Breast-density measurement using photon-counting spectral mammography.

PURPOSE: To evaluate a method for measuring breast density using photon-counting spectral mammography. Breast density is an indicator of breast cancer risk and diagnostic accuracy in mammography, and can be used as input to personalized screening, treatment monitoring and dose estimation. METHODS: The measurement method employs the spectral difference in x-ray attenuation between adipose and fibro-glandular tissue, and does not rely on any a priori information. The method was evaluated using phantom measurements on tissue-equivalent material (slabs and breast-shaped phantoms) and using clinical data from a screening population (n=1329). A state-of-the-art nonspectral method for breast-density assessment was used for benchmarking. RESULTS: The precision of the spectral method was estimated to be 1.5-1.8 percentage points (pp) breast density. Expected correlations were observed in the screening population for thickness versus breast density, dense volume, breast volume, and compression height. Densities ranged between 4.5% and 99.6%, and exhibited a skewed distribution with a mode of 12.5%, a median of 18.3%, and a mean of 23.7%. The precision of the nonspectral method was estimated to be 2.7-2.8 pp. The major uncertainty of the nonspectral method originated from the thickness estimate, and in particular thin/dense breasts posed problems compared to the spectral method. CONCLUSIONS: The spectral method yielded reasonable results in a screening population with a precision approximately two times that of the nonspectral method, which may improve or enable applications of breast-density measurement on an individual basis such as treatment monitoring and personalized screening.

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters.

PURPOSE: To establish operator-independent, fully automated planning of standard cardiac geometries and to determine the impact on interstudy reproducibility of cardiac functional parameters. MATERIALS AND METHODS: Cardiac MR imaging was done in 50 patients referred for left-ventricular function assessment. In all patients, first standard manual planning was performed followed by automatic planning (AUTO1) and repeat automatic planning (AUTO2) after repositioning the patient to investigate interstudy reproducibility. Cardiac functional parameters were assessed and cine scans were visually graded on a 4-point scale from nondiagnostic to excellent. RESULTS: Overall success rate of AUTO was 94% with good to excellent geometry planning in >94% of cine standard views. Comparing manual versus fully automated planning, a high agreement of cardiac functional parameters (Lin's concordance correlation coefficient, 0.91 to 0.99) with minimal percent bias (0.24 to 3.84%) was found. In addition, a high interstudy reproducibility of automatic planning was demonstrated (Lin's concordance correlation coefficient, 0.89 to 0.99; percent bias, 0.38 to 5.04%; precision, 3.46 to 9.09%). CONCLUSION: Fully automated planning of cardiac geometries could reliably be performed in patients showing a variety of cardiovascular pathologies. Standard cardiac geometries were precisely replicated and functional parameters were highly accurate.

Automated planning of scan geometries in spine MRI scans.

Consistency of MR scan planning is very important for diagnosis, especially in multi-site trials and follow-up studies, where disease progress or response to treatment is evaluated. Accurate manual scan planning is tedious and requires skillful operators. On the other hand, automated scan planning is difficult due to relatively low quality of survey images ("scouts") and strict processing time constraints. This paper presents a novel method for automated planning of MRI scans of the spine. Lumbar and cervical examinations are considered, although the proposed method is extendible to other types of spine examinations, such as thoracic or total spine imaging. The automated scan planning (ASP) system consists of an anatomy recognition part, which is able to automatically detect and label the spine anatomy in the scout scan, and a planning part, which performs scan geometry planning based on recognized anatomical landmarks. A validation study demonstrates the robustness of the proposed method and its feasibility for clinical use.