AIMC Topic: Signal-To-Noise Ratio

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Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.

Scientific reports
Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), ofte...

In-silico CT simulations of deep learning generated heterogeneous phantoms.

Biomedical physics & engineering express
Current virtual imaging phantoms primarily emphasize geometric accuracy of anatomical structures. However, to enhance realism, it is also important to incorporate intra-organ detail. Because biological tissues are heterogeneous in composition, virtua...

Limited-angle SPECT image reconstruction using deep image prior.

Physics in medicine and biology
. In single-photon emission computed tomography (SPECT) image reconstruction, limited-angle conditions lead to a loss of frequency components, which distort the reconstructed tomographic image along directions corresponding to the non-collected proje...

Deep learning-assisted Raman spectroscopy for rapid lactic acid bacteria identification at the colony level.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
To achieve rapid and accurate identification at the colony level and improve the efficiency of colony selection, we proposed an adaptive colony Raman acquisition method based on signal-to-noise ratio screening (ACRA-SNR). This method enables in situ ...

De-speckling of medical ultrasound image using metric-optimized knowledge distillation.

Scientific reports
Ultrasound imaging provides real-time views of internal organs, which are essential for accurate diagnosis and treatment. However, speckle noise, caused by wave interactions with tissues, creates a grainy texture that hides crucial details. This nois...

Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.

BMC medical imaging
BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, ...

Accelerating brain T2-weighted imaging using artificial intelligence-assisted compressed sensing combined with deep learning-based reconstruction: a feasibility study at 5.0T MRI.

BMC medical imaging
BACKGROUND: T2-weighted imaging (T2WI), renowned for its sensitivity to edema and lesions, faces clinical limitations due to prolonged scanning time, increasing patient discomfort, and motion artifacts. The individual applications of artificial intel...

A multi-domain collaborative denoising bearing fault diagnosis model based on dynamic inter-domain attention mechanism and noise-aware loss function.

PloS one
Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of ...

Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU.

PloS one
Shortwave communication plays a vital role in disaster relief and remote communications due to its long-range capabilities and resilience to interference. However, challenges such as multipath propagation, frequency-selective fading, and low signal-t...

Generative deep-learning-model based contrast enhancement for digital subtraction angiography using a text-conditioned image-to-image model.

Computers in biology and medicine
BACKGROUND: Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accident...