Constrained least‐squares restoration of nuclear medicine images: Selecting the coarseness function

Abstract

Image restoration using the constrained least-squares (CLS) method theoretically adapts to the image being processed. In addition, it only requires knowing the modulation transfer function of the imaging system when applied to nuclear medicine images. Prompted by these observations, a systematic evaluation of the effects of the form of the "coarseness function" [C(f)] used by the CLS method has been conducted. Nine C(f)'s are evaluated using an observer preference and a normalized mean-squared error (NMSE) criterion. This evaluation is conducted for three modulation transfer functions and a wide range of count levels. The results of the subjective studies support using the form of C(f) which has been most widely employed in previous studies, i.e., the form designed to minimize the energy in the second derivative of the restored image. A different form of C(f) is generally found to be optimal by the mean-squared error criterion. The CLS method is then compared to: (1) no processing, (2) count-dependent smoothing, and (3) count-dependent Metz restoration. When evaluated using objective measurements of error and contrast, the CLS method is found to be slightly inferior to the best method, Metz restoration. However, CLS restoration is found to be equal to or better than the other methods when judged by the results of observer preference studies.

Improvement of the diagnostic accuracy of lymph node metastases of colorectal cancer in 18F-FDG-PET/CT by optimizing the iteration number for the image reconstruction

“…We then used the data from 60 min of acquisition as a reference to calculate the 10-min reconstruction images using Eq. 3 [23]:…”

Comparative study of anatomical normalization errors in SPM and 3D-SSP using digital brain phantom

“…The normalized mean-squared error (NMSE) method was used to evaluate the effect of the head tilts on the errors of anatomical standardization. Formula (1) was used for NMSE calculation [16,17]. This NMSE method calculated the deviation square error of the processing image, which we compared with the reference image; the processed image with the smallest NMSE value was selected as the most similar one to the reference image:…”

The usefulness of fully three-dimensional OSEM algorithm on lymph node metastases from lung cancer with 18F-FDG PET/CT

“…The optimal reconstruction parameters were investigated using the normalized mean square error (NMSE) method (Eq. 1) by changing the iteration number at the constant subset number as follows [16]: For 2D-OSEM, iteration number was changed from 1 to 8 at the constant subset number of 14. For FORE ?…”