Purpose: To determine whether dynamic MR mammography is possible on midfield systems without loss of diagnostic sensitivity when compared to the standard highfield technique.

Materials And Methods: 42 consecutive patients were examined twice: Once using the standard dynamic 2D gradient echo technique at 1.5 T; a second examination was performed on a 0.5 T system. For the midfield examinations a 3D sequence with optimized T1 contrast was used to compensate for the shorter T1 relaxation times at 0.5 T. Subtraction images were calculated to improve detectability of enhancing lesions.

Results: Image quality was comparable on both systems. Mean enhancement of lesions was higher at 0.5 T/3D as compared to 1.5 T/2D (161% versus 112%). In malignant lesions, enhancement at 0.5 T/3D surpassed that at 1.5 T/2D in 88% of cases; average maximum signal intensity increase of cancers was significantly higher at 0.5 T/3D as compared to 1.5 T/2D (183% versus 108% relative to baseline). One satellite lesion of a recurrent carcinoma was detected on the 0.5 T/3D images only.

Conclusion: A 3D gradient echo pulse sequence can be used to compensate for the T1 shortening effect of the lower field strength. With a 3D sequence, sensitivity of MRM at 0.5 T is even superior to that of the standard 2D highfield technique.

Download full-text PDF

Source
http://dx.doi.org/10.1055/s-2007-1015904DOI Listing

Publication Analysis

Top Keywords

image quality
8
standard highfield
8
gradient echo
8
higher t/3d
8
t/3d compared
8
compared t/2d
8
[mr mammography
4
mammography tesla
4
tesla comparison
4
comparison image
4

Similar Publications

Purpose: To assess longitudinal changes in optical quality across the periphery (horizontal meridian, 60°) in young children who are at high (HR) or low risk (LR) of developing myopia, as well as a small subgroup of children who developed myopia over a 3-year time frame.

Methods: Aberrations were measured every 6 months in 92 children with functional emmetropia at baseline. Children were classified into HR or LR based on baseline refractive error and parental myopia.

View Article and Find Full Text PDF

Purpose: Wearable electronic low vision enhancement systems (wEVES) improve visual function but are not widely adopted by people with vision impairment. Here, qualitative research methods were used to investigate the usefulness of wEVES for people with age-related macular degeneration (AMD) after an extended home trial.

Methods: Following a 12-week non-masked randomised crossover trial, semi-structured interviews were completed with 34 participants with AMD, 64.

View Article and Find Full Text PDF

Background: Iliac vein compression syndrome (IVCS) impedes venous blood return from the lower extremities due to iliac vein compression, manifesting as leg swelling, varicose veins, and thrombosis. These symptoms significantly degrade quality of life. Although iliac vein stenting provides symptomatic relief, the recovery process is protracted and fraught with challenges such as in-stent restenosis and psychological distress.

View Article and Find Full Text PDF

Medical image annotation is scarce and costly. Few-shot segmentation has been widely used in medical image from only a few annotated examples. However, its research on lesion segmentation for lung diseases is still limited, especially for pulmonary aspergillosis.

View Article and Find Full Text PDF

Regional Image Quality Scoring for 2-D Echocardiography Using Deep Learning.

Ultrasound Med Biol

January 2025

Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Health Research, SINTEF, Trondheim, Norway.

Objective: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.

Methods: Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!