Magnetic resonance imaging as a non‐invasive tool to assess gastric emptying in mice

Chavero-Pieres, Marta; Viola, Maria Francesca; Appeltans, Iris; Abdurahiman, Saeed; Gsell, Willy; Matteoli, Gianluca; Himmelreich, Uwe; Boeckxstaens, Guy E.

doi:10.1111/nmo.14490

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…SVM was used to classify the features extracted from the image and find the appropriate diagnosis [38, 39]. The diagnosis of the heart was done using features that enter into more than one of the hidden layers, which in turn gives an output and a line that is taken into account in choosing the accuracy of the result in the NN algorithm [40]. Tumour detection: Abnormal cell growth in one place is called a tumour.…”

Section: Related Workmentioning

confidence: 99%

Secure medical image transmission using deep neural network in e‐health applications

Alarood

Faheem

Al‐Khasawneh

et al. 2023

Healthcare Tech Letters

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Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Secure medical image transmission using deep neural network in e‐health applications

Alarood

Faheem

Al‐Khasawneh

et al. 2023

Healthcare Tech Letters

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Magnetic Resonance Imaging of Gastric Motility in Conscious Rats

Wang,

Alkaabi,

Cornett

et al. 2024

Preprint

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IntroductionGastrointestinal (GI) magnetic resonance imaging (MRI) can simultaneously capture gastric peristalsis, emptying, and intestinal filling and transit. Performing GI MRI with animals requires anesthesia, which complicates physiology and confounds interpretation and translation from animals to humans. This study aims to enable MRI in conscious rats, and for the first time, characterize GI motor functions in awake versus anesthetized conditions.MethodsWe acclimated rats to remain awake, still, and minimally stressed during MRI. We scanned 14 Sprague-Dawley rats in both awake and anesthetized conditions after voluntarily consuming a contrast-enhanced test meal.ResultsAwake rats remained physiologically stable during MRI, showed gastric emptying of 23.7±1.4% after 48 minutes, and exhibited strong peristaltic contractions propagating through the antrum with a velocity of 0.72±0.04 mm/s, a relative amplitude of 40.7±2.3%, and a frequency of 5.1±0.1 cycles per minute. In the anesthetized condition, gastric emptying was about half of that in the awake condition, likely due to the effect of anesthesia in halving the amplitudes of peristaltic contractions rather than their frequency (not significantly changed) or velocity. In awake rats, the intestine filled more quickly and propulsive contractions were more occlusive.ConclusionWe demonstrated the effective acquisition and analysis of GI MRI in awake rats. Awake rats show faster gastric emptying, stronger gastric contraction with a faster propagation speed, and more effective intestinal filling and transit, compared to anesthetized rats. Our protocol is expected to benefit future preclinical studies of GI physiology and pathophysiology.

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Magnetic resonance imaging as a non‐invasive tool to assess gastric emptying in mice

Chavero-Pieres

Viola

Appeltans

et al. 2022

Neurogastroenterology Motil

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Background: Methods to study gastric emptying in rodents are time consuming or terminal, preventing repetitive assessment in the same animal. Magnetic resonance imaging (MRI) is a non-invasive technique increasingly used to investigate gastrointestinal function devoid of these shortcomings. Here, we evaluated MRI to measure gastric emptying in control animals and in two different models of gastroparesis.Methods: Mice were scanned using a 9.4 Tesla MR scanner. Gastric volume was measured by delineating the stomach lumen area. Control mice were scanned every 30 min after ingestion of a 0.2 g meal and stomach volume was quantified. The ability of MRI to detect delayed gastric emptying was evaluated in models of morphine-induced gastroparesis and streptozotocin-induced diabetes.Key Results: Magnetic resonance imaging reproducibly detected increased gastric volume following ingestion of a standard meal and progressively decreased with a half emptying time of 59 ± 5 min. Morphine significantly increased gastric volume measured at t = 120 min (saline: 20 ± 2 vs morphine: 34 ± 5 mm 3 ; n = 8-10; p < 0.001) and increased half emptying time using the breath test (saline: 85 ± 22 vs morphine: 161 ± 46 min; n = 10; p < 0.001). In diabetic mice, gastric volume assessed by MRI at t = 60 min (control: 23 ± 2 mm 3 ; n = 14 vs diabetic: 26 ± 5 mm 3 ; n = 18; p = 0.014) but not at t = 120 min (control: 21 ± 3 mm 3 ; n = 13 vs diabetic: 18 ± 5 mm 3 ; n = 18; p = 0.115) was significantly increased compared to nondiabetic mice. Conclusions and Inferences:Our data indicate that MRI is a reliable and reproducible tool to assess gastric emptying in mice and represents a useful technique to study gastroparesis in disease models or for evaluation of pharmacological compounds.

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Magnetic resonance imaging as a non‐invasive tool to assess gastric emptying in mice

Cited by 3 publications

References 33 publications

Secure medical image transmission using deep neural network in e‐health applications

Secure medical image transmission using deep neural network in e‐health applications

Magnetic Resonance Imaging of Gastric Motility in Conscious Rats

Magnetic resonance imaging as a non‐invasive tool to assess gastric emptying in mice

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