Koninklijke Philips Corporation N.v.

Ingwer Curt Carlsen

Kirsten Regina Meetz

Stewart Young

Rafael Wiemker

Heinrich Schulz

Steffen Renisch

Joerg Sabczynski

Roland Johannes Opfer

Steffen Weiss

Tobias Klinder

Joerg Bredno

Daniel Bystrov

Cristian Lorenz

Jens Von Berg

Michael Harald Kuhn

Lingxiong Shao

Thomas Netsch

Lilla Boroczky

Vladimir Pekar

Michael Kaus

Fabian Wenzel

Thomas Blaffert

Sven Kabus

Nicole Schadewaldt

Todd R McNutt

Astrid Ruth Franz

Hans Barschdorf

Mark Olszewski

Pradyumna Dutta

Micheal J Petrillo

Paola Karina Tulipano

Ralph Buchert

R Keith Tipton

Angela J DaSilva

An oncology monitoring system comprises: an image analysis module () configured to perform an oncological monitoring operation based on images of a subject, for example acquired by positron emission tomography (PET) and computed tomography (CT); and a clinical guideline support module (). The clinical guideline support module is configured to: display a graphical flow diagram (GFD) of a clinical therapy protocol for treating the subject comprising graphical blocks (B, B, B, B, B, B, B, B, B, B, B, B, B, B, B) representing therapeutic or monitoring operations of the clinical therapy protocol including at least one monitoring operation performed by the image analysis module; annotate a graphical block of the graphical flow diagram with subject-specific information pertaining to a therapeutic or monitoring operation represented by the graphical block; and display an annotation (POP) of a graphical block (B) responsive to selection of the graphical block by a user.

PET/CT based monitoring system supported by a clinical guideline navigator

A therapy planner is configured to construct a therapy plan based on a planning image segmented into segments delineating features of a subject. A predictive plan adaptation module is configured to adjust the segments to represent a foreseeable change in the subject and to invoke the therapy planner to construct a therapy plan corresponding to the foreseeable change. A data storage stores a plurality of therapy plans generated for a subject by the therapy planner and the predictive plan adaptation module based on at least one planning image of the subject. A therapy plan selector is configured to select one of the plurality of therapy plans for use in a therapy session based on a preparatory image acquired preparatory to the therapy session.

Prospective adaptive radiation therapy planning

A perfusion analysis system includes a perfusion modeller and a user interface. The perfusion modeller generates a patient specific perfusion model based on medical imaging perfusion data for the patient, a general perfusion model, and a quantification of one or more identified pathologies of the patient that affect perfusion in the patient. The user interface accepts an input indicative of a modification to the quantification of the one or more identified pathologies. In response, the perfusion modeller updates the patient specific perfusion model based on the medical imaging perfusion data for the patient, the general perfusion model, and the quantification of the one or more identified pathologies of the patient, including the modification thereto.

Perfusion imaging system with a patient specific perfusion model

To improve the scanning effect, a scanning method is provided, which comprises the steps of performing at least one of an nCT scan and a CTA scan on an object so as to obtain a set of images; detecting characteristics of a region of interest based on the set of images; and performing a CTP scan on the region of interest by adopting the characteristics to obtain a CTP image. By deriving the characteristics of the region of interest, e.g. a lesion or an area covering the lesion, before performing the CTP scan, and by adapting the subsequent CTP scan based on the characteristics of the region of interest, the drawback introduced by a limited scan area of the CTP scanner is mitigated, or even overcome.

Scanning method and system

A comprehensive strategy is used to determine valid reference time-concentration curves (TCCs) from image data. The image data corresponds to a series of image scans acquired over time for an area of interest of a patient to which a contrast agent was previously administered. The image scans are initially registered to a common coordinate system. Then, observed potential reference TCCs in the image scans are compared to modeled reference TCCs to determine if the potential reference TCCs are plausible reference TCCs. Thereafter, any plausible reference TCCs are evaluated to determine if they contain residual, isolated motion artifacts. If a plausible reference TCC does not include any motion artifacts, the plausible reference TCC is considered a valid reference TCC. If a plausible reference TCC is determined to include motion artifacts, the plausible reference TCC is modified to a valid reference TCC by removing the motion artifacts, or otherwise the plausible reference TCC is rejected. The valid reference TCCs can be used to improve the effectiveness of dynamic, contrast-enhanced imaging studies.

