Where Intelligence Guides Intention.
Radiation therapy begins long before the beam. It begins with understanding—of anatomy, of proximity, of consequence.
Neurospace brings multimodal imaging, patient-specific anatomy, spatial risk context, and multidisciplinary review into one pre-planning environment designed to help clinical teams see the case as a whole before treatment planning begins.
Planning is not one image. It is a clinical interpretation of many.
A radiation oncology plan may depend on simulation imaging, diagnostic studies, postoperative change, prior therapy, target definition, critical structures, and the judgment of several specialists. Neurospace Radiation Oncology Space is designed to make those relationships easier to review together—without reducing the case to disconnected windows and datasets.
Multimodal Context
Bring CT, MRI, PET and other clinically relevant imaging into a unified spatial review so target and normal-tissue decisions can be made with the full imaging context visible.
Changing Anatomy
Review intact, postoperative, and longitudinal anatomy in context, making change itself part of the planning conversation rather than a separate comparison task.
Critical Structure Awareness
Keep targets, organs at risk, prior treatment context, and spatial relationships visible together so proximity can be understood before optimization decisions are made.
Shared Interpretation
Give radiation oncologists, neuroradiologists, neurosurgeons, physicists, and dosimetrists a common patient-specific visual language for multidisciplinary review.
Patient-Specific Spatial Model
One patient. Multiple datasets. One clinical frame of reference.
Neurospace is designed to help teams reconcile imaging sources into a patient-specific model that preserves the context of each modality while making their spatial relationship easier to interrogate. The Neurospace Intelligence Layer supports this step by synthesizing multimodal information and highlighting correlations that deserve clinical review.
The objective is not to hide complexity. It is to organize it—so the physician can move from image acquisition to clinical meaning without losing track of where information came from or how it relates to anatomy.
- Multimodal registration and fusion workflows with reviewable source context
- Patient-specific 3D anatomy and pathology visualization
- Structured clinical layers for targets, critical structures, and planning notes
- AI-assisted anatomical identification and segmentation proposals for clinician review
- Human validation and refinement before AI-assisted structures become clinician-approved layers
Proximity Intelligence
Distance matters. Relationship matters more.
A clinically meaningful planning review is not only about identifying where a target is. It is about understanding how that target sits relative to sensitive anatomy, prior treatment context, surgical change, and the structures that constrain therapeutic intent.
Neurospace turns proximity into a visual planning layer. The Intelligence Layer can quantify and prioritize target-to-critical-structure relationships, recognize spatial patterns across patient-specific anatomy, and surface areas that deserve focused multidisciplinary attention before the treatment plan is finalized.
- Dynamic proximity visualization between targets and critical structures
- Review layers for organs at risk and prophylactic regions
- Contextual comparison of current anatomy with prior studies or treatment information
- Clear visual separation between source data, AI assistance, and clinician-approved structures
Artificial insight, built around the decisions radiation oncology teams already make.
In Radiation Oncology Space, AI is not a generic add-on and it does not prescribe treatment. It works upstream of the final plan to organize complex case information, assist anatomical understanding, expose spatial relationships, and prepare a more coherent pre-treatment review environment for the clinical team.
Specialty-Specific AI
See relationships. Prioritize risk. Preserve clinical control.
The Intelligence Layer combines imaging context, structure information, longitudinal data, and clinical review state into a patient-specific computational model. Its role is to help the team recognize what matters sooner—not to replace the radiation oncologist, dosimetrist, physicist, or treatment planning system.
Every AI-assisted output is designed to remain distinguishable from source data and clinician-approved information so the team can review, refine, accept, or reject what the system proposes.
From image ingestion to a review-ready planning context.
Radiation Oncology Space is designed as an intelligence and visualization layer around the existing clinical workflow. The Neurospace Intelligence Layer participates throughout the pre-treatment pathway by organizing data, assisting image correlation and segmentation, prioritizing spatial relationships, and preparing review context—without replacing the physician, treatment planning system, dose calculation, or institutional QA. Each AI-assisted step remains traceable and reviewable.
Ingest + Organize
Bring clinically relevant imaging and case data into one patient workspace; AI helps classify and prioritize available context.
Fuse + Correlate
Align multimodal and longitudinal image sets while AI-assisted synthesis highlights relationships for review.
Identify + Segment
Generate or import structures; AI-assisted anatomical identification proposes targets/OAR context for clinician validation.
Interpret + Prioritize
Examine targets, OARs, postoperative change, and case context as AI prioritizes relationships that may warrant attention.
