Frequently Asked Questions

Answers about AIRS Medical’s AI imaging solutions: SwiftMR® for faster MRI scans without compromising image quality, and SwiftSight for quantitative brain MRI and body composition analysis.

Product & technology

SwiftMR is a deep learning-based solution that reduces MRI scanning times. It shortens scan time by up to 50%, enhancing the patient experience and improving workflow efficiency.

A key advantage of SwiftMR is its ability to reduce scanning times without sacrificing image quality. It has been extensively validated in clinical settings and has reconstructed over 7 million scans across 1,700+ institutions worldwide.

SwiftMR uses a two-step process. First, it modifies the parameters of the MRI equipment to reduce the scanning time. This step shortens the scan, but on its own it produces images of lower quality than the original standard.

To address this, SwiftMR utilizes advanced deep learning technology, enhancing the images to meet — and in published reader studies, on several quality measures even surpass — their original quality. This innovative approach ensures efficiency in scanning time while upholding high standards of image quality.

We can modify nearly all parameters but primarily adjust parameters related to scan speed such as average, parallel imaging, and echo train length, among others. Occasionally, users may request not to change specific parameters, and in those instances, we respect their preferences and do not alter those settings.

Absolutely! SwiftMR remains beneficial even for modern, fast-imaging MRI equipment. SwiftMR is FDA-cleared to work in conjunction with vendor deep learning reconstruction solutions such as GE AIR Recon DL, and can further accelerate the scan time based on your current parameter settings. We optimize for additional acceleration, making it possible to achieve even faster imaging without compromising image quality.

Processing speed depends on network speed and the size of the study, but typically takes under 2 minutes per study. Larger studies like 3D may take longer.

This fits the typical imaging workflow: by the time a patient is moved off the MRI equipment and returns, the processed images are already on the PACS worklist — no disruption to the standard imaging procedure.

Yes, we provide a one-month free trial of SwiftMR. During the demo period, you have the opportunity to provide feedback and assess your satisfaction with the image quality. However, please note that as a policy, we do not offer demo licenses for more than one month. Therefore, it is advisable to schedule the demo when you can thoroughly assess its utility.

Yes, SwiftMR was developed with different image formation and reconstruction pipelines from different vendors in mind. If an input image is from a specific vendor, the output image will also retain the unique image characteristics of that vendor.

As part of ACR accreditation, centers need to submit MR images to be reviewed by radiologists, who assess image quality based on 5 categories (pulse sequence & image contrast, anatomic coverage & imaging planes, spatial & temporal resolution, artifacts, and exam identification). There are no concerns in using SwiftMR-processed images for ACR accreditation.

Performance

The amount by which imaging time can be reduced with SwiftMR varies slightly depending on the imaging area and imaging technique. However, based on collaborative research conducted with numerous university hospitals, it has been proven that a reduction in imaging time of up to 50% is possible while still allowing for proper interpretation by radiologists. SwiftMR is FDA 510(k)-cleared to support images with scan time reduction by up to 50%.

Yes, it is. SwiftMR is designed to restore images that are interpretable even with reduced imaging time. Furthermore, it has been validated for clinical effectiveness and safety through collaborative research with numerous university hospitals, including Seoul National University Hospital and Severance Hospital.

SwiftMR has been trained using pairs of images that display identical anatomy but vary in noise levels. This specialized training enables the SwiftMR algorithm to precisely identify and remove noise patterns while preserving anatomical structures, including lesions.

To verify this aspect, continuous collaborative research with numerous university hospitals is being conducted. So far, all published peer-reviewed SwiftMR studies to date have demonstrated at least "non-inferiority" compared to standard imaging.

Deep learning involves two key processes: training and inference. During the training phase, the model is 'trained' using pairs of images: one set captured in a short duration with low quality, and another set taken over a longer duration with high quality. The deep learning model learns to convert a low-quality image into a high-quality one. SwiftMR is a model that has been trained with approximately 3 million images from multiple vendors' scanners.

Once the training phase is complete, the model performs 'inference' to transform short-duration, low-quality images into their high-quality, long-duration counterparts. This deep learning process effectively enhances images that may appear coarse or blurred due to shorter scan times. It reduces noise in grainy images and converts low-resolution, blurred images into higher resolutions — not only restoring the original image quality but, in published studies, sometimes surpassing it.

