Background: In recent years experience has been accumulated in percutaneous radiofrequency ablation (RFA) of lung malignancies in nonsurgical patients. Objectives: In this study, we retrospectively evaluated a simultaneous diagnostic and therapeutic approach including CT-guided biopsy followed immediately by RFA of solitary malignant pulmonary lesions. Methods: CT-guided transthoracic core needle biopsy of solitary pulmonary lesions suspicious for malignancy was performed and histology was proven based on immediate frozen sections. RFA probes were placed into the pulmonary tumors under CT guidance and the ablation was performed subsequently. The procedure-related morbidity was analyzed. Follow-up included a CT scan and pulmonary function parameters. Results: A total of 33 CT-guided biopsies and subsequent RFA within a single procedure were performed. Morbidity of CT-guided biopsy included pulmonary hemorrhage (24%) and a mild pneumothorax (12%) without need for further interventions. The RFA procedure was not aggravated by the previous biopsy. The rate of pneumothorax requiring chest tube following RFA was 21%. Local tumor control was achieved in 77% with a median follow-up of 12 months. The morbidity of the CT-guided biopsy had no statistical impact on the local recurrence rate. Conclusions: The simultaneous diagnostic and therapeutic approach including CT-guided biopsy followed immediately by RFA of solitary malignant pulmonary lesions is a safe procedure. The potential of this combined approach is to avoid unnecessary therapies and to perform adequate therapies based on histology. Taking the local control rate into account, this approach should only be performed in those patients who are unable to undergo or who refuse surgery.

A new surface-based method that approximates the mammographic deformation due to breast compression without using a specific complex model needed for finite-element-based methods was developed and tested with favorable results. The simulated compression can serve as foundation for a point-to-line correspondence between 2D mammograms and 3D MR image data.

Purpose Iodine-containing contrast agent (CA) used in contrast-enhanced CT angiography (CTA) can pose a health risk for patients. A system that adjusts the frequently used standard CA dose for individual patients based on their clinical parameters can be useful. As basis the quality of the image contrast in CTA volumes has to be determined, especially to recognize excessive contrast induced by CA overdosing. However, a manual assessment with a ROI-based image contrast classification is a time-consuming step in everyday clinical practice. Methods We propose a method to automate the contrast measurement of aortic CTA volumes. The proposed algorithm is based on the mean HU values in selected ROIs that were automatically positioned in the CTA volume. First, an automatic localization algorithm determines the CTA image slices for certain ROIs followed by the localization of these ROIs. A rule-based classification using the mean HU values in the ROIs categorizes images with insufficient, optimal and excessive contrast. Results In 95.89% (70 out of 73 CTAs obtained with the ulrich medical CT motion contrast media injector) the algorithm chose the same image contrast class as the radiological expert. The critical case of missing an overdose did not occur with a positive predicative value of 100%. Conclusion The resulting system works well within our range of considered scan protocols detecting enhanced areas in CTA volumes. Our work automized an assessment for classifying CA-induced image contrast which reduces the time needed for medical practitioners to perform such an assessment manually.

Background Detection of breast cancer in women with high breast densities is a clinical challenge. Purpose To study the influence of different degrees of breast density on the sensitivity of contrast-enhanced breast magnetic resonance imaging (CE-BMRI) versus X-ray mammography (XRM). Material and Methods We performed an additional analysis of two large Phase III clinical trials (G1; G2) which included women with histologically proven breast cancers, called “index cancers.” Additional cancers were detected during image reading. We compared the sensitivity of CE-BMRI and XRM in women with different breast densities (ACR A→D; Version 5). For each study, six blinded readers evaluated the images. Results are given as the “Median Reader.” Results A total of 774 patients were included, 169 had additional cancers. While sensitivity of CE-BMRI for detecting all index cancers was independent of breast density (ACR A→D) (G1: 83%→83%; G2: 91%→91%) the sensitivity of XRM declined (ACR A→D) (G1: 79%→62%; G2: 82%→64%). Thus, the sensitivity difference between both imaging modalities in ACR A breasts of 3% (G1) and 9% (G2) increased to 21% (G1) and 26% (G2) in ACR D breasts. Sensitivity of CE-BMRI for detecting at least one additional cancer increased with increasing breast density (ACR A→D) (G1: 50%→73%, G2: 57%→81%). XRM’s sensitivity decreased (G1: 34%→20%) or remained stable (G2: 24%→25%). Conclusion CE-BMRI showed significantly higher sensitivity compared to XRM.

Mammography is the most commonly used imaging modality in breast cancer screening and diagnosis. The analysis of 2D mammographic images can be difficult due to the projective nature of the imaging technique and poor contrast between tumorous and healthy fibro-glandular tissue. Contrast-enhanced magnetic resonance imaging (MRI) can overcome these disadvantages by providing a 3D dataset of the breast. The detection of corresponding image structures is challenging due to large breast deformations during the image acquisition. We present a method for analyzing 2D/3D intra-individual correspondences between mammography and MRI datasets. Therefore, an ICP-based B-spline registration is used to approximate the breast deformation differences. The resulting deformed MR image is projected onto the 2D plane to enable a comparison with the 2D mammogram. A first evaluation based on six mammograms revealed an average accuracy of 4.87 mm. In contrast to previous FEM-based approaches, we propose a fast and easy to implement 3D/3D-registration, for simulating the mammographic breast compression. PURPOSEMammography is the breast imaging modality that is used most commonly in cancer screening and diagnosis. It provides a two-dimensional projection image of the compressed breast (between two plates), characterized by a fine resolution ([< 0.1× < 0.1]mm 2 ). Due to the projective nature of this imaging technique and a poor contrast between tumorous and healthy fibro-glandular tissue, the analysis of mammographic images can be challenging. Contrast-enhanced magnetic resonance imaging (MRI) can be used additionally 1 to overcome this disadvantages by providing a three-dimensional image of the female breast with functional information, which is commonly used for tumour staging (resolution ≈ [1 × 1 × 2-3]mm 3 ). In contrast to mammography, MRI is acquired without compression of the breast.To analyze the combined information provided by the two imaging modalities, the examining radiologist has to localize corresponding structures in the mammogram and the MR image. This task is challenging due to (1) different dimensionality and spatial resolution, (2) different contrasts between tissue types and (3) different deformation of the breast during acquisition. The huge breast deformation differences make a localization of corresponding structures especially challenging.In this paper, we present a method to enable an analysis of 2D/3D intra-individual correspondences between mammography and MRI. For this purpose, an ICP-based B-spline registration has been developed within the context of this work to approximate the breast deformation. To apply a surface based registration, the MR surface information is extracted from the 3D MR image and the mammographic surface information is approximated by reconstructing the mammographic volume. The resulting deformed MR image is projected onto the 2D plane to enable a comparison with the mammogram.Previously presented approaches for correspondence analysis between MRI and mammography have, for example, used ...

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The automatic segmentation of abdominal organs is a pre-requisite for many medical applications. Successful methods typically rely on prior knowledge about the to be segmented anatomy as it is for instance provided by means of active shape models (ASMs). Contrary to most previous ASM based methods, this work does not focus on individual organs. Instead, a more holistic approach that aims at exploiting inter-organ relationships to eventually segment a complex of organs is proposed. Accordingly, a flexible framework for automatic construction of multi-object ASMs is introduced, employed for coupled shape modeling, and used for co-segmentation of liver and spleen based on a new coupled shape/separate pose approach. Our first results indicate feasible segmentation accuracies, whereas pose decoupling leads to substantially better segmentation results and performs in average also slightly better than the standard single-object ASM approach.

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