Level-Set-Segmentierung von Rattenhirn-MRTs

In dieser Arbeit wird die Segmentierung von Gehirngewebe aus magnet-resonanz-tomographischen Kopfaufnahmen von Ratten mittels Level-Set-Methoden vorgestellt. Dieses so genannte Skull-Stripping stellt einen wichtigen Vorverarbeitungsschritt für quantitative, morphometrische Untersuchungen oder aber Visualisierungsaufgaben dar. Ein kontrastbasierter Segmentierungsalgorithmus wird von einem Pseudo-3DAnsatz in einen echt-dreidimensionalen Segmentierer überführt. Die durch die Level-Set-Funktion beschriebene Kontur wird mittels einer partiellen Differentialgleichung iterativ deformiert und den Grenzen des zu segmentierenden Objektes angenähert. Die Geschwindigkeitsfunktion, welche lokale Kontraste auf der Konturnormalen auswertet und so die Oberflächenentwicklung bestimmt, wird untersucht und das lokale Signal adaptiert. Hierzu wird eine Glättung des Signals eingeführt, die sowohl in Richtung der Konturnormalen als auch parallel dazu wirkt. Zusätzlich wird eine varianzbasierte Kontrastverstärkung des lokalen Signals entwickelt. Daraus resultieren insbesondere in Bildbereichen mit geringem Signal-zu-Rausch-Verhältnis erheblich robustere und exaktere Segmentierungsergebnisse. Diese Leistungsfähigkeit wird an vorliegenden Rattenhirn-MRTs demonstriert

Rauschreduktion in digitalen Niedrigdosis-Röntgenbildern

Rauschreduktion in Niedrigdosis-Röntgenbildern, wie sie beispielsweise in der intraoperativen Fluoroskopie erzeugt werden, stellt für die Bildverarbeitung der akquirierten Aufnahmen einen entscheidenden Schlüsselfaktor dar. Für eine visuelle Qualitätsverbesserung wird in dieser Arbeit eine auf einem multiskalaren Ansatz beruhende Rauschreduktion vorgestellt, welche auf der zur nichtlinearen, anisotropen Diffusion verwandten bilateralen Filterung basiert. Hierbei werden insbesondere die bestehenden Verfahren auf die Eigenschaften des im Röntgenbild vorhandenen Rauschens angepasst. Die Leistungsfähigkeit der entwickelten Filterung wird an klinischen Fluoroskopiesequenzen demonstriert

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients

Background: MR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute.Methods: Auto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures. Results: Automated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures. Conclusion: Majority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.</p

Technical Note: Four‐dimensional deformable digital phantom for MRI sequence development

From Wiley via Jisc Publications RouterHistory: received 2021-02-04, rev-recd 2021-05-14, accepted 2021-05-26, pub-electronic 2021-08-02Article version: VoRPublication status: PublishedFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/R5131631/1Funder: NIHR Manchester Biomedical Research CentreFunder: Cancer Research UK; Id: http://dx.doi.org/10.13039/501100000289; Grant(s): A21993Abstract: Purpose: MR‐guided radiotherapy has different requirements for the images than diagnostic radiology, thus requiring development of novel imaging sequences. MRI simulation is an excellent tool for optimizing these new sequences; however, currently available software does not provide all the necessary features. In this paper, we present a digital framework for testing MRI sequences that incorporates anatomical structure, respiratory motion, and realistic presentation of MR physics. Methods: The extended Cardiac‐Torso (XCAT) software was used to create T1, T2, and proton density maps that formed the anatomical structure of the phantom. Respiratory motion model was based on the XCAT deformation vector fields, modified to create a motion model driven by a respiration signal. MRI simulation was carried out with JEMRIS, an open source Bloch simulator. We developed an extension for JEMRIS, which calculates the motion of each spin independently, allowing for deformable motion. Results: The performance of the framework was demonstrated through simulating the acquisition of a two‐dimensional (2D) cine and demonstrating expected motion ghosts from T2 weighted spin echo acquisitions with different respiratory patterns. All simulations were consistent with behavior previously described in literature. Simulations with deformable motion were not more time consuming than with rigid motion. Conclusions: We present a deformable four‐dimensional (4D) digital phantom framework for MR sequence development. The framework incorporates anatomical structure, realistic breathing patterns, deformable motion, and Bloch simulation to achieve accurate simulation of MRI. This method is particularly relevant for testing novel imaging sequences for the purpose of MR‐guided radiotherapy in lungs and abdomen

Breast MRI segmentation for density estimation:Do different methods give the same results and how much do differences matter?

PURPOSE: To compare two methods of automatic breast segmentation with each other and with manual segmentation in a large subject cohort. To discuss the factors involved in selecting the most appropriate algorithm for automatic segmentation and, in particular, to investigate the appropriateness of overlap measures (e.g., Dice and Jaccard coefficients) as the primary determinant in algorithm selection. METHODS: Two methods of breast segmentation were applied to the task of calculating MRI breast density in 200 subjects drawn from the Avon Longitudinal Study of Parents and Children, a large cohort study with an MRI component. A semiautomated, bias-corrected, fuzzy C-means (BC-FCM) method was combined with morphological operations to segment the overall breast volume from in-phase Dixon images. The method makes use of novel, problem-specific insights. The resulting segmentation mask was then applied to the corresponding Dixon water and fat images, which were combined to give Dixon MRI density values. Contemporaneously acquired T1 - and T2 -weighted image datasets were analyzed using a novel and fully automated algorithm involving image filtering, landmark identification, and explicit location of the pectoral muscle boundary. Within the region found, fat-water discrimination was performed using an Expectation Maximization-Markov Random Field technique, yielding a second independent estimate of MRI density. RESULTS: Images are presented for two individual women, demonstrating how the difficulty of the problem is highly subject-specific. Dice and Jaccard coefficients comparing the semiautomated BC-FCM method, operating on Dixon source data, with expert manual segmentation are presented. The corresponding results for the method based on T1 - and T2 -weighted data are slightly lower in the individual cases shown, but scatter plots and interclass correlations for the cohort as a whole show that both methods do an excellent job in segmenting and classifying breast tissue. CONCLUSIONS: Epidemiological results demonstrate that both methods of automated segmentation are suitable for the chosen application and that it is important to consider a range of factors when choosing a segmentation algorithm, rather than focus narrowly on a single metric such as the Dice coefficient