Coverage

SwiftMR is compatible with all MRI vendors and scanner models, regardless of their age or magnet type, at field strengths from 0.25T to 3.0T — including open and upright MRI systems.

If you're looking to implement SwiftMR in MRI vendors other than GE, Siemens, Philips, Canon (Toshiba), Fujifilm (Hitachi), and Fonar, please reach out to our team!

SwiftMR coverage is determined by the sequences that make up each study. For instance, the vessel wall imaging order primarily consists of sequences such as 3D PDWI (SPACE), 3D T1WI (SPACE), CE 3D T1WI (SPACE), and so on, and all of these sequences are supported.

SwiftMR supports certain derivative images like MIP and ADC. In this case, the source image is processed, and the resulting image is sent to the PACS system.

Additionally, it is possible to send the reconstructed image back to the MRI scanner for further processing to generate derivative images.

SwiftMR supports images from pediatric patients. The validation dataset submitted for FDA 510(k) clearance includes pediatric data (ages 0–21).

Workflow

SwiftMR does not replace the original MR image. You can retain both the original MR image and the SwiftMR-processed image if you have sufficient storage capacity. However, it's important to note that the original image will be the accelerated one, since the protocols have been changed for the acceleration process.

We're introducing noise by reducing scan time, but not changing any artifact or anatomical information. This is a common and good question we get — yes, you will still be able to see it and discern it on the source images to know if you need to rescan. Please note that we're not adding signal from native images. We are simply removing noise, so naturally the signal will reveal itself.

Installation

The installation is carried out over a total of two days: (1) gateway PC installation and protocol setting will occur on the first day; (2) user training and monitoring will occur on the second day.

Protocol setting must be done on the actual MR console, and the working hours may vary depending on whether the equipment is in operation or not. User training begins along with actual use on patients, and we monitor to ensure there is no problem with the operation.

Additionally, in case of issues, you can contact the responsible customer success manager for immediate response. If the issue persists for an extended period, we recommend scanning using your previous method (original protocols + direct transmission to PACS) until the issue is resolved.

No, there is no such risk. SwiftMR operates within the safe parameters already supported by the equipment, and its time-saving approach does not strain the equipment. SwiftMR optimizes data acquisition, maintaining the quality of images while reducing the operational time. This efficiency can potentially extend the lifespan of the equipment for the same number of images captured.

Additionally, SwiftMR has been developed as a stand-alone system and is incapable of installing any software on or affecting the equipment in any way. The integration that enables image transfer between the equipment and SwiftMR adheres strictly to the existing DICOM image transmission protocol. From the MRI equipment's point of view, SwiftMR looks like a PACS server. There have been no reported cases of equipment malfunction attributable to SwiftMR in the 1,700+ institutions that have implemented it.

Legal, compliance & security

Patient information is not sent outside of the customer's network. For the cloud deployment, Protected Health Information (PHI) is separated from the image data and deidentified. Only the images are transmitted to the cloud server. Any information sent to our cloud server is deleted within 24 hours after processing.

SwiftMR adheres to domestic data protection laws, ensuring that patient information remains confidential and is not exposed externally. Both images and patient-level data are promptly anonymized or deleted after the required service period.

Furthermore, at AIRS Medical headquarters, we handle personal information, image data, and cloud database management in accordance with ISO/IEC 27001:2022 information security standards and patient data protection standards, alongside GDPR, HIPAA, and SOC 2 Type II attestations.

When the DICOM images are sent to the gateway PC, the gateway PC removes fields containing patient information (such as name and gender) following the Safe Harbor Method as defined in HIPAA before transmitting the image data to the cloud server.

An encrypted serial number is assigned before transmission to the server. Once the images are reconstructed on the server and sent back to the gateway PC, the patient information is recombined based on the serial number before storing the final MR image to the PACS.

There are no concerns about patient data being compromised because SwiftMR servers do not store patient personal information. Additionally, the transmission of anonymized image data to and from the cloud is supported by encryption protocols.

