Regular monitoring of left ventricular ejection fraction (LVEF) for thalassemia major is widely practiced, but its informativeness for iron chelation treatment is unclear. Eighty-one patients with thalassemia major but no history of cardiac disease underwent quantitative annual LVEF monitoring by radionuclide ventriculography for a median of 6.0 years (interquartile range, 2-12 years). Intraobserver and interobserver reproducibility for LVEF determination were both less than 3%. LVEF values before and after transfusion did not differ, and exercise stress testing did not reliably expose underlying cardiomyopathy. An absolute LVEF of less than 45% or a decrease of more than 10 percentage units was significantly associated with subsequent development of symptomatic cardiac disease (P < .001) and death (P ‫؍‬ .001), with a median interval between the first abnormal LVEF findings and the development of symptomatic heart disease of 3.5 years, allowing time for intervention. In 34 patients in whom LVEF was less than 45% or decreased by more than 10 percentage units, intensified chelation therapy was recommended (21 with subcutaneous and 13 with intravenous deferoxamine). All 27 patients who complied with intensification survived, whereas the 7 who did not comply died (P < .0001). The KaplanMeier estimate of survival beyond 40 years of age for all 81 patients is 83%. Sequential quantitative monitoring of LVEF is valuable for assessing cardiac risk and for identifying patients with thalassemia major who require intensified chelation

There are technical differences between the tracers. Overall image quality score is superior using technetium, with less low-count artefact and less attenuation. Stress defect depth and extent are slightly greater using thallium, with no difference between MIBI and tetrofosmin. All three tracers perform well in clinical terms, with high sensitivity and specificity for angiographic stenosis and no differences in accuracy between the tracers.

Accurate volume of interest (VOI) estimation in PET is crucial in different oncology applications such as response to therapy evaluation and radiotherapy treatment planning. The objective of our study was to evaluate the performance of the proposed algorithm for automatic lesion volume delineation; namely the fuzzy hidden Markov chains (FHMC), with that of current state of the art in clinical practice threshold based techniques. As the classical hidden Markov chain (HMC) algorithm, FHMC takes into account noise, voxel intensity and spatial correlation, in order to classify a voxel as background or functional VOI. However the novelty of the fuzzy model consists of the inclusion of an estimation of imprecision, which should subsequently lead to a better modelling of the 'fuzzy' nature of the object of interest boundaries in emission tomography data. The performance of the algorithms has been assessed on both simulated and acquired datasets of the IEC phantom, covering a large range of spherical lesion sizes (from 10 to 37 mm), contrast ratios (4:1 and 8:1) and image noise levels. Both lesion activity recovery and VOI determination tasks were assessed in reconstructed images using two different voxel sizes (8 mm3 and 64 mm3). In order to account for both the functional volume location and its size, the concept of % classification errors was introduced in the evaluation of volume segmentation using the simulated datasets. Results reveal that FHMC performs substantially better than the threshold based methodology for functional volume determination or activity concentration recovery considering a contrast ratio of 4:1 and lesion sizes of <28 mm. Furthermore differences between classification and volume estimation errors evaluated were smaller for the segmented volumes provided by the FHMC algorithm. Finally, the performance of the automatic algorithms was less susceptible to image noise levels in comparison to the threshold based techniques. The analysis of both simulated and acquired datasets led to similar results and conclusions as far as the performance of segmentation algorithms under evaluation is concerned.