Map Risk + Simulate
Quantify proximity and spatial constraints, explore planning context, and focus multidisciplinary review before optimization.
Review + Collaborate
Clinicians validate the intelligence layer, share the common 3D case, and hand approved information into downstream planning.
From anatomy to planning intent.
The approved Neurospace dosimetric visualization remains the central visual language of Radiation Oncology Space. Here it represents the Intelligence Layer moving from multimodal anatomy and AI-assisted structure understanding toward a planning-ready spatial context: relationships are synthesized, proximity is prioritized, and review state is organized before downstream treatment planning—while the clinician remains in control of interpretation and treatment decisions.
One case. Several specialties. A shared spatial language.
Radiation oncology decisions often sit at the intersection of imaging, surgery, physics, anatomy, and longitudinal care. Neurospace gives each discipline the same patient-specific case model while preserving the perspective each specialist brings to the plan.
Radiation Oncologist
Target intent, treatment strategy, clinical priorities, and final physician judgment.
Neuroradiologist
Imaging interpretation, disease extent, postoperative change, and multimodal correlation.
Neurosurgeon
Operative anatomy, resection context, surgical corridors, and residual disease considerations.
Medical Physicist
Technical feasibility, planning integrity, imaging quality, and treatment-system constraints.
Dosimetrist
Structure organization, planning preparation, optimization workflow, and plan construction context.
AR-Based Interdisciplinary Review
When a flat screen is not enough, bring the case into spatial context.
For complex anatomy, Neurospace can extend the planning discussion into a mixed-reality review environment. Patient-specific structures can be explored at human scale through a supported headset, giving specialists another way to inspect spatial relationships during planning conferences, education, and remote collaboration.
The visual is intentionally headset-based: AR information exists within the mixed-reality view, not as a magical projection visible to everyone in the room.
- Patient-specific 3D anatomy viewed through enterprise mixed-reality hardware
- Shared review for multidisciplinary planning and education
- Spatial annotations and focused discussion of targets and critical structures
- Remote expert participation through Neurospace Teleportal where configured
Designed to fit into the radiation oncology department you already have.
Neurospace is positioned as a clinical intelligence and collaboration layer around existing infrastructure. Integration is designed around established imaging and radiation oncology data pathways so institutions can extend their workflow without replacing the systems that already perform acquisition, planning, delivery, and record management.
Built for cases where spatial context changes the conversation.
The following are examples of planning contexts the Space is designed to support. They describe intended workflow value—not autonomous clinical decisions or treatment prescriptions.
SRS / SRT Case Review
Correlate diagnostic and planning imaging, review lesion-to-critical-structure relationships, and bring physician, imaging, and physics perspectives into one shared case model.
Resection Cavity Context
Compare postoperative anatomy with preoperative and diagnostic imaging to improve the visual context surrounding cavity, residual disease, and nearby sensitive anatomy.
Skull-Base Proximity
Use multimodal visualization to examine complex relationships between disease and adjacent neural, vascular, optic, and brainstem structures during planning review.
Prior Treatment Context
Bring previous studies and available treatment information into the current case review so prior anatomy and treatment history are easier to discuss alongside present findings.
Multidisciplinary Decision Support
Present the same patient-specific spatial model to surgery, radiology, oncology, and physics teams to reduce the translation gap between specialties.
Case-Based Teaching
Use real clinical structure and imaging relationships to teach residents, fellows, and multidisciplinary teams how planning decisions emerge from anatomy and context.
The Intelligence Layer informs the plan. The clinical team owns the decision.
AI is clinically contextual
Multimodal synthesis, anatomical identification, segmentation assistance, proximity analysis, pattern recognition, and workflow prioritization are applied specifically to radiation oncology pre-treatment review.
Reviewable by design
Source data, AI-derived proposals, confidence/context cues, annotations, and clinician-approved layers remain distinguishable so the team can inspect what the system is showing and decide what enters the clinical workflow.
Existing systems remain essential
Neurospace does not replace treatment planning systems, dose calculation engines, delivery systems, institutional QA, or the clinician responsible for the plan.
Clinical judgment remains final
The physician remains responsible for diagnosis, target definition, treatment intent, prescription, and patient-specific clinical decisions.
See the case before you see the plan.
Bring multimodal imaging, patient-specific anatomy, the Neurospace Intelligence Layer, spatial risk context, and multidisciplinary review into one pre-treatment environment—built to complement the systems already inside your department and keep clinical judgment at the center.