AIMC Topic: Image Processing, Computer-Assisted

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Deep learning corrects artifacts in RASER MRI profiles.

Magnetic resonance imaging
A newly developed magnetic resonance imaging (MRI) approach is based on "Radiowave amplification by the stimulated emission of radiation" (RASER). RASER MRI potentially allows for higher resolution, is inherently background-free, and does not require...

Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics.

Medical physics
BACKGROUND: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report.

Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

Medical physics
BACKGROUND: Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis an...

Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.

European radiology
OBJECTIVES: To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm.

Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.

Radiological physics and technology
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN)...

FusionOC: Research on optimal control method for infrared and visible light image fusion.

Neural networks : the official journal of the International Neural Network Society
Infrared and visible light image fusion can solve the limitations of single-type visual sensors and can boost the target detection performance. However, since the traditional fusion strategy lacks the controllability and feedback mechanism, the fusio...

Lung nodule classification using radiomics model trained on degraded SDCT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) screening has shown promise in reducing lung cancer mortality; however, it suffers from high false positive rates and a scarcity of available annotated datasets. To overcome these challeng...

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: For the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured us...

Deep-Learning-Based Segmentation of Cells and Analysis (DL-SCAN).

Biomolecules
With the recent surge in the development of highly selective probes, fluorescence microscopy has become one of the most widely used approaches to studying cellular properties and signaling in living cells and tissues. Traditionally, microscopy image ...

A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.

PloS one
In this paper, we propose a dynamic authorizable ciphertext image retrieval scheme based on secure neural network inference that effectively enhances the security of image retrieval while preserving privacy. To ensure the privacy of the original imag...