Abstract
The objective and quantitative analysis of longitudinal single photon emission computed tomography (SPECT) images are significant for the treatment monitoring of brain disorders. Therefore, a computer aided analysis (CAA) method is introduced to extract a change-rate map (CRM) as a parametric image for quantifying the changes of regional cerebral blood flow (rCBF) in longitudinal SPECT brain images. The performances of the CAA-CRM approach in treatment monitoring are evaluated by the computer simulations and clinical applications. The results of computer simulations show that the derived CRMs have high similarities with their ground truths when the lesion size is larger than system spatial resolution and the change rate is higher than 20%. In clinical applications, the CAA-CRM approach is used to assess the treatment of 50 patients with brain ischemia. The results demonstrate that CAA-CRM approach has a 93.4% accuracy of recovered region's localization. Moreover, the quantitative indexes of recovered regions derived from CRM are all significantly different among the groups and highly correlated with the experienced clinical diagnosis. In conclusion, the proposed CAA-CRM approach provides a convenient solution to generate a parametric image and derive the quantitative indexes from the longitudinal SPECT brain images for treatment monitoring.
Highlights
Single photon emission computed tomography (SPECT) with 99mTc-ethyl cysteine dimer (99mTc-ECD) has been widely used to evaluate many types of cerebrovascular diseases and brain disorders by measuring regional cerebral blood flow [1,2,3,4,5,6]
The properties of change-rate map (CRM) in treatment monitoring are quantified by image quality indexes, including the normalized absolute error (NAE), peak signal to noise ratio (PSNR), and normalized cross-correlation (NCC)
17 of 50 patients are reported as severe recovery in brain perfusion after internal carotid artery (ICA) stenting, while 22 patients are scaled as moderate recovery
Summary
Single photon emission computed tomography (SPECT) with 99mTc-ethyl cysteine dimer (99mTc-ECD) has been widely used to evaluate many types of cerebrovascular diseases and brain disorders by measuring regional cerebral blood flow (rCBF) [1,2,3,4,5,6]. Longitudinal 99mTc-ECD SPECT brain imaging can be adopted to monitor the changes of rCBF to support the treatment plan [7, 8]. The most common approach for interpreting SPECT brain images is visual inspection in daily clinical practice. The relevant structural images, such as CT or MRI images, are preferred for the visual interpretation together with SPECT images. The baseline and follow-up SPECT brain images should be parallelly interpreted under the same condition to figure out the differences. The image processing technology can bring solutions to the visual inspection problems and explore the hidden information in the images
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