Plausible reference curves for dynamic, contrast-enhanced imaging studies

A system for displaying lung ventilation information, the system comprising an input () and a processing unit (). The input being provided for receiving multiple CT images () of a lung, each CT image () corresponding to one phase of at least two different phases in a respiratory cycle. The processing unit () being configured to compare CT images () corresponding to different phases in the respiratory cycle for determining a deformation vector field for each phase, to generate for each phase a ventilation image () based on the corresponding deformation vector field, to spatially align the ventilation images (), and to generate for at least one common position () in each one of the aligned ventilation images (), a function () of a time course of a ventilation value for said common position (), each ventilation value in the function () being based on the deformation vector fields corresponding to the aligned ventilation images ().

System for providing lung ventilation information

The invention relates to automatically adjusting an acquisition protocol for dynamic medical imaging, such as dynamic CT, MRI or PET imaging. The protocols are adjusted based on anatomic and dynamic models () which are individualized or fitted to each patient based on a scout scan (). The adjustment can compensate for changes in the patient due to patient motion (e.g. breathing or heartbeat) or flow of contrast or tracing agent during the sequence. This ensures that changes in the reconstructed images are indicative of pathological changes in the patient and not caused by patient motion or changes in scanning parameters or timing. The dynamic model can be a motion model () used to predict the motion of anatomic/physiologic features, typically organs, during scanning, or a haemodynamic model () used to predict flow of the contrast agent allowing for precise timing of the scanning sequence.

Adjusting acquisition protocols for dynamic medical imaging using dynamic models

A scanner () is used to provide images for automated diagnoses of neurodegenerative diseases, such as Alzheimers disease. The images are registered () to a template (). The aligned image is analyzed () in relation to reference image data () which has been registered to the template which is contained in a knowledge maintenance engine () for similar patterns of hypo-intensity that would indicate (in the case of an FDG tracer) reduced glucose uptake in the brain. The most appropriate reference images for the analysis of the present study are chosen by a filter (). The present study is then given a dementia score () as a diagnostic feature vector that indicates to a clinician the type and severity of the ailment based on the analysis. The scanner () can produce PET or other metabolic and MR images for diagnosis. The MR can be used to measure blood flow rate into the brain. From the blood flow rate and the metabolic image, tracer, e.g. FDG, uptake maps can be generated for use in the automated diagnoses.

Automated diagnosis and alignment supplemented with positron emission tomography (PET) and magnetic resonance (MR) flow estimation

Image registration very often used to be a tedious task which had to be performed manually. According to an exemplary embodiment of the present invention, a non-rigid image registration by an iterative refinement process based on a combination of landmarks with similarity values is proposed. Advantageously, a very fast and robust method maybe provided.

Elastic image registration

An imaging system () includes imaging modalities such as a PET imaging system () and a CT scanner (). The CT scanner () is used to produce a first image () which is used for primary contouring. The PET system () is used to provide a second image (), which provides complementary information about the same or overlapping anatomical region. After first and second images () are registered with one another the first and second images () are concurrently segmented to outline a keyhole (). The keyhole portion of the second image () is inserted into the keyhole () of the first image (). The user can observe the composite image and deform a boundary () of the keyhole () by a mouse () to better focus on the region of interest within previously defined keyhole.

Data representation for rtp

A system for generating registered diagnostic images (), such as nuclear and magnetic resonance (MR) images, of a subject includes a nuclear imaging device () for generating emission diagnostic images () and optionally also intermediate transmission or emission images (). A second imaging device (), such as an MR imaging device generates magnetic resonance diagnostic images () and optionally also intermediate images which are more readily registered with images from the nuclear imaging device than the diagnostic MR images. Processing for the images includes a preprocessing portion () for generating a transform for aligning common anatomical structures in images () generated by the nuclear imaging device and the MR imaging device and a diagnostic image registration portion for applying the transform to bring the emission and magnetic resonance diagnostic images into registration ().

Registration of multi-modality images

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Hamburg, Germany

Ingwer Curt Carlsen