PET Imaging Analysis
PET analysis models work on tracer distribution from FDG, PSMA, DOTATATE, and amyloid studies, computing SUVmax, SUVpeak, and metabolic tumor volume that the reading physician verifies before it enters the report.
Custom Software
AI Nuclear Medicine Software Development
AI nuclear medicine software applies machine learning to PET, SPECT, and theranostic imaging data to help physicians quantify tracer uptake, segment lesions, and compare response across serial studies. It operates strictly as decision support: the reading physician interprets every result and makes all diagnostic, staging, and dosing decisions.
Nuclear medicine generates dense quantitative data that rarely reaches its full value inside legacy reading workflows. Taction Software builds AI nuclear medicine tools that sit alongside existing PET/CT and SPECT/CT pipelines, surfacing measurements and prior-study comparisons without displacing physician judgment. Every deployment is engineered for auditability, traceability, and clinician control.
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What Is AI Nuclear Medicine
AI nuclear medicine refers to machine learning and computer vision applied to functional and molecular imaging: PET, SPECT, planar scintigraphy, and theranostic dosimetry. Unlike anatomical imaging, nuclear studies measure physiology, so models work with tracer distribution, time-activity curves, and standardized uptake values rather than structure alone. These systems are assistive by design. They pre-populate measurements, flag regions for review, and align serial studies, but the interpreting physician confirms or overrides every output. Our work in this space sits within our broader healthcare AI practice, where model validation, bias monitoring, and regulatory posture are treated as engineering requirements rather than afterthoughts.
SPECT and Planar Studies
SPECT and planar scintigraphy support covers myocardial perfusion, bone scans, renal function, and thyroid uptake, where quantitative SPECT reconstruction and attenuation correction feed measurements into a physician-reviewed reading worklist.
Theranostics and Dosimetry
Theranostic dosimetry tooling supports Lu-177 and Ac-225 workflows by organizing pre-treatment and post-treatment activity data, producing absorbed dose estimates that the authorized user reviews and approves before any prescribing decision.
Radiomics Feature Extraction
Radiomics pipelines extract texture, shape, and intensity features from segmented volumes, producing reproducible quantitative imaging biomarkers for research registries and prospective studies rather than standalone clinical conclusions.
Automated Lesion Segmentation
Lesion segmentation models propose contours across whole-body PET volumes, reducing manual delineation time. Physicians edit and accept each contour, and every revision is versioned so the final total lesion glycolysis figure is traceable.
Decision Support Boundaries
All outputs are labeled as clinical decision support. The software never issues a diagnosis, stages disease, or authorizes a therapeutic dose. Physician confirmation is a required, logged step in every workflow we build.
Core AI Nuclear Medicine Services
Our AI nuclear medicine services span the full path from feasibility assessment to production deployment inside a hospital or imaging network. Most engagements begin with a data readiness review, because nuclear medicine archives are often fragmented across PACS, dose management systems, and departmental spreadsheets. From there we build ingestion, model integration, reader-facing interfaces, and monitoring. We also handle the integration layer that determines whether a tool gets adopted: worklist placement, structured reporting output, and write-back into existing systems. Each service is scoped so that clinical, IT, and regulatory stakeholders can review deliverables independently before anything touches a live reading environment.
01
Custom Model Development
We build and train custom imaging models on institutional data, covering segmentation, classification, and quantification tasks, with documented training data provenance, holdout evaluation, and subgroup performance reporting before any clinical pilot.
02
Third-Party Algorithm Integration
We integrate commercially available FDA-cleared algorithms into your reading workflow, handling orchestration, result routing, and failure handling so vendor tools appear as one coherent AI orchestration layer for readers.
03
PACS and DICOM Integration
Integration work covers DICOM routing, structured report generation, and secondary capture output, so results land in the archive correctly. Our DICOM standards reference explains the underlying data model.
04
Reading Workflow Interfaces
We design reader-facing viewers and hanging protocols that display AI measurements alongside prior studies, keeping edit, accept, and reject controls one click away so physician oversight never adds friction.
05
Reporting and Data Extraction
Structured reporting integration pushes verified measurements into report templates and downstream registries, replacing manual transcription while preserving the physician-approved values as the single source of record.
06
Model Monitoring and Retraining
Post-deployment we instrument drift detection, scanner-level performance tracking, and periodic revalidation, with fairness monitoring across demographic and equipment subgroups so degradation is caught before it affects reads.
Benefits of AI Nuclear Medicine
The practical benefits of AI nuclear medicine are concentrated in measurement consistency, time recovered from repetitive tasks, and better use of data the department already collects. Manual segmentation and multi-study comparison consume significant reader time, and quantification varies between readers and between scanners. Software that standardizes these steps produces more comparable numbers over time, which matters for response assessment and for research. We do not publish efficacy or throughput claims, because those depend entirely on your baseline workflow, scanner mix, and case volume. What we commit to is measurable instrumentation, so your team can evaluate impact against your own data rather than a vendor assertion.
Measurement Reproducibility
Automated quantification reduces inter-reader variability in uptake measurement, producing values that are more comparable across serial studies and supporting more consistent response assessment over a treatment course.
Reader Time Reallocation
Pre-populated contours and measurements shift reader effort from manual delineation toward interpretation, easing reading workflow pressure in departments facing rising volumes and constrained nuclear medicine staffing.
Longitudinal Comparison
Automated prior study registration aligns current and historical volumes, letting physicians assess lesion-level change without reconstructing comparisons manually across separate archive sessions.
Research Data Readiness
Structured extraction turns routine reads into a queryable imaging data repository, supporting trial recruitment, registry submission, and retrospective analysis that fragmented reporting cannot practically enable.
Standardized Departmental Protocols
Encoding acquisition and analysis rules in software promotes protocol standardization across sites and scanners, reducing the variability that undermines multi-site quantitative imaging programs.
Clearer Oncology Coordination
Verified quantitative results flow into tumor board and treatment planning discussions more cleanly, complementing the workflows described on our oncology AI page.
Our AI Nuclear Medicine Process
We run AI nuclear medicine projects in defined phases so clinical stakeholders can approve direction before engineering cost accumulates. Discovery establishes the clinical question, the data available to answer it, and the regulatory pathway implied by the intended use. Development is iterative, with physician review built into each cycle rather than saved for acceptance testing. Deployment is staged: shadow mode first, where outputs are recorded but not shown, then limited clinical availability, then broader rollout. This sequencing matters in nuclear medicine because model behavior varies by scanner, reconstruction method, and tracer, and those differences surface only against real institutional data.
Discovery and Feasibility
Discovery defines intended use, reader workflow, and available archives, then produces a fixed-scope estimate. We assess data volume, annotation burden, and regulatory pathway implications before committing to a build.
Data Preparation and Annotation
We assemble de-identified training sets, establish annotation protocols, and measure inter-annotator agreement, because ground truth quality determines model ceiling more than architecture choice in nuclear imaging.
Model Development and Validation
Development proceeds through cross-validation to held-out external validation, with performance reported by scanner, tracer, and patient subgroup so limitations are documented rather than averaged away.
Clinical Workflow Integration
Integration wires the model into archive routing, worklists, and reporting, tested against your radiology systems. See our radiology information system development work for related RIS integration patterns.
Shadow Mode Evaluation
Before clinical exposure the system runs in shadow deployment, logging outputs alongside physician reads so your team can compare agreement and identify failure modes without patient impact.
Staged Rollout and Monitoring
Rollout expands by reader group and modality, with performance dashboards and defined rollback triggers. Post-deployment surveillance continues for the life of the deployment.
Technology and Compliance
Nuclear medicine software touches PHI, imaging archives, and in some configurations therapeutic planning data, so compliance posture is designed in from the first sprint. Our engineering follows HIPAA-aligned practice, and Taction holds ISO 27001 certification. Where a tool’s intended use makes claims about diagnosis or treatment, it may meet the definition of Software as a Medical Device, which changes documentation, validation, and change control obligations substantially. We assess that early rather than retrofitting. On the technical side we build on standards-based imaging interfaces, containerized inference, and audit logging detailed enough to reconstruct any individual result after the fact.
HIPAA-Aligned Engineering
Builds apply encryption in transit and at rest, role-based access, and complete audit logging. Our HIPAA compliance software development practice defines the controls we implement by default.
SaMD and FDA Considerations
Where intended use implies diagnostic claims, SaMD classification applies. We build design history documentation, validation records, and change control to support the applicable regulatory submission path.
Imaging Standards and Interoperability
We implement DICOM services, DICOM Structured Reporting, and FHIR resources for downstream exchange, so verified results move between imaging and clinical systems without manual re-entry.
Model Validation and Bias Monitoring
Validation reports performance by scanner model, reconstruction protocol, and patient subgroup, with ongoing bias monitoring because equipment and demographic shifts change model behavior over time.
Generative Output Controls
Where models draft narrative text, we treat hallucination risk as a design constraint: outputs stay grounded in extracted measurements, and physician review of every generated passage is mandatory.
Deployment and Infrastructure Security
Deployments run on-premise, in your cloud tenancy, or hybrid, with network segmentation, signed container images, and documented penetration testing before production release.
Why Choose Taction Software
Taction Software was founded in 2013 and has spent over 12 years building healthcare software, with more than 200 healthcare projects delivered. We operate from four US offices in Chicago, Cheyenne, Austin, and Sacramento, and hold ISO 27001 certification. Our credibility in imaging AI comes from delivery rather than marketing: we have built EHR and EMR platforms, FDA-registered mobile applications, and behavioral health systems, which means we understand the regulatory and integration constraints that stall imaging projects. Our leadership brings more than 20 years of personal experience in the field, and that judgment shapes how we scope regulated work.
01
Established Healthcare Focus
Founded in 2013, we have concentrated on healthcare rather than treating it as one vertical among many, giving us practical depth in clinical workflow and imaging system constraints.
02
Regulated Product Experience
We have delivered FDA-registered applications including Revive Ease and PainKare, so design controls, validation documentation, and change management are established practice rather than new territory.
03
EHR and Clinical Systems Depth
Our Voyant Health EHR and EMR work means EHR integration is handled by engineers who have built the systems on both sides of the interface, not just consumed an API.
04
Behavioral and Sensitive Data Handling
Delivering CHIPSS for behavioral health required strict data segmentation and consent handling, experience that transfers directly to imaging archives holding sensitive diagnostic information.
05
US-Based Delivery Teams
Four US offices support overlapping working hours, on-site discovery sessions, and direct access to engineers, which shortens stakeholder review cycles on regulated builds.
06
Certified Security Posture
ISO 27001 certification and HIPAA-aligned engineering mean security controls are documented and auditable, supporting your vendor risk assessment without a lengthy remediation phase.
Pricing
AI nuclear medicine pricing depends on scope, data condition, integration surface, and regulatory pathway. A single-module build integrating an existing algorithm into a reading workflow costs far less than a custom-trained model requiring external validation and a regulatory submission. We price after discovery, not before, because nuclear medicine data readiness varies enormously between institutions and drives a large share of effort. Discovery produces an itemized, fixed-scope estimate with phase-level breakdown, so you can approve or defer specific components. Third-party algorithm licensing, cloud infrastructure, and scanner vendor fees are separate from our engineering cost and are itemized clearly.
MVP or Single Module
An MVP covering one modality, one analysis task, and basic archive integration typically runs $40,000 to $80,000, suitable for validating clinical value before broader commitment.
Full Platform Build
A full platform with multiple analysis pipelines, reader interfaces, reporting integration, and monitoring typically falls between $80,000 and $200,000 depending on validation depth required.
Enterprise Deployment
Enterprise engagements spanning multi-site rollout, custom model training, external validation, and regulatory documentation start at $200,000 and scale with site count and submission scope.
Discovery Phase Scoping
Discovery is a paid, time-boxed phase producing an itemized estimate, architecture plan, and regulatory assessment. It is deliberately separable so you can evaluate our work before full commitment.
Cost Drivers to Expect
Annotation volume, external validation requirements, scanner heterogeneity, and SaMD documentation are the largest variables. We identify each during discovery so budget planning reflects real effort.
Ongoing Support Costs
Post-launch model monitoring, retraining cycles, and support are quoted separately as a retainer, sized to your deployment footprint and revalidation cadence.
Get Started
If you are evaluating AI nuclear medicine tooling for a PET, SPECT, or theranostics program, the fastest way forward is a discovery call with our clinical engineering team. We will review your archive condition, reader workflow, and intended use, then return an itemized, fixed-scope estimate with a clear regulatory assessment. Reach out to schedule that conversation.
FAQs
Frequently Asked Questions
Buyers evaluating AI nuclear medicine software usually have questions clustered around three concerns: whether the tool makes clinical decisions, what regulatory obligations it creates, and what it costs to reach a validated production deployment. The answers below reflect how we actually scope and deliver these projects. If your situation involves a novel tracer, an unusual scanner mix, or an intended use that may require FDA submission, the specifics matter more than any general answer, and a discovery conversation will be more useful than a page like this one.
No. Every tool we build functions as clinical decision support. The system proposes measurements, contours, and flags for review, and the interpreting physician confirms, edits, or rejects each one. Physician confirmation is a required and logged workflow step. We do not build autonomous diagnostic or therapeutic decision-making, and we do not make efficacy claims about model output.
It depends entirely on intended use. Software making diagnostic or treatment claims may meet the definition of Software as a Medical Device and require a regulatory pathway, while internal quantification and research tooling often does not. We assess classification during discovery and build the design controls and validation documentation your pathway requires.
An MVP or single module runs $40,000 to $80,000. A full platform typically falls between $80,000 and $200,000. Enterprise deployments with multi-site rollout and regulatory documentation start at $200,000. Discovery produces an itemized, fixed-scope estimate, and third-party licensing and infrastructure costs are quoted separately from engineering.
Yes. We implement standards-based DICOM integration including query and retrieve, storage, worklist, and Structured Reporting, and we test against your specific archive and viewer configuration. Where vendor-specific behavior deviates from the standard, which is common, we handle that during integration rather than assuming conformance.
We instrument drift detection, track performance by scanner and protocol, and monitor subgroup results for fairness. Monitoring includes defined thresholds and rollback triggers. Revalidation cadence is agreed during scoping, and retraining is treated as a controlled change with documentation rather than a silent model swap.
Yes, subject to governance approval and appropriate agreements. We work with de-identified datasets, document data provenance, and establish annotation protocols with your physicians. Data volume and annotation quality usually determine feasibility more than model architecture, which is why discovery includes a candid data readiness assessment.