The delineation and diagnosis of non-mass-like lesions, most notably DCIS (ductal carcinoma in situ), is among the most challenging tasks in breast MRI reading. Even for human observers, DCIS is not always easy to diferentiate from patterns of active parenchymal enhancement or from benign alterations of breast tissue. In this light, it is no surprise that CADe/CADx approaches often completely fail to classify DCIS. Of the several approaches that have tried to devise such computer aid, none achieve performances similar to mass detection and classification in terms of sensitivity and specificity. In our contribution, we show a novel approach to combine a newly proposed metric of anatomical breast symmetry calculated on subtraction images of dynamic contrast-enhanced (DCE) breast MRI, descriptive kinetic parameters, and lesion candidate morphology to achieve performances comparable to computer-aided methods used for masses. We have based the development of the method on DCE MRI data of 18 DCIS cases with hand-annotated lesions, complemented by DCE-MRI data of nine normal cases. We propose a novel metric to quantify the symmetry of contralateral breasts and derive a strong indicator for potentially malignant changes from this metric. Also, we propose a novel metric for the orientation of a finding towards a fix point (the nipple). Our combined scheme then achieves a sensitivity of 89% with a specificity of 78%, matching CAD results for breast MRI on masses. The processing pipeline is intended to run on a CAD server, hence we designed all processing to be automated and free of per-case parameters. We expect that the detection results of our proposed non-mass aimed algorithm will complement other CAD algorithms, or ideally be joined with them in a voting scheme.
Aim: This study aims to determine the effectiveness of a novel image-processing algorithm for multi-scale enhancement
of chest radiographs to improve detection and localization of real pulmonary nodules. Background: Our wavelet-based
enhancement method interactively adjusts the contrast of medical images extracting the spatial frequency components at different scales, followed by a weighting procedure. This study aims to explore the usefulness of this novel procedure for chest image reporting. Method: Sixteen radiologists viewed 50 PA chest radiographs in order to localize pulmonary
nodules. The databank contains 25 normal and 25 abnormal images, with multi-nodule cases. Subjects were allowed to mark unlimited number of locations followed by ranking confidence of nodule presence according to a 5-level scale. Subjects viewed all cases at least in two out of three conditions: unprocessed, enhanced and with morphing between
these two. MCMR ROC and JAFROC analyses were conducted. Results: No significant differences were found in ROC
AUC values across modalities and specialities. Only localization performance with morphing tool is significantly higher (F(1,8)=13.303, p=0.007) for chest expert (JAFROC FOM=0.6355) from non-chest (JAFROC FOM=0.4675) radiologists. Conclusion: Radiologists specialized in chest image interpretation performed consistently well in localizing pulmonary nodules, whereas non-chest radiologists were suffer from distracting effect of morphing tool.
Independent Component Analysis (ICA) is a blind source separation technique that has previously been applied to various time-varying signals. It may in particular be utilized to study 1H-MR spectroscopic imaging (MRSI) data. The work presented firstly investigates preprocessing and parameterization for ICA on simulated data to assess different strategies. We then applied ICA processing to 2D/3D brain and prostate MRSI data obtained from two healthy volunteers and 17 patients. We conducted a correlation analysis of the mixing and separating matrices resulting from ICA processing with maps obtained from metabolite quantitations in order to elucidate the relationship between quantitative and ICA results. We found that the mixing matrices corresponding to the estimated independent components highly correlate with the metabolite maps for some cases,
and for others differ. We provide explanations and speculations for that and propose a scheme to utilize the knowledge for
hot-spot detection. From our experience, ICA is much faster than the calculation of metabolic maps. Additionally, water and
lipid contaminations are on the way removed from the data; the user needs not manually exclude spectroscopic voxels from
processing or analysis. ICA results show hot spots in the data, even where quantitation-based metabolic maps are difficult to
assess due to noisy data or macromolecule distortions.
Breast cancer diagnosis based on magnetic resonance images (breast MRI) is increasingly being accepted as an
additional diagnostic tool to mammography and ultrasound, with distinct clinical indications.1 Its capability
to detect and differentiate lesion types with high sensitivity and specificity is countered by the fact that visual
human assessment of breast MRI requires long experience. Moreover, the lack of evaluation standards causes
diagnostic results to vary even among experts. The most important MR acquisition technique is dynamic contrast
enhanced (DCE) MR imaging since different lesion types accumulate contrast material (CM) differently. The
wash-in and wash-out characteristic as well as the morphologic characteristic recorded and assessed from MR
images therefore allows to differentiate benign from malignant lesions. In this work, we propose to calculate
second order statistical features (Haralick textures) for given lesions based on subtraction and 4D images and
on parametermaps. The lesions are classified with a linear classification scheme into probably malignant or
probably benign. The method and model was developed on 104 histologically graded lesions (69 malignant and
35 benign). The area under the ROC curve obtained is 0.91 and is already comparable to the performance of a
trained radiologist.
We present a novel software assistant for the analysis of multi-voxel 2D or 3D in-vivo-spectroscopy signals based on the
rapid-prototyping platform MeVisLab. Magnetic Resonance Spectroscopy (MRS) is a valuable in-vivo metabolic
window into tissue regions of interest, such as the brain, breast or prostate. With this method, the metabolic state can be
investigated non-invasively. Different pathologies evoke characteristically different MRS signals, e.g., in prostate cancer,
choline levels increase while citrate levels decrease compared to benign tissue. Concerning the majority of processing
steps, available MRS tools lack performance in terms of speed. Our goal is to support clinicians in a fast and robust
interpretation of MRS signals and to enable them to interactively work with large volumetric data sets. These data sets
consist of 3D spatially resolved measurements of metabolite signals. The software assistant provides standard analysis
methods for MRS data including data import and filtering, spatio-temporal Fourier transformation, and basic calculation
of peak areas and spectroscopic metabolic maps. Visualization relies on the facilities of MeVisLab, a platform for
developing clinically applicable software assistants. It is augmented by special-purpose viewing extensions and offers
synchronized 1D, 2D, and 3D views of spectra and metabolic maps. A novelty in MRS processing tools is the side-by-side
viewing ability of standard FT processed spectra with the results of time-domain frequency analysis algorithms like
Linear Prediction and the Matrix Pencil Method. This enables research into the optimal toolset and workflow required to
avoid misinterpretation and misapplication.
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