Nuclear cardiac imaging is a noninvasive, sensitive method providing information on cardiac structure and physiology. Single photon emission tomography (SPECT) evaluates myocardial perfusion, viability, and function and is widely used in clinical routine. The quality of the tomographic image is a key for accurate diagnosis. Image filtering, a mathematical processing, compensates for loss of detail in an image while reducing image noise, and it can improve the image resolution and limit the degradation of the image. SPECT images are then reconstructed, either by filter back projection (FBP) analytical technique or iteratively, by algebraic methods. The aim of this study is to review filters in cardiac 2D, 3D, and 4D SPECT applications and how these affect the image quality mirroring the diagnostic accuracy of SPECT images. Several filters, including the Hanning, Butterworth, and Parzen filters, were evaluated in combination with the two reconstruction methods as well as with a specified MatLab program. Results showed that for both 3D and 4D cardiac SPECT the Butterworth filter, for different critical frequencies and orders, produced the best results. Between the two reconstruction methods, the iterative one might be more appropriate for cardiac SPECT, since it improves lesion detectability due to the significant improvement of image contrast.

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Introduction: Low-dose Whole Body Multi-Detector Computed Tomography (MDCT) has been established as an alternative to conventional X-ray imaging for Multiple Myeloma (MM) diagnosis. During an MDCT scan two dose indices are displayed on the monitor to account for the dose delivered to the patient: the volume computed tomography dose index (CTDI vol ) and the dose length product (DLP). Both parameters though, are not sufficient in estimating the actual dose on their own. Two methods are proposed to promptly evaluate the scan dose, based on the two indices displayed: an effective dose evaluation through the DLP (Huda et al, 2008) and the Size-Specific Dose Estimate (SSDE), which also takes into account the patient's size, based on the CTDI vol (AAPM Report 204). Material and method:In this study a standardized protocol was developed and data from a good number of clinical examinations were collected. Effective dose was calculated based on the scanner displayed DLP. SSDE calculations were based on the scanner displayed values for the CTDI vol . SSDE is the averaged patient dose within the scan volume corrected for patient size. Dose is estimated using both methods for a set of 85 patients, examined for MM, for the torso body part. Although these indices are quite different in principle, they both present a rough but fast and prompt evaluation of the delivered dose. The results of the two methods are presented and evaluated.Result: Calculated ED and SSDE values were found to present a weak correlation. Pairwise comparisons showed that the dose values through the two methods differed significantly (P < 0.001) by (0.92 ± 0.79) mSv. The Bland-Altman plot showed that the 95% LOA is 3.1 mSv wide, yelding a relatively poor agreement between the two methods. Conclusion:The two methods of evaluation of the CT scan dose indices, ED and SSDE, based on DLP and CTDI vol correspondingly, provide an easily applicable dose estimation of a CT scan, but their values are found to present a notable difference. This means that they can not be used interchangeably clinically, but the most appropriate one should be used accordingly. ED relies on standardized phantoms and therefore has shortcomings with respect to its ability to reflect any individual patient effective dose. The SSDE is a good tool for estimating the average radiation dose for a given patient depending on the input parameters and the dimensions of the specific person in question but does not incorporate any organ/tissue weighting factors. It is recommended that when examined patients deviate significantly from the reference person by ICRP, dose should be estimated through the SSDE method. It is also proposed that tissue weighting factors would be incorporated with the SSDE methodology to provide a more refined estimate of risk.

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