Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.
We present techniques for segmenting the middle phalanx of the middle finger in digital radiographic images using deformable models and active shape models (ASMs). The result of segmentation may be used in the estimation of bone mineral density which in turn may be used in the diagnosis of osteoporosis. A technique for minimizing user dependence is described. The segmentation accuracy of the two methods is assessed by comparing contours produced by the algorithms to those produced by manual segmentation, using the Hausdorff distance measure. The ASM technique produces more accurate segmentation.
Purpose The liver is a common site for metastatic disease, which is a challenging and life‐threatening condition with a grim prognosis and outcome. We propose a standardized workflow for the diagnosis of oligometastatic disease (OMD), as a gold standard workflow has not been established yet. The envisioned workflow comprises the acquisition of a multimodal image data set, novel image processing techniques, and cone beam computed tomography (CBCT)‐guided biopsy for subsequent molecular subtyping. By combining morphological, molecular, and functional information about the tumor, a patient‐specific treatment planning is possible. We designed and manufactured an abdominal liver phantom that we used to demonstrate multimodal image acquisition, image processing, and biopsy of the OMD diagnosis workflow. Methods The anthropomorphic abdominal phantom contains a rib cage, a portal vein, lungs, a liver with six lesions, and a hepatic vessel tree. This phantom incorporates three different lesion types with varying visibility under computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography CT (PET‐CT), which reflects clinical reality. The phantom is puncturable and the size of the corpus and the organs is comparable to those of a real human abdomen. By using several modern additive manufacturing techniques, the manufacturing process is reproducible and allows to incorporate patient‐specific anatomies. As a first step of the OMD diagnosis workflow, a preinterventional CT, MRI, and PET‐CT data set of the phantom was acquired. The image information was fused using image registration and organ information was extracted via image segmentation. A CBCT‐guided needle puncture experiment was performed, where all six liver lesions were punctured with coaxial biopsy needles. Results Qualitative observation of the image data and quantitative evaluation using contrast‐to‐noise ratio (CNR) confirms that one lesion type is visible only in MRI and not CT. The other two lesion types are visible in CT and MRI. The CBCT‐guided needle placement was performed for all six lesions, including those visible only in MRI and not CBCT. This was possible by successfully merging multimodal preinterventional image data. Lungs, bones, and liver vessels serve as realistic inhibitions during needle path planning. Conclusions We have developed a reusable abdominal phantom that has been used to validate a standardized OMD diagnosis workflow. Utilizing the phantom, we have been able to show that a multimodal imaging pipeline is advantageous for a comprehensive detection of liver lesions. In a CBCT‐guided needle placement experiment we have punctured lesions that are invisible in CBCT using registered preinterventional MRI scans for needle path planning.
The article Image registration in dynamic renal MRI—current status and prospects, written by Frank G. Zöllner, Amira Šerifović‑Trbalić, Gordian Kabelitz, Marek Kociński, Andrzej Materka and Peter Rogelj, was originally published electronically on the publisher’s internet portal on 9 October 2019 without open access.With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 24 April 2020 to ©
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