The cloud server is hosted on AWS, which adheres to not only ISO 27001 but also ISO 27017 and ISO 27018 standards. These regulations are internationally recognized as the most stringent for information security and personal data protection related to cloud services.

Image interpretation is a unique responsibility and authority of the interpreters, and the company does not take responsibility for this aspect. This is similar to how MRI equipment manufacturers do not assume responsibility for interpretation.

However, if there are situations where the core functionality of SwiftMR — image reconstruction — fails to work due to errors, resulting in the omission of image restoration or exceeding the examination time, we will promptly address and resolve the issue, preferably on the same day. If compensation is required due to such situations, we will engage in appropriate discussions to provide support.

Up to this point, we have not encountered any such issues, but to ensure flexible responses, we have also secured product liability insurance globally.

Competitors

When compared to competitors, SwiftMR has several strengths that can be summarized as follows:

(1) Vendor-neutral: SwiftMR can be applied to various MRI models without being limited by MRI manufacturers.

(2) Wide coverage range: SwiftMR is FDA-cleared for MR images of all body parts, with broad sequence coverage.

(3) Enhanced image quality: SwiftMR increases image quality by providing denoising and resolution enhancement at the same time.

Specific competitive advantages depending on the type of competitor are as follows:

Versus vendor-specific solutions developed by MRI equipment manufacturers — SwiftMR can be easily applied in hospitals that have more than two brands of MRI equipment, while vendor-specific solutions require separate investments for each brand. SwiftMR can also be applied to all MRI models without the need for hardware and software upgrades.

Versus vendor-neutral solutions applicable to multiple manufacturers — SwiftMR provides noise reduction and sharpness enhancement together across supported sequences, rather than denoising alone, and is FDA-cleared for MR images of all body parts. Coverage is determined by the sequences that make up each study — reach out to our team to review your protocol list.

Existing protocols are optimized with comprehensive consideration of parameters affecting scan time — not just the number of averages.

With the deep learning reconstruction engine for denoising and resolution enhancement always working in unison, higher acceleration can be achieved compared to having to choose between the trade-off of noise and resolution.

About MRI

Acquiring a high-quality MR image takes time. It is technically possible to scan faster, but that usually comes at the cost of image quality — and standards for acceptable image quality vary from one radiology center to another.

Reaching the image quality a facility expects typically requires a longer scan, so a single exam can run anywhere from 15 to 60 minutes. That hurts the patient experience and the operational efficiency of the radiology center.

Yes, reducing the scan duration is achievable by acquiring less information during the imaging process. This can be done by adjusting equipment console parameters such as lowering the 'Average' setting, increasing GRAPPA (or SENSE or CS), or decreasing the matrix size.

All of these methods, however, trade away image quality — the resulting images look coarser or more blurred than standard imaging. Shorter scan times have to be weighed against that compromise.

SwiftSight-Brain is AI-powered brain MRI quantification software. It automatically processes brain MRI scans to produce quantitative volumetric measurements and MS lesion analysis, then generates reports that are returned to PACS. It is designed to support clinicians and patients with objective imaging data.

SwiftSight-Brain is FDA 510(k)-cleared for automatic labelling, visualization, and volumetric quantification of segmentable brain structures and lesions from MR images, with volumetric data compared to reference percentile data.

SwiftSight-Brain segments the brain into anatomical subregions and calculates the volume of each. Those volumes are compared against an age- and sex-matched normative database and expressed as a percentile, giving clinicians an objective reference point for how a region compares to a healthy reference population.

Brain Age is a summary metric designed to make the volumetric analysis easier to interpret. It compares volumes of key regions (whole brain, cerebrum, cerebellum, the four lobes, and hippocampus) against age- and sex-matched references from the normative database, with each region weighted by its effect size — hippocampal volume, for example, carries more weight than occipital volume because it is more strongly associated with neurodegeneration. The full methodology is detailed in a forthcoming white paper.

Beyond standard lobar volumes, SwiftSight-Brain reports more granular structures such as the choroid plexus and detailed regional metrics, supporting research and advanced diagnostic evaluation by the clinician.

