Super Spatial Resolution (SSR) method for small animal SPECT imaging: A Monte Carlo study

Massari, R.; D’Elia, Annunziata; Soluri, Andrea; Soluri, A.

doi:10.1016/j.nima.2020.164584

Cited by 9 publications

(6 citation statements)

References 31 publications

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“…The experimental subject is placed on an animal bed that can move axially with respect to the rotation plane. The mechanics are designed to allow the necessary movements of the detectors for the application of the Super Spatial Resolution (SSR) technique which could be applied to improve the effective spatial resolution achievable, thus representing a key element for the study of very small brain structures 38 . To this purpose, multi-degree freedom carriages are used to obtain a fine alignment both linear and planar of the detectors.…”

mentioning

confidence: 99%

FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability

D’Elia

Schiavi

Manduca

et al. 2022

Sci Rep

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Autism Spectrum Disorder (ASD) is a pervasive neurodevelopmental disorder emerging in early life characterized by impairments in social interaction, poor verbal and non-verbal communication, and repetitive patterns of behaviors. Among the best-known genetic risk factors for ASD, there are mutations causing the loss of the Fragile X Messenger Ribonucleoprotein 1 (FMRP) leading to Fragile X syndrome (FXS), a common form of inherited intellectual disability and the leading monogenic cause of ASD. Being a pivotal regulator of motor activity, motivation, attention, and reward processing, dopaminergic neurotransmission has a key role in several neuropsychiatric disorders, including ASD. Fmr1Δexon 8 rats have been validated as a genetic model of ASD based on FMR1 deletion, and they are also a rat model of FXS. Here, we performed behavioral, biochemical and in vivo SPECT neuroimaging experiments to investigate whether Fmr1Δexon 8 rats display ASD-like repetitive behaviors associated with changes in striatal dopamine transporter (DAT) availability assessed through in vivo SPECT neuroimaging. At the behavioral level, Fmr1Δexon 8 rats displayed hyperactivity in the open field test in the absence of repetitive behaviors in the hole board test. However, these behavioral alterations were not associated with changes in striatal DAT availability as assessed by non-invasive in vivo SPECT and Western blot analyses.

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confidence: 99%

FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability

D’Elia

Schiavi

Manduca

et al. 2022

Sci Rep

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“…The normal distribution was used to randomly generate phantoms in batches. The number of point sources in the six triangular regions in the Derenzo-like phantoms was random and could be selected from the integer range (1)(2)(3)(4)(5)(6)(7)(8)(9). The intensity values of the background and point sources were random decimals and could be selected from the range (0.0-1.0).…”

Section: Digital Phantoms and Preprocessingmentioning

confidence: 99%

“…One of the most important strategies is to apply multi-image SR technology on the image domain (3). In the projection domain, an HR projection can be calculated from a series of LR projections containing different information, and then the image is reconstructed using the reconstruction method (4)(5)(6). However, the design of these methods is based on a perfect, error-free calibration system, and, in practice, errors in motion and registration may introduce uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study

Cheng¹,

Wen²,

Zhang³

et al. 2022

Ann Transl Med

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Background: Single-photon emission computed tomography (SPECT) is widely used in the early diagnosis of major diseases such as cardiovascular disease and cancer. High-resolution (HR) imaging requires HR projection data, which typically comes with high costs. This study aimed to obtain HR SPECT images based on a deep learning algorithm using low-resolution (LR) detectors.Methods: A super-resolution (SR) reconstruction network based on deep learning and transfer learning for parallel-beam SPECT was proposed. LR SPECT sinograms were converted into HR sinograms.Training data were designed and generated, including digital phantoms (128×128 pixels), HR sinograms (128×128 pixels), and LR sinograms (128×64 pixels). A series of random phantoms was first used for pretraining, and then the extended cardiac-torso (XCAT) phantom was used to fine-tune the parameters. The effectiveness of the method was evaluated using an unknown cardiac phantom. To simulate a wide range of noise levels, the total count levels of the projection were normalized to 1e7 (100%), 1e6 (10%), and 1e5 (5%). Finally, the training sets for different count levels were generated. Transfer learning was employed to accelerate the training. Results:The proposed network was validated on the simulation data sets using different Poisson noise levels. The quantitative values of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indicators of the reconstructed images were improved compared to those recorded using the benchmark methods. Using the proposed method, an image resolution comparable to that of images reconstructed from the HR projection could be achieved.Conclusions: Based on deep learning and transfer learning, an SR reconstruction network in the projection domain of the parallel-beam SPECT was developed. The simulation results under a wide range of noise levels evidenced the potential of the proposed network to improve SPECT resolution for LR detector scanners.

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Section: Introductionmentioning

confidence: 99%

“…Monte Carlo simulations in medical physics are widely used in the design and development of imaging systems such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), to monitor nuclear decay, fragmentation in the patient body or for range verification in particle therapy. For example, many works on emerging instrumentation for SPECT imaging systems (Auer et al 2018, Brown 2021, Massari et al 2020 require extensive and realistic Monte Carlo simulations to investigate and optimize the detection modules and novel geometrical configurations such as multi-head detectors. In abstract terms, such simulations create a mapping from a given source distribution inside the patient to a signal captured by the imaging device outside of the patient by transporting particles one-by-one through the objects present in the simulation.…”

Section: Introductionmentioning

confidence: 99%

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

Sarrut¹,

Etxebeste²,

Krah³

et al. 2021

Phys. Med. Biol.

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A method is proposed to model by a Generative Adversarial Network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for SPECT imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

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Super Spatial Resolution (SSR) method for small animal SPECT imaging: A Monte Carlo study

Cited by 9 publications

References 31 publications

FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability

FMR1 deletion in rats induces hyperactivity with no changes in striatal dopamine transporter availability

Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

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