A survey on deep learning in medical image reconstruction
E Ahishakiye, M Bastiaan Van Gijzen… - Intelligent …, 2021 - mednexus.org
Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …
at minimal cost and risk to the patients. Deep learning and its applications in medical …
Deep learning‐based image reconstruction for different medical imaging modalities
Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT)
is a mathematical process that generates images at many different angles around the …
is a mathematical process that generates images at many different angles around the …
Multi-domain integrative Swin transformer network for sparse-view tomographic reconstruction
Decreasing projection views to a lower X-ray radiation dose usually leads to severe streak
artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin …
artifacts. To improve image quality from sparse-view data, a multi-domain integrative Swin …
Iterative residual optimization network for limited-angle tomographic reconstruction
Limited-angle tomographic reconstruction is one of the typical ill-posed inverse problems,
leading to edge divergence with degraded image quality. Recently, deep learning has been …
leading to edge divergence with degraded image quality. Recently, deep learning has been …
The use of deep learning methods in low-dose computed tomography image reconstruction: a systematic review
Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
reconstruction (IR), which have been utilised widely in the image reconstruction process of …
A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery
J Minnema, A Ernst, M van Eijnatten… - Dentomaxillofacial …, 2022 - academic.oup.com
Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical
interventions and has therefore become an increasingly popular treatment modality in …
interventions and has therefore become an increasingly popular treatment modality in …
A super-resolution guided network for improving automated thyroid nodule segmentation
Background and Objective: A thyroid nodule is an abnormal lump that grows in the thyroid
gland, which is the early symptom of thyroid cancer. In order to diagnose and treat thyroid …
gland, which is the early symptom of thyroid cancer. In order to diagnose and treat thyroid …
Four-dimensional cone beam ct imaging using a single routine scan via deep learning
A novel method is proposed to obtain four-dimensional (4D) cone-beam computed
tomography (CBCT) images from a routine scan in patients with upper abdominal cancer …
tomography (CBCT) images from a routine scan in patients with upper abdominal cancer …
Machine learning assists in increasing the time resolution of X-ray computed tomography applied to mineral precipitation in porous media
D Lee, F Weinhardt, J Hommel, J Piotrowski, H Class… - Scientific Reports, 2023 - nature.com
Many subsurface engineering technologies or natural processes cause porous medium
properties, such as porosity or permeability, to evolve in time. Studying and understanding …
properties, such as porosity or permeability, to evolve in time. Studying and understanding …
Single image super-resolution approaches in medical images based-deep learning: a survey
Abstract Medical image Super-Resolution (SR) reconstruction refers to the process of
regenerating a High-Resolution (HR) image from a degraded Low-Resolution (LR) image or …
regenerating a High-Resolution (HR) image from a degraded Low-Resolution (LR) image or …