Charles Guan,Alexander P Rockhill,Masashi Sode et al.
Charles Guan et al.
Purpose: Volumetric ultrafast ultrasound produces massive datasets with high frame rates, dense reconstruction grids, and large channel counts. Beamforming computational demands limit research throughput and prevent real-...
In search of truth: evaluating concordance of AI-based anatomy segmentation models [0.03%]
寻找真相：评估基于人工智能的解剖分割模型的一致性
Lena Giebeler,Deepa Krishnaswamy,David Clunie et al.
Lena Giebeler et al.
Purpose: Artificial intelligence based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar functionality models raises the challenge of evaluating t...
Challenging Hounsfield Unit cutoffs: spectral thresholding for synthetic coronary plaque phantoms on photon-counting CT [0.03%]
挑战霍索恩斯单位截止值：光子计数CT上合成冠状动脉斑块模型的谱阈值法
Florian Goldmann,Michael Wels,Thomas Allmendinger et al.
Florian Goldmann et al.
Purpose: Assess whether photon-counting computed tomography (PCCT) improves discrimination of vulnerable coronary soft-plaque components by extending one-dimensional Hounsfield Unit (HU) thresholding to a simple, interpre...
Accuracy and reliability of artery-vein differentiation in small-field macular OCT angiography [0.03%]
小视场黄斑区光学相干断层血管造影的动脉静脉鉴别准确性及可靠性
Haneen Alfauri,Tugce Ilayda Turer,Cyriac Manjaly et al.
Haneen Alfauri et al.
Purpose: Accurate artery-vein (AV) differentiation in small-field macular optical coherence tomography angiography (OCTA) remains challenging due to a lack of standardized guidelines. We propose and validate criteria for ...
Synthesizing breast cancer ultrasound images from healthy samples using latent diffusion models [0.03%]
利用潜在扩散模型从健康样本合成乳腺癌超声图像
Yannuo Wen,Kathleen M Curran,Xinzhu Wang et al.
Yannuo Wen et al.
Purpose: Breast ultrasound is widely used for cancer screening, but data scarcity and annotation challenges hinder deep learning adoption. Synthetic image generation offers a promising solution to enhance training dataset...
Estimation of controlled attenuation parameter-based liver steatosis via raw ultrasound data from handheld devices [0.03%]
基于手持设备原始超声数据的受控衰减参数肝脂肪变估计
Jakob Schäfer,Charlotte Herzog,Tina Gabriel et al.
Jakob Schäfer et al.
Purpose: Assessment of liver steatosis is primarily performed through visual evaluation during ultrasound examinations. A more objective approach relies on quantifying ultrasound attenuation, typically using devices such ...
Comparison of 2D and 3D carotid plaque analysis and longitudinal in vivo ultrasound registration using 3D histology [0.03%]
基于3D组织学的颈动脉斑块的二维与三维超声影像分析及长期体内超声影像注册比较研究
Yurim Lee,Maxwell J Kiernan,Carol C Mitchell et al.
Yurim Lee et al.
Purpose: Characterizing carotid plaque specimens based on two-dimensional (2D) "representative" histology sections is considered standard practice in clinics. In comparison, three-dimensional (3D) histology has the potent...
ECLARE: efficient cross-planar learning for anisotropic resolution enhancement [0.03%]
高效的平面外学习增强各向异性分辨率技术(ECLARE)
Samuel W Remedios,Shuwen Wei,Shuo Han et al.
Samuel W Remedios et al.
Purpose: In clinical imaging, magnetic resonance (MR) image volumes are often acquired as stacks of 2D slices with decreased scan times, improved signal-to-noise ratio, and image contrasts unique to 2D MR pulse sequences....
Rolling convolution filters for lightweight neural networks in medical image analysis [0.03%]
用于医学图像分析的轻量级神经网络的滚动卷积滤波器
Naveen Paluru,Mehak Arora,Phaneendra K Yalavarthy
Naveen Paluru
Purpose: To introduce a filter design element called rolling convolution filters for developing lightweight convolutional neural networks (CNNs) in medical image analysis, aiming to reduce model complexity and memory foot...
Bennett A Landman
Bennett A Landman
JMI Editor-in-Chief Bennett Landman offers guidance to help authors achieve higher impact, clearer assessment of contributions, and more useful and direct reviews from the peer review community. ...