This year marked the 50th anniversary of computed tomography, and yet the technology continues to develop. Over the last year we’ve seen stories about improving workflow, bringing down costs, and the emergence of photon counting scanners. Here, presented in chronological order, are the ten biggest CT stories of the year from our Daily News online.

RaySearch to distribute Canon CT and PET/CT solutions in the U.S.
In April Canon Medical Systems USA announced plans to distribute its Aquilion Large Bore CT and Celestion PET/CT solutions through RaySearch Laboratories’ U.S. sales force.

The agreement builds on a partnership between the two, focused on offering U.S. providers an integrated radiation oncology portfolio that creates more efficient workflows for finding optimal cancer treatment plans.

“In this new phase of our collaboration, we look forward to working with RaySearch so that more customers and patients can benefit from our combined solution,” said Yuji Hamada, president and CEO of Canon Medical Systems USA, in a statement.

The Aquilion Large Bore CT offers the largest bore opening at 90 cm; a 70 cm field-of-view; and an 85 cm extended field of view. It weighs 660 lbs, which enables it to scan larger patients, and utilizes Adaptive Iterative Dose Reduction 3D (AIDR 3D) technology to automatically reduce radiation dosage while maintaining spatial resolution and image texture.

Equipped with a 90 cm CT bore as well, the Celestion PET/CT is designed to support the simulation of radiation treatment planning. It can extend its field of view from 70 cm to 85 cm and uses Canon’s SEMAR technology to reduce metallic artifacts and improve visualization of implants and adjacent soft tissues for clearer and more confident diagnosis.

In 2018, Canon and RaySearch struck up a collaboration to offer Canon’s imaging systems with RayStation, RaySearch’s treatment planning solution. Together, the solutions provide clinicians with significant pieces of information needed to develop treatment plans, including the size and location of the tumor, the direction of radiotherapy beams and appropriate isocenter locations.

The partnership also included RaySearch’s oncology information manager RayCare, which handles image management and workflow for virtual simulation; and RayStation Simulation, which creates a virtual simulation that incorporates patient modeling, isocenter placement, beam design and the exporting of patient marking systems.

Siemens unveils its fastest, single-source CT scanner, Somatom X.ceed
Siemens Healthineers introduced its fastest, single-source CT scanner, the Somatom X.ceed, in May of this year.

The high-speed and high-resolution system, which is FDA 510(k) pending, is designed to simplify procedures in the most challenging clinical areas, where time and precision are critical, including cardiac and emergency imaging and CT-guided interventions.

“As the number and complexity of radiological procedures increase, demands on staff are reaching heightened levels. This continues to cause unwarranted variation in both diagnostic and interventional procedures,” said Philipp Fischer, head of computed tomography at Siemens Healthineers.

Among the features the scanner offers is myExam Companion, which provides high speed and high spatial resolution to guide users through diagnostic procedures. It enables the system to use its power and fast rotation time of 0.25 seconds per rotation to their full potential, which is key for cardiac, emergency, and spectral imaging.

It also is equipped with myNeedle Companion, the first universal solution with a harmonized user interface that allows users to plan and guide percutaneous needle procedures across modalities, including CT and angiography. Included is myNeedle Laser, a fully integrated option that projects the needle entry point and insertion angle directly on a patient’s body, even in advanced double-angulated procedures with multiple needle paths.

Somatom X.ceed comes with an 82-centimeter bore, a 120 kW X-ray tube and a user-friendly iPad tablet operation. It supports dual-energy spectral imaging and with myExam Companion, can speed up emergency imaging workflow from patient preparation to image evaluation and provide ready to read results. The feature is also predicted to help standardize results and ensure low dose levels in cardiovascular imaging procedures.

The solution is expected to be fully cleared along with myNeedle Companion by RSNA 2021. Siemens expects to receive the CE mark by the end of the summer and initially plans to focus on sales in Europe and the U.S., followed by China and Asia-Pacific.

Philips launches spectral detector-based CT scanner
Also in May, Philips unveiled its latest innovation in precision diagnostics, its spectral detector-based Spectral CT 7500.