SwiftSight-Brain delivers three core outputs:

SwiftSight-Brain currently generates the following reports:

On FLAIR sequences, SwiftSight-Brain quantifies total lesion volume, tracks lesion change over time, identifies newly appearing lesions, and supports longitudinal progression tracking — giving clinicians quantitative measures to inform their own assessment of disease activity and treatment response.

SwiftSight-Brain requires two sequences:

SwiftSight-Brain analyzes and quantifies brain atrophy patterns that may be associated with various neurological disorders. These quantitative measures are intended to support — not replace — the clinician's own diagnosis.

When combined with SwiftMR, our solution has shown improved volumetric accuracy, and we are continuously enhancing the algorithm to further reduce variations across different vendors and scan parameters.

The normative database comprises 32,615 subjects, with broad representation from U.S. and worldwide populations. A white paper detailing the methodology is forthcoming.

No additional manual steps are required. Sites configure a SwiftSight series name once; the system then recognizes the scan automatically, generates the report, and routes it back to PACS.

Typical processing is 3–5 minutes, with parallel processing of multiple studies, so reports return to PACS without holding up the queue.

SwiftSight-Brain matches follow-up scans by PatientID and automatically generates volume-change and progression reports when prior studies are available — no manual setup per patient.

Compared to other solutions, SwiftSight-Brain offers automated series-based report routing (rather than sending images to different destinations), faster processing, parallel study handling, broader protocol tolerance, and patient-friendly reporting.

DTI plus volumetry is a planned capability.

Measurements

SwiftSight quantifies visceral and subcutaneous fat, abdominal and thigh muscle proportion, muscle quality, bilateral muscle balance, and liver fat. Results are combined into a structured body composition report with visual interpretation and peer benchmarking.

SwiftSight analyzes defined MRI images of the abdomen, thighs, and liver. Within these regions, it measures key fat and muscle compartments, including abdominal wall, psoas, paraspinal, quadriceps, hamstrings and adductors.

Weight and BMI do not show how fat and muscle are distributed. SwiftSight provides quantitative information about visceral fat, subcutaneous fat, muscle amount, muscle quality, muscle balance, and liver fat, helping distinguish patients who may have similar weight or BMI but very different body composition profiles.

SwiftSight measures intramuscular fat infiltration, or myosteatosis, within analyzed muscle groups. Lower fat infiltration corresponds with higher muscle quality, allowing muscle composition to be evaluated alongside muscle proportion.

SwiftSight separates abdominal adipose tissue into visceral fat, which is located deeper around the internal organs, and subcutaneous fat, which lies beneath the skin. Each can be quantified independently rather than being combined into a single body-fat measurement.

Yes. SwiftSight uses proton density fat fraction, or PDFF, to quantify liver fat. The software analyzes three regions of interest, calculates an average liver fat fraction, and provides a visual color map alongside the quantitative result.

Reports & tracking

SwiftSight generates a visual Body Composition Report summarizing key measurements such as muscle proportion, muscle quality, muscle balance, visceral fat, subcutaneous fat, and liver fat. Additional report pages show the analyzed anatomy and provide more detailed abdominal, thigh, and liver measurements.

SwiftSight compares measurements with reference populations matched by age and sex. Percentiles and visual expected and optimal ranges help providers and patients understand where an individual measurement sits relative to comparable people.

Yes. Because the measurements are quantitative, repeat MRI assessments can be used to evaluate changes in fat distribution, muscle composition, muscle quality, balance, and liver fat over time.

Programs & workflow

The capabilities can support metabolic assessment, healthy-aging programs, weight-management programs, rehabilitation, preventive imaging, and whole-body MRI services.

MRI does not use ionizing radiation and provides detailed soft-tissue visualization, allowing fat, muscle, and liver composition to be assessed within the same imaging-based body composition program.

Body composition can be incorporated into an existing whole-body, wellness, or preventive MRI program or used as part of a new service. MRI data are analyzed by SwiftSight, quantitative results are generated, and the resulting report can be incorporated into the center’s existing patient consultation and follow-up workflow.

Yes. SwiftMR can be integrated with the workflow to accelerate MRI acquisition while providing imaging data for SwiftSight quantitative analysis.

Still have questions?

Our clinical team is happy to walk you through how SwiftMR fits your scanners, protocols, and workflow. Book a virtual demo to review images together.