positive predictive value 90.9%, negative predictive value 93.9%).2 Similar findings were reported by Rieber and colleagues,5 again using CMR perfusion imaging alone. The state-of-theart technology is applied only for CMR imaging. [...]SPECT shows incon clusive results, mainly because of patient motion or attenuation artefacts secondary to long acquisition times and lack of attenuation correction, which are in great part solved by use of modern SPECT instrumentation.3,4 Additionally, by contrast with CMR assess ment, criteria for a positive SPECT are not detailed, which might explain the low diagnostic accuracy reported for SPECT (eg, how is ischaemia assessed?).

Gated myocardial perfusion SPECT allows calculation of enddiastolic and end-systolic volumes (EDV and ESV, respectively) and left ventricular ejection fraction (LVEF). The quantification algorithms QGS (quantitative gated SPECT), 4D-MSPECT, and CARE heart show a good correlation with cardiac MRI. Nevertheless, differences in contour finding suggest algorithm-specific effects if heart axes vary. The effect of tilting heart axes on gated SPECT was quantified as a possible source of error. Methods: Sixty men underwent gated SPECT (450 MBq of ^sup 99m^Tc-tetrofosmin or sestamibi, 8 gates/cycle). After correct reorientation (R^sub 0^), datasets were tilted by 5°, 10°, 15°, 20°, 30°, and 45° along both long axes (R^sub 5^, R^sub 10^, R^sub 15^, R^sub 20^, R^sub 30^, and R^sub 45^, respectively). EDV, ESV, and LVEF were calculated using QGS, 4D-MSPECT, and CARE heart. Because a 15° tilt could be a maximum possible misreorientation in routine, R^sub 0^ and R^sub 15^ results were analyzed in detail. Absolute-difference values between results of tilted and correctly reoriented datasets were calculated for all tilts and algorithms. Results: QGS and CARE heart succeeded for R^sub 0^ and R^sub 15^ in all cases, whereas 4D-MSPECT failed to find the basal plane in 1 case (patient B). R^sup 2^ values between paired R^sub 15^/R^sub 0^ results were 0.992 (QGS), 0.796 (4D-MSPECT; R^sub 2^ = 0.919 in n = 59 after exclusion of the failed case), and 0.916 (CARE heart) for EDV; 0.994 (QGS), 0.852 (4D-MSPECT; R^sup 2^ = 0.906 in n = 59), and 0.899 (CARE heart) for ESV; and 0.988 (QGS), 0.814 (4D-MSPECT; R^sub 2^ = 0.81 0 in n = 59), and 0.746 (CARE heart) for LVEF. Concerning all levels of misreorientation, 1 patient was excluded for all algorithms because of multiple problems in contour finding; additionally for 4D-MSPECT patient B was excluded. In the 45° group, QGS succeeded in 58 of 59 cases, 4D-MSPECT in 58 of 58, and CARE heart in 33 of 59. Mean absolute differences for EDV ranged from 5.1 ± 4.1 to 12.8 ± 10.5 mL for QGS, from 6.7 ± 6.3 to 34.2 ± 20.7 mL for 4D-MSPECT, and from 5.4 ± 5.6 to 25.2 ± 16.1 mL for CARE heart (tilts between 5° and 45°). Mean absolute differences for ESV ranged from 4.1 ± 3.7 to 8.0 ± 9.4 mL for QGS, from 5.6 ± 8.0 to 10.0 ± 10.5 mL for 4D-MSPECT, and from 5.4 ± 5.6 to 25.5 ± 16.1 mL for CARE heart. Mean absolute differences for LVEF ranged from 1.1% ± 1.0% to 2.2% ± 1.8% for QGS, from 4.0% ± 3.5% to 8.0% ± 7.1% for 4D-MSPECT, and from 3.4% ± 2.9% to 9.2% ± 6.0% for CARE heart. Conclusion: Despite tilted heart axes, QGS showed stable results even when using tilts up to 45°. 4D-MSPECT and CARE heart results varied with reorientation of the heart axis, implying that published validation results apply to correctly reoriented data only. [PUBLICATION ABSTRACT]