sodium-pertechnetate-tc-99m and Respiration-Disorders

This paper describes the recent advances in technical and analytical methods in pulmonary ventilation SPECT studies, including a respiratory-gated image acquisition of Technetium-99m (99mTc)-labeled Technegas SPECT, a fusion image between Technegas SPECT and chest CT images created by a fully automatic image registration algorithm, and a three-dimensional (3D). display of xenon-133 (133Xe) gas SPECT data, and new analytical approaches by means of fractal analysis or the coefficient of variations of the pixel counts for Technegas SPECT data. The respiratory-gated image acquisition can partly eliminate problematic effects of the SPECT images obtained during non-breath-hold. The fusion image is available for routine clinical use, and provides complementary information on function and anatomy. The 3D displays of dynamic 133Xe SPECT data are helpful for accurate perception of the anatomic extent and locations of impaired ventilation, and the assessment of the severity of ventilation abnormalities. The new analytical approaches facilitate the objective assessment of the degrees of ventilation abnormalities.

Topics: Adult; Aged; Female; Humans; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Lung; Lung Diseases; Male; Middle Aged; Pulmonary Ventilation; Radiopharmaceuticals; Respiration Disorders; Sodium Pertechnetate Tc 99m; Subtraction Technique; Tomography, Emission-Computed, Single-Photon; Tomography, X-Ray Computed; Xenon Radioisotopes

Technegas lung ventilation images sometimes have 'hot spots', particularly in patients with respiratory disease. A novel technique is presented for quantifying this 'spottiness' using morphological texture analysis. A set of 32 images from patients with various respiratory diseases is studied. Images are filtered at a range of scales using morphological opening, and the slopes of image metrics versus structuring element size are used as texture parameters. The results are compared with the opinions of three experienced nuclear medicine physicians who have classified the images into two groups, 'spotty' and 'non-spotty', and have ranked the former. For the spotty images, the computer and observer ranks are compared; the highest correlation is rs = 0.66 (p = 0.01) for a single parameter, and rs = 0.71 (p < 0.01) for a combination of two parameters. Using a pair of parameters, 83% and 90% correct classification rates are obtained for the spotty and non-spotty classes, respectively. It is concluded that these texture parameters provide a useful measure of image spottiness, and it is demonstrated that this technique is superior to previously published methods. The practical value of the technique is illustrated using two applications.

Topics: Humans; Image Processing, Computer-Assisted; Lung; Observer Variation; Radionuclide Imaging; Respiration Disorders; Sodium Pertechnetate Tc 99m