The invention relates to processing multi-subject volumes

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Preclinical research is characterized by hypothesis driven programs to either discover or explore new insights into biological processes. A goal of these programs is systematic discovery and development of biomarkers, drugs, and therapies that will ultimately be translated from animal models to humans should they prove promising during preclinical studies. Small animal models of human diseases, particularly genetically engineered mice, are widely used in preclinical studies. Studies on distributed population groups (example group of animals which received a drug vs. control group of animals) are done to substantiate the hypothesis.

In vivo imaging permits both the temporal and the spatial bio-distribution of a molecular probe and related biological processes to be determined in a more meaningful manner throughout an intact living subject. It is possible to image multiple subjects simultaneously in a scanner in order to achieve higher throughput. Processing, evaluation and visualization of resulting image data may be performed using a pre-clinical workstation, such as the IMALYTICS Workspace from Philips Healthcare.

The method may be implemented as a computer program product.

The techniques described herein may, among other things, allow easy access and/or data management of image scans and quantitative results pertaining to group studies. The presence of multiple subjects in an imaging scan may pose a challenge in preclinical research workflow. In the presence of multiple subjects, associating relevant subject ID, group name and analysis data with the subject may become cumbersome. The archived separate volume image datasets can be retrieved based on group attributes such as strain name, group name, in particular if those attributes are included in the metadata. The image analysis user interface of existing preclinical applications can be customized for associating analyzed data or statistical results with a given subject's imaging data. This can lead to better integration of quantification data with the imaging study.

The user interface may enable collecting a set of information about a discovery project such as: principal investigator, groups involved in the study, and subjects associated with each group. The discovery projects may be accessed and stored by project name, allowing subject data associated with a given project to be stored together.

A hierarchy of data in a preclinical group study may comprise different projects performed at an institution. A project may comprise a number of groups of subjects. Each group may be exposed to a different kind of experiment. Each group may comprise a plurality of subjects. For high-throughput use of imaging equipment, a number of subjects may be scanned simultaneously. For example, FIG. 3 shows a visualization of an input volume image dataset comprising two subjects. These subjects do not necessarily belong to the same group and/or project. This may make the data cumbersome to use, because the input volume image dataset does not belong to a single group and/or project.

The presence of multiple subjects and subjects from different study groups in an imaging scan poses a challenge in workflow. In the presence of subjects from a study group in an imaging study, associating relevant subject ID, group name and analysis data with the subject becomes cumbersome. For example, an image format such as DICOM can store information pertaining to a single subject. The techniques described herein facilitate generating volumetric data per subject from an imaging scan containing volumetric data of multiple subjects and storing volumetric data of a subject as a separate DICOM series. The method may automatically determine presence of multiple subjects in a given scan and generate new volumetric data per subject. The generated volumetric data per subject can be saved as a new DICOM series for better workflow management and integration of the generated analysis data (in the later part of the workflow) with the subject.

Metadata attributes associated with a subject may comprise, for example, project ID, project name, project description, principal investigator, study, study ID, study description, technologist, modality type, scan type, acquisition protocol, reconstruction protocol, strain name, and/or study type (individual or group).