The intelligent system is designed to produce high quality spectral images for every patient on every scan to help improve disease characterization and reduce rescans and follow-ups, and does so at the same dose levels as conventional scans. The result is shorter scan time, more confident diagnoses and patients prescribed effective and more personalized treatment plans, according to Kees Wesdorp, chief business leader of precision diagnosis at Philips.

“This latest intelligent system helps to bring clarity to defining moments in healthcare by delivering on certainty, simplicity and reliability in every clinical area from cardiac care, to emergency radiology, diagnostic oncology, intervention and radiation oncology,” he said in a statement.

Spectral CT 7500 reduces time for diagnosis by 34%, repeat scans by 25% and follow-up scans by 30%. This helps offset the need for unnecessary, suboptimal and repeat imaging, which adds up to as much as $12 billion a year.

Fully integrated into hospital workflow, the solution enables the technologist to perform spectral chest and head scans in less than one second and full upper body spectral scans in less than two seconds. Its higher sensitivity allows it to more accurately detect malignant findings and improves readings of incidental findings. In addition, it can use photons to help salvage suboptimal injection scans without the need to rescan patients, thereby shortening the time it takes to diagnose a condition.

Spectral CT 7500 enables users to serve additional patient populations they could not before, from pediatric to bariatric, and for any clinical indication. This includes challenging cardiac scans with high and irregular heart rates. It also can optimize reading with rich spectral results and AI-based smart tools available in any reading environment with Spectral Magic Glass on PACS.

“Conventional CT scanners are limited and can only show us where things are located — like lesions, cysts, bleeds, fractures and more. Philips spectral detector-based systems help to characterize what the finding is, not just where it is, providing us greater confidence in diagnoses,” said Dr. Finn Rasmussen, associate professor and a consultant radiologist at Aarhus University.

Lean management decreases CT acquisition length, saves on exam time
Emory Healthcare announced in May that it is employing lean management principles to decrease CT acquisition time and save its radiological technologists hundreds of hours in performing exams.

A method for managing and organizing work, lean management aims to improve a company’s performance, particularly the quality and profitability of its production processes. The hospital system found that implementing it brought the number of emergency CT exams completed within two hours up to 71% and saved it six weeks of annualized rad tech time.

“The lessons we learned using these lean management tools may benefit other organizations facing similar challenges,” wrote quality program manager, Dr. Pratik Rachh and his colleagues in their study.

Advanced imaging volumes in the ED created significant patient throughput challenges for Emory Healthcare.

Forming a multidisciplinary team of physicians, nurses, technologists, administrators and a patient family advisor, the healthcare system implemented several changes and began tracking process metrics around the length of time for CT exams and monthly media turnaround time. A phased rollout over half a year saw turnaround time improve, but only for six weeks. Eliminating inefficiencies, however, caused median turnaround times to fall from between 90 and 109 minutes to 82 and 106 minutes. In addition, rad techs saved roughly 268 hours of annualized time.

Racch attributes the lack of sustainment to staff that went on family and medical leave, and said staffing constraints made it difficult to adjust to surging demand when the ED received an influx of patients.

“Sustaining improvements has been challenging. However, the lessons learned established a collaborative ED and radiology department partnership that continues to work on this complex challenge of optimizing ED CT [turnaround times],” he and his colleagues wrote.

The findings were published in RadioGraphics.

Detecting cancer and heart disease in a single low-dose CT scan with AI
Clinicians at Massachusetts General Hospital and engineers at Rensselaer Polytechnic Institute have developed a deep learning algorithm capable of screening for cardiovascular disease risk and cancer from the same scan.

Applied to low-dose CT, the AI solution is designed to expedite the diagnosis process, accelerate treatment and improve patient outcomes, while eliminating the need for additional scanning and subsequently, more radiation exposure.

“Recent studies have shown that the patients diagnosed with cancer have a much greater risk of CVD mortality than the general population. Nevertheless, when the cancer risk population receives cancer screening, their potential CVD risk may be overlooked. Our work shows that deep learning can convert LDCT for lung cancer screening into a dual-screening quantitative tool for CVD risk estimation for this high-risk group,” Pingkun Yan, an assistant professor of biomedical engineering and member of the Center for Biotechnology and Interdisciplinary Studies (CBIS) at Rensselaer, told HCB News in May.

Developed at Rensselaer and tested at Massachusetts