Hanno Heyke Homann

A specific absorption rate prediction concept for parallel transmission MR

The specific absorption rate (SAR) is a limiting factor in high‐field MR. SAR estimation is typically performed by numerical simulations using generic human body models. However, SAR concepts for single‐channel radiofrequency transmission cannot be directly applied to multichannel systems. In this study, a novel and comprehensive SAR prediction concept for parallel radiofrequency transmission MRI is presented, based on precalculated magnetic and electric fields obtained from electromagnetic simulations of numerical body models. The application of so‐called Q‐matrices and further computational optimizations allow for a real‐time estimation of the SAR prior to scanning. This SAR estimation method was fully integrated into an eight‐channel whole body MRI system, and it facilitated the selection of different body models and body positions. Experimental validation of the global SAR in phantoms demonstrated a good qualitative and quantitative agreement with the predictions. An initial in vivo validation showed good qualitative agreement between simulated and measured amplitude of (excitation) radiofrequency field. The feasibility and practicability of this SAR prediction concept was shown paving the way for safe parallel radiofrequency transmission in high‐field MR. Magn Reson Med, 2012.

Computation of synthetic mammograms with an edge-weighting algorithm

The promising increase in cancer detection rates1, 2 makes digital breast tomosynthesis (DBT) an interesting alternative to full-field digital mammography (FFDM) in breast cancer screening. However, this benefit comes at the cost of an increased average glandular dose in a combined DBT plus FFDM acquisition protocol. Synthetic mammograms, which are computed from the reconstructed tomosynthesis volume data, have demonstrated to be an alternative to a regular FFDM exposure in a DBT plus synthetic 2D reading mode.3 Besides weighted averaging and modified maximum intensity projection (MIP) methods,4, 5 the integration of CAD techniques for computing a weighting function in the forward projection step of the synthetic mammogram generation has been recently proposed.6, 7 In this work, a novel and computationally efficient method is presented based on an edge-retaining algorithm, which directly computes the weighting function by an edge-detection filter.

Spectral lesion characterization on a photon-counting mammography system

Spectral X-ray imaging allows to differentiate between two given tissue types, provided their spectral absorption characteristics differ measurably. In mammography, this method is used clinically to determine a decomposition of the breast into adipose and glandular tissue compartments, from which the glandular tissue fraction and, hence, the volumetric breast density (VBD) can be computed. Another potential application of this technique is the characterization of lesions by spectral mammography. In particular, round lesions are relatively easily detected by experienced radiologists, but are often difficult to characterize. Here, a method is described that aims at discriminating cystic from solid lesions directly on a spectral mammogram, obtained with a calibrated spectral mammography system and using a hypothesis-testing algorithm based on a maximum likelihood approach. The method includes a parametric model describing the lesion shape, compression height variations and breast composition. With the maximum likelihood algorithm, the model parameters are estimated separately under the cyst and solid hypothesis. The resulting ratio of the maximum likelihood values is used for the final tissue characterization. Initial results using simulations and phantom measurements are presented.

Dose-Saving Potential of Linear- and Non-Linear Energy Weighting in Photon-Counting Spectral Mammography

Energy weighting techniques are known to improve the contrast-to-noise (CNR) ratio in energy-sensitive, x-ray photon detection, in particular in the absence of scattered radiation. In spite of the rather moderate reported improvements in CNR, typically ranging between 5-10%, it is of high relevance to quantify the potential for saving radiation dose in a mammography screening environment. In this paper we experimentally investigate the possible improvements to be obtained by energy-weighting of data acquired with a Philips MicroDose SI mammography system. We compare three schemes to combine the raw data consisting of counts registered in the low- and high-energy bins, respectively: conventional summation, linear weighting and non-linear weighting of the two energy bins. Measurements on a dedicated phantom were analyzed to quantify the potential for reduction of patient dose of linear and non-linear energy weighting. By averaging improvements of CNR achieved over several pairs of regions-of-interest (ROI) we report a potential to reduce the patient dose by 7% for linear- and 9% for non-linear energy weighting, in good agreement with expectation.