Evaluating presynaptic and postsynaptic innervation in heart failure.

Congestive heart failure increasingly contributes to the overall morbidity and mortality associated with cardiovascular disease. Although significant advances in therapies allow patients to feel better and have improved functional status and survival, not all patients respond equally to these therapies. Moreover, for any given level of left ventricular systolic dysfunction, it is difficult to predict who will have progressive heart failure leading to death or transplantation or who will die suddenly. It has long been recognized that the sympathetic nervous system plays a major role in the morbidity and mortality associated with congestive heart failure from systolic left ventricular dysfunction. Although some of the sympathetic effect occurs at the systemic level, malfunction at the ventricular myocyte-sympathetic nerve terminal interface is likely a major contributor to sudden death and progressive heart failure. Imaging the cardiac sympathetic nervous system can be used to evaluate this myoneural interface and to predict outcome.

Effect of reconstruction algorithms on myocardial blood flow measurement with 13N-ammonia PET.

UNLABELLED: Filtered backprojection (FBP) is the traditional method for 13N-NH3 PET studies. Ordered-subsets expectation maximization (OSEM) is popular for PET studies because of better noise properties. Scant data exist on the effect of reconstruction algorithms on quantitative myocardial blood flow (MBF) estimation. METHODS: Twenty patients underwent dynamic acquisition rest/stress 13N-NH3 studies. In Part 1, 19 rest/stress image pairs were reconstructed by FBP (10-mm Hanning filter) and by OSEM with 28 subsets and 2 (OSEM2), 6 (OSEM6), or 8 iterations (OSEM8), and a 10-mm postreconstruction smoothing gaussian filter. In Part 2, 9 image pairs were reconstructed by FBP (10-mm Hanning filter) and by OSEM with 28 subsets, 8 iterations, and a gaussian 5-, 10-, or 15-mm postreconstruction smoothing filter. Average MBF (mL/min/mL of myocardium) was calculated using a 3-compartment model. RESULTS: Part 1: For rest MBF, the correlations between FBP and each of the OSEM algorithms were r2 = 0.71, 0.73, and 0.77, respectively. MBF by OSEM6 (0.98 +/- 0.48 [mean +/- SD]) and OSEM8 (0.96 +/- 0.46) was not significantly different from FBP (1.02 +/- 0.39), but OSEM2 (0.80 +/- 0.37) was significantly lower (P < 0.0003). With stress, the correlations were high between FBP and OSEM6 and OSEM8 (r2 = 0.85 and 0.90), and MBF by OSEM6 and OSEM8 was not significantly different from FBP. Part 2: Resting MBF correlated well between FBP and all OSEM smoothing filters (r2 = 0.82, 0.85, and 0.88). Rest MBF using postsmoothing 5- or 10-mm filters was not different from FBP but was significantly lower with the 15-mm filter (P < 0.05). With stress, the correlations were good between FBP and OSEM regardless of smoothing (r2 = 0.76, 0.77, and 0.79). However, MBF with postsmoothing 10- and 15-mm filters was significantly lower than by FBP (P < 0.05). CONCLUSION: Reconstruction algorithms significantly affect the estimation of quantitative blood flow data and should not be assumed to be interchangeable. Although aggressive smoothing may produce visually appealing images with reduced noise levels, it may cause an underestimation of absolute quantitative MBF. In selecting a reconstruction algorithm, an optimal balance between noise properties and diagnostic accuracy must be emphasized.