Torch™ software is intended to provide estimates (deterministic) of absorbed radiation dose at the voxel level following internal administration of approved radioactive products. This is dependent on input data regarding biodistribution being supplied to the application. Torch software only allows voxel-based dose calculations. For use with internally administered radioactive products. Torch should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing information for instructions.
Device Story
Torch is a software-based medical image management and processing system used in clinical settings by physicians. It processes patient-specific medical images (PET, SPECT, CT) to perform post-treatment dosimetry. The device imports DICOM images, registers time-point studies, propagates contours, and performs pharmacokinetic modeling. It utilizes a Monte Carlo radiation transport algorithm, accelerated by GPU parallel processing, to calculate absorbed radiation dose at the voxel level. The output is a voxel-level dose map in DICOM format. By providing precise, patient-specific dosimetry for therapeutic radiopharmaceuticals, the device assists clinicians in assessing absorbed doses to tumors and normal tissues, supporting treatment evaluation and clinical decision-making.
Clinical Evidence
No clinical studies were required. Verification and validation were performed via bench testing. Torch's dose engine was validated against the ICRP 110 Adult Male reference dataset and compared to established Monte Carlo codes (EGS++ 2018, GATE 8.1, GEANT4 10.5/OpenDose project) and the OLINDA/EXM 2.0 predicate. Total absorbed dose results agreed within 5% of references; individual particle contributions agreed within 5.2%. Additional validation against physical film measurements (using 90Y and a custom puck-shaped phantom) showed agreement within 5%.
Technological Characteristics
Software-based radiological image processing system. Uses Monte Carlo radiation transport algorithm accelerated by GPUs. Inputs: PET/CT or SPECT/CT DICOM data. Outputs: Voxel-level absorbed dose maps in DICOM format. Compatible with alpha, beta, and photon radionuclide emissions (specifically 90Y, 177Lu, 131I). Operates on standard clinical computing hardware.
Indications for Use
Indicated for patients receiving internal administration of approved radioactive products (e.g., 90Y, 177Lu, 131I) for therapeutic purposes. Used by clinicians to estimate absorbed radiation dose at the voxel level in normal tissues and tumors.
Regulatory Classification
Identification
A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.
Special Controls
*Classification.* Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).
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November 28, 2022
Voximetry, Incorporated % Lisa Pritchard VP. Regulatory, Quality, Clinical & Engineering DuVal & Associates. P.A. 825 Nicollet Mall Medical Arts Building # 1820 MINNEAPOLIS, MN 55402
Re: K220630
Trade/Device Name: Torch™ Software Regulation Number: 21 CFR 892.2050 Regulation Name: Medical Image Management And Processing System Regulatory Class: Class II Product Code: LLZ Dated: March 3, 2022 Received: March 4, 2022
Dear Lisa Pritchard:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
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Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
Daniel M. Krainak, Ph.D. Assistant Director Magnetic Resonance and Nuclear Medicine Team DHT 8C: Division of Radiological Imaging and Radiation Therapy Devices OHT8: Office of Radiological Health Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
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## Indications for Use
510(k) Number (if known) K220630
Device Name Torch™ Software
### Indications for Use (Describe)
Torch™ software is intended to provide estimates (deterministic) of absorbed radiation dose at the voxel level following internal administration of approved radioactive products. This is dependent on input data regarding being supplied to the application. Torch software only allows voxel-based dose calculations. For use with internally administered radioactive products. Torch should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing information for instructions.
| Type of Use (Select one or both, as applicable) | |
|------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------|
| <span style="text-decoration: overline;">☑</span> Prescription Use (Part 21 CFR 801 Subpart D) | <span style="text-decoration: overline;">☐</span> Over-The-Counter Use (21 CFR 801 Subpart C) |
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Image /page/3/Picture/1 description: The image shows the word "VOXIMETRY" in all caps and in a bold, sans-serif font. To the left of the word is a stylized flame in shades of orange and yellow. The flame appears to be coming from the top of the "V" in "VOXIMETRY". The overall design is clean and modern.
# 510(k) Summary
#### I. SUBMITTER
Voximetry, Inc. 8517 Excelsior Drive, Suite 207 Madison, WI 53717
Phone: 262.751.6441 Email:swallace@voximetry.com
Contact Person: Sue Wallace, PhD Date Prepared: November 25, 2022
#### II. DEVICE
Name of Device: Torch™ Software Common or Usual Name: Torch Classification Name: Radiological Image Processing System Regulatory Class: II (21 C.F.R. 892.2050) Product Code: LLZ
#### III. PREDICATE DEVICE
Primary: Voxel Dosimetry V1.0, K191216 This predicate has not been subject to a design-related recall.
