38 research outputs found
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Generative adversarial network-created brain SPECTs of cerebral ischemia are indistinguishable to scans from real patients
Deep convolutional generative adversarial networks (GAN) allow for creating images from existing databases. We applied a modified light-weight GAN (FastGAN) algorithm to cerebral blood flow SPECTs and aimed to evaluate whether this technology can generate created images close to real patients. Investigating three anatomical levels (cerebellum, CER; basal ganglia, BG; cortex, COR), 551 normal (248 CER, 174 BG, 129 COR) and 387 pathological brain SPECTs using N-isopropyl p-I-123-iodoamphetamine (I-123-IMP) were included. For the latter scans, cerebral ischemic disease comprised 291 uni- (66 CER, 116 BG, 109 COR) and 96 bilateral defect patterns (44 BG, 52 COR). Our model was trained using a three-compartment anatomical input (dataset 'A'; including CER, BG, and COR), while for dataset 'B', only one anatomical region (COR) was included. Quantitative analyses provided mean counts (MC) and left/right (LR) hemisphere ratios, which were then compared to quantification from real images. For MC, 'B' was significantly different for normal and bilateral defect patterns (P = 0.08) reached significance relative to images of real patients. With a minimum of only three anatomical compartments serving as stimuli, created cerebral SPECTs are indistinguishable to images from real patients. The applied FastGAN algorithm may allow to provide sufficient scan numbers in various clinical scenarios, e.g., for "data-hungry" deep learning technologies or in the context of orphan diseases
Evaluation of both perfusion and atrophy in multiple system atrophy of the cerebellar type using brain SPECT alone
BACKGROUND: Partial volume effects in atrophied areas should be taken into account when interpreting brain perfusion single photon emission computed tomography (SPECT) images of neurodegenerative diseases. To evaluate both perfusion and atrophy using brain SPECT alone, we developed a new technique applying tensor-based morphometry (TBM) to SPECT. METHODS: After linear spatial normalization of brain perfusion SPECT using (99m)Tc-ethyl cysteinate dimer ((99m)Tc-ECD) to a Talairach space, high-dimension-warping was done using an original (99m)Tc-ECD template. Contraction map images calculated from Jacobian determinants and spatially normalized SPECT images using this high-dimension-warping were compared using statistical parametric mapping (SPM2) between two groups of 16 multiple system atrophy of the cerebellar type (MSA-C) patients and 73 age-matched normal controls. This comparison was also performed in conventionally warped SPECT images. RESULTS: SPM2 demonstrated statistically significant contraction indicating local atrophy and decreased perfusion in the whole cerebellum and pons of MSA-C patients as compared to normal controls. Higher significance for decreased perfusion in these areas was obtained in high-dimension-warping than in conventional warping, possibly due to sufficient spatial normalization to a (99m)Tc-ECD template in high-dimensional warping of severely atrophied cerebellum and pons. In the present high-dimension-warping, modification of tracer activity remained within 3% of the original tracer distribution. CONCLUSIONS: The present new technique applying TBM to brain SPECT provides information on both perfusion and atrophy at the same time thereby enhancing the role of brain perfusion SPEC