Influence of reconstruction algorithms on image quality in SPECT myocardial perfusion imaging

• INTRODUCTION: We investigated if image- and diagnostic quality in SPECT MPI could be maintained despite a reduced acquisition time adding Depth Dependent Resolution Recovery (DDRR) for image reconstruction. Images were compared with filtered back projection (FBP) and iterative reconstruction using Ordered Subsets Expectation Maximization with (IRAC) and without (IRNC) attenuation correction (AC).MATERIALS AND METHODS: Stress- and rest imaging for 15 min was performed on 21 subjects with a dual head gamma camera (Infinia Hawkeye; GE Healthcare), ECG-gating with 8 frames/cardiac cycle and a low-dose CT-scan. A 9 min acquisition was generated using five instead of eight gated frames and was reconstructed with DDRR, with (IRACRR) and without AC (IRNCRR) as well as with FBP. Three experienced nuclear medicine specialists visually assessed anonymized images according to eight criteria on a four point scale, three related to image quality and five to diagnostic confidence. Statistical analysis was performed using Visual Grading Regression (VGR).RESULTS: Observer confidence in statements on image quality was highest for the images that were reconstructed using DDRR (P<0·01 compared to FBP). Iterative reconstruction without DDRR was not superior to FBP. Interobserver variability was significant for statements on image quality (P<0·05) but lower in the diagnostic statements on ischemia and scar. The confidence in assessing ischemia and scar was not different between the reconstruction techniques (P = n.s.).CONCLUSION: SPECT MPI collected in 9 min, reconstructed with DDRR and AC, produced better image quality than the standard procedure. The observers expressed the highest diagnostic confidence in the DDRR reconstruction.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine (hsv//eng)

Nyckelord

image quality

MPI SPECT

noise regularization

resolution recovery

Visual Grading Regression

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