Additional: OLINDA/EXM 2.0, K163687 This predicate has not been subject to a design-related recall.
#### IV. DEVICE DESCRIPTION
Torch is a software device for use in absorbed dose estimation of FDA approved radiopharmaceuticals. After administration of FDA approved radiopharmaceuticals, Torch provides post-treatment dosimetry evaluation on the voxel level using Monte Carlo techniques to assist the clinician in assessing absorbed dose to normal tissues and tumors.
> 8517 Excelsior Drive, Suite 207 Madison, WI www.voximetry.com
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Key functions of the Torch software include image import, image registration, contour propagation, pharmacokinetic modelling, and radiation transport modelling which is accelerated using parallel processing capabilities of Graphics Processing Units (GPUs). A Monte Carlo (MC) radiation transport algorithm is used to calculate absorbed dose with very high dosimetric accuracy.
Torch relies on medical images (e.g., positron emission tomography (PET), single photon emission computed tomography (SPECT), or computed tomography (CT)) which consist of a three-dimensional matrix of pixels, called voxels. Medical imaging acquired on the voxel level affords measurement of tissue heterogeneity and nonuniform source distribution of radiopharmaceuticals. Monte Carlo produces dose distributions at the voxel level with high precision.
#### V. INDICATIONS FOR USE
Torch™ software is intended to provide estimates (deterministic) of absorbed radiation dose at the voxel level following internal administration of approved radioactive products. This is dependent on input data regarding biodistribution being supplied to the application. Torch software only allows voxel-based dose calculations. For use with internally administered radioactive products. Torch should not be used to deviate from approved product dosing and administration instructions. Refer to the product's prescribing information for instructions.
### COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE VI. PREDICATE DEVICE
Both the subject and predicate devices are designed for use by physicians to estimate delivered dosing of radiopharmaceutical therapy at the voxel level. Both are software devices that receive inputs from radiological images to estimate absorbed dose using the well-established Monte Carlo method. At a high level, the subject and predicate devices are based on the following same technological characteristics:
- Radiological Image Processing System used to determine a delivered . radiopharmaceutical dose at the voxel level;
- Software device for prescription use in a professional environment ● (e.g., clinic, hospital);
- No patient contact;
- Parses SPECT/CT or PET/CT DICOM data; ●
- Provides image registration of different time points to a common ● reference study;
- Generates and integrates voxel-level time-activity curves; ●
- Conducts voxel-level absorbed dose calculations using a Monte Carlo . method;
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- Provides absorbed dose map in DICOM format; ●
- . Allows creation, transformation, and modification of contours/regions of interest to define objects in medical image volumes to support treatment calculation and evaluation;
- Supports computed tomography (CT), positron emission tomography . (PET), and single photon emission computed tomography (SPECT); and
- . Compatible radionuclide emissions include alphas, betas, and photons.
The following technological differences exist between the subject and predicate devices:
- . Calculation method for Beta particle emissions (Torch uses full Monte Carlo method, primary predicate uses local energy deposition); and
- Torch only supports therapeutic radionuclides and does not support . gamma emitting radionuclides which are used for imaging.
These minor differences in technological characteristics were determined to not raise any different questions of safety or effectiveness.
#### VII. PERFORMANCE DATA
Tests for verification and validation have been completed following Voximetry's design-control procedures. A risk analysis was completed, and risk controls implemented to mitigate identified hazards. Test results indicate that all specifications have met the acceptance criteria. As part of Verification, for each supported radionuclide (90Y, 177Lu, and 1311), Torch absorbed dose was computed in tumors and critical organs in the ICRP 110 Adult Male reference dataset and compared with the average results from three established Monte Carlo codes: EGS++ 2018, GATE 8.1, and GEANT4 10.5 (i.e. the OpenDose project). All results indicated agreement within 5%. Details are shown in the table below.
| Source Organ | Difference [%] = 100% * (Torch-OpenDose)/OpenDose | | |
|------------------|---------------------------------------------------|------|-------|
| | 131I | 90Y | 177Lu |
| Liver | -0.4 | 0.4 | -0.7 |
| Spleen | -1.3 | 0.3 | -0.6 |
| Right Kidney | -0.9 | 0.5 | 3.3 |
| Left Kidney | -1.3 | 0.5 | -0.6 |
| Lumbar Spongiosa | -4.5 | -4.2 | -4.4 |
| Left Lung | -0.8 | 1.8 | 2.1 |
Using the same phantom, additional testing was performed to validate the dose engine by computing total absorbed dose as well as separate dose contributions for each decay particle (alpha, beta, and gamma) and comparing to both OpenDose and the predicate device, OLINDA version 2.0.
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| Radionuclide | Statistic | Difference = 100% * (Torch-<br>Reference)/Reference | |
|--------------|-----------|-----------------------------------------------------|--------------|
| | | OpenDose | OLINDA |
| Lu-177 | Min | -5.21 | -0.75 |
| | Max | 3.27 | 3.39 |
| | Range | -5.21 - 3.27 | -0.75 - 3.39 |
| | Average | -0.23 | 0.44 |
| I-131 | Min | -5.59 | -2.78 |
| | Max | 0.81 | 3.15 |
| | Range | -5.59 - 0.81 | -2.78 - 3.15 |
| | Average | -1.12 | -0.21 |
| Y-90 | Min | -4.22 | -5.15 |
| | Max | 1.79 | 0.04 |
| | Range | -4.22 - 1.79 | -5.15 - 0.04 |
| | Average | -0.09 | -1.97 |
Total absorbed dose agreed with both references within 5%, and individual contributions agreed within 5.2%. Details are shown in the table below.
Again, using the same phantom, additional testing was performed to validate the dose engine by computing absorbed dose on a per-particle level for different source-target combinations, and comparing to OpenDose. Most results agreed with OpenDose computations within 1%, and all agreed within 5%.
Finally, Torch's Monte Carlo dose engine was tested against an established Monte Carlo dose algorithm (EGS) and physical film measurements, using 90Y and a custom-made puck-shaped film phantom developed in collaboration with the University of Wisconsin Accredited Dosimetry Calibration Laboratory. Torch agreed with measurements and previous calculations within 5%, as shown below.
| Depth<br>(mm) | Difference = 100% * (Torch-Reference)/Reference | |
|---------------|-------------------------------------------------|------|
| | Film Measurement | EGS |
| 0 | -1.67 | 1.82 |
| 0.9446 | 2.52 | 2.32 |
| 1.8892 | 4.25 | 2.23 |
| 2.8338 | 2.67 | 2.85 |
| 3.7784 | 2.54 | 1.60 |
No clinical studies were required for validation of the Torch software.
The testing results support that all the software specifications have met the acceptance criteria.
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#### VIII. Substantial Equivalence
Torch™ operates on a voxel level and performs dose calculation for photons and electrons based on patient specific CT scans using full-Monte Carlo (FMC) method while Voxel Dosimetry uses the semi-Monte Carlo method (SMC). Torch refers to the same patient population as Voxel Dosimetry, supports the same isotopes (Lu-177, I-131, Y-90) and has equivalent intended use.
OLINDA/EXM® v2.0 is based on the use of S-factors, which are calculated on patient-like phantoms using a Monte Carlo method. The S-factors are equal to the average absorbed dose to a target organ generated by a unit of activity in a source organ. OLINDA/EXM® v2.0 dose calculations can thus be performed by multiplying the source organ time-activity curve integral by the S-factor. The FMC method used in Torch™, on the other hand, operates on a voxel level and performs dose calculations for photons and electrons based on patient specific CT scans. Therefore, Torch™ is patient-specific and produces voxellevel dose-maps instead of average organ-level dose estimates as OLINDA/EXM® v2.0 provides. Voxel Dosimetry ™ can also perform accurate lesion dosimetry because doses are calculated on a voxellevel and the same method can be used for lesions as for organs. This is not possible with OLINDA/EXM® v2.0, with which only a rough estimate of lesion doses is possible. Torch refers to the same patient population as OLINDA/EXM® v2.0.
#### IX. CONCLUSIONS
In summary, Torch™ v1.0, has the same Intended Use and similar technological characteristics that do not raise different questions of safety or effectiveness compared to the predicate devices. Therefore, Torch™ v1.0 is substantially equivalent to a combination of the predicate devices Voxel Dosimetry™ v1.0 (K191216) and OLINDA/EXM® v2.0 (K163687) and supports its clinical effectiveness, safety and intended use.
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Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.