Mammography Initial Training

Jul 24th, 2019Jul 28th, 2019

Houston, TX

US

https://store.aheconline.com/index.php?route=product/product&path=59_78&product_id=4317

Phone:800-239-1361

This class will provide the technologist with all of the required components of training as outlined by MQSA.

CT-based deep learning identifies patients at further lung cancer risk

April 16, 2024

My BreastAI Suite by GE HealthCare

My BreastAI Suite by GE HealthCare

April 2, 2024

Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.

Deep-learning model determines breast tumor staging

April 16, 2024

F-18 FDG and Ga-68 FAPI-46 images of a 51-year-old woman with adenocarcinoma with lepidic growth pattern in right upper lobe. (A) Maximum-intensity-projection PET images. The lesion had F-18 FDG uptake below blood pool level but was strongly Ga-68 FAPI-46-positive. CT-guided biopsy led to pathologic diagnosis of adenocarcinoma, and the patient was treated by stereotactic body radiation therapy because of functional inoperability. Image courtesy of the Journal of Nuclear Medicine.

FAPI-PET shows promise in patients with lung cancer

April 15, 2024

CT-based deep learning identifies patients at further lung cancer risk

By Kate Madden Yee

A CT-based deep learning model can identify individuals with clinical stage IA non-small cell lung cancer (NSCLC) at high risk of further disease, researchers have found.

April 16, 2024

ChatGPT-4 matches radiologists in flagging errors on reports

ChatGPT-4 matched radiologists when tasked with detecting errors in imaging reports, with the study suggesting the technology may be useful for improving reporting accuracy.

April 16, 2024

Deep-learning model determines breast tumor staging

By Amerigo Allegretto

A deep-learning model based on mammographic images can determine tumor staging and sentinel lymph node metastasis in breast cancer patients.

April 16, 2024

Researchers explain the data preparation and structure of a proposed CNN-based deep learning model using mammography features to determine breast tumor staging. The image is available for use under a Creative Commons license: CC BY-NC-ND 4.0 DEED Attribution-NonCommercial-NoDerivs 4.0 International.

FAPI-PET shows promise in patients with lung cancer

FAPI-PET imaging could emerge as a new tool for assessing patients with single pulmonary tumors, especially in those with negative results on other scans.

April 15, 2024

F-18 FDG and Ga-68 FAPI-46 images of a 51-year-old woman with adenocarcinoma with lepidic growth pattern in right upper lobe. (A) Maximum-intensity-projection PET images. The lesion had F-18 FDG uptake below blood pool level but was strongly Ga-68 FAPI-46-positive. CT-guided biopsy led to pathologic diagnosis of adenocarcinoma, and the patient was treated by stereotactic body radiation therapy because of functional inoperability. Image courtesy of the Journal of Nuclear Medicine.

IMV: Nuclear medicine procedure volumes decrease

By Davin Korstjens

Total nuclear medicine patient study volumes decreased between 2021 and 2023, according to a newly published IMV 2024 Nuclear Medicine Market Outlook Report.

April 15, 2024

Can brain MRI for emergency imaging of dizziness be more effective?

By Kate Madden Yee

Predictors for acute conditions such as stroke could make brain MRI more effective in patients who present in the emergency department with dizziness or vertigo, according to recent research.

April 15, 2024

NIH: Occupational radiation dose to RTs needs careful monitoring

By Kate Madden Yee

Occupational doses to radiologic technologists imparted by assistance with fluoroscopically-guided interventional procedures are generally low, but they do need careful monitoring, a team of National Institute of Health (NIH) researchers has found.

April 12, 2024

Vision transformer AI model boosts PET/CT imaging of cancer

A vision transformer AI model outperformed convolutional neural networks when classifying tumors on PET/CT images as benign or malignant.

April 12, 2024

Predictions and Grad-CAMs of ViT-based models on sample PET/CT, PET, and CT test images from the “positive” class. ViT was fine-tuned using training data for each modality. The bounding boxes depicted in the figure indicate malignant lesions. The top row of the Grad-CAMs shows important areas for “positive” predictions, and the bottom row shows areas for “negative” predictions. Image courtesy of Scientific Reports.

UltraCon: Expert talks ultrasound's role in imaging dense breasts

By Amerigo Allegretto

Kibo Nam, PhD, from Thomas Jefferson University spoke with AuntMinnie.com about the current state of ultrasound in imaging dense breasts.

April 12, 2024

Negative axillary ultrasound can lead to breast biopsy omission

By Amerigo Allegretto

Many women with early-stage HR+HER2- breast cancer and a negative presurgical axillary ultrasound can skip sentinel lymph node biopsy.

April 12, 2024

Andreas Giannakou, MD, from Brigham & Women’s Hospital and Harvard Medical School presents research highlighting how many women with early-stage HR+HER2- breast cancer and a negative presurgical axillary ultrasound can skip sentinel lymph node biopsy.

Many young women with breast cancer symptoms delay care

By Amerigo Allegretto

Many young women who have breast cancer symptoms delay timely screening and care.

April 12, 2024

Katherine Fleshner, MD, from the University of Calgary, presents research exploring trends in young women with breast cancer symptoms delaying care, including for screening mammography.

UltraCon: Work-related MSK disorders tied to sonographer burnout

By Amerigo Allegretto

Work-related musculoskeletal (MSK) disorders are tied to work-related burnout among sonographers, according to research highlighted April 10 at UltraCon.

April 11, 2024

Jennifer Bagley from the University of Oklahoma Health Sciences Center presents results at UltraCon suggesting that work-related musculoskeletal disorders are tied to burnout among sonographers.

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