Influence of Attenuation Correction by Brain Perfusion SPECT/CT Using a Simulated Abnormal Bone Structure: Comparison Between Chang and CT Methods

Evaluation of attenuation correction method for head holder in brain perfusion single-photon emission computed tomography

Head holder attenuation affects brain perfusion single-photon emission computed tomography (SPECT) image quality. Here, we proposed a head holder-attenuation correction (AC) method using attenuation coefficient maps calculated by Chang's method from CT images. Then, we evaluated the effectiveness of the head holder-AC method by numerical phantom and clinical cerebral perfusion SPECT studies. In the numerical phantom, the posterior counts were 10.7% lower than the anterior counts without head holder-AC method. However, by performing head holder-AC, the posterior count recovered by approximately 6.8%, approaching the true value. In the clinical study, the normalized count ratio was significantly increased by performing the head holder-AC method in the posterior-middle cerebral artery, posterior cerebral artery and cerebellum regions. There were no significant increases in other regions. The head holder-AC method can correct the counts attenuated by the head holder.

Attenuation correction using deep learning for brain perfusion SPECT images

OBJECTIVE

Non-uniform attenuation correction using computed tomography (CT) improves the image quality and quantification of single-photon emission computed tomography (SPECT). However, it is not widely used because it requires a SPECT/CT scanner. This study constructs a convolutional neural network (CNN) to generate attenuation-corrected SPECT images directly from non-attenuation-corrected SPECT images.

METHODS

We constructed an auto-encoder (AE) using a CNN to correct the attenuation in brain perfusion SPECT images. SPECT image datasets of 270 (44,528 slices including augmentation), 60 (5002 slices), and 30 (2558 slices) cases were used for training, validation, and testing, respectively. The acquired projection data were reconstructed in three patterns: uniform attenuation correction using Chang's method (Chang-AC), non-uniform attenuation correction using CT (CT-AC), and no attenuation correction (No-AC). The AE learned an end-to-end mapping between the No-AC and CT-AC images. The No-AC images in the test dataset were loaded into the trained AE, which generated images simulating the CT-AC images as output. The generated SPECT images were employed as attenuation-corrected images using the AE (AE-AC). The accuracy of the AE-AC images was evaluated in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity metric (SSIM). The intensities of the AE-AC and CT-AC images were compared by voxel-by-voxel and region-by-region analysis.

RESULTS

The PSNRs of the AE-AC and Chang-AC images, compared using CT-AC images, were 62.2, and 57.9, and their SSIM values were 0.9995 and 0.9985, respectively. The AE-AC and CT-AC images were visually and statistically in good agreement.

CONCLUSIONS

The proposed AE-AC method yields highly accurate attenuation-corrected brain perfusion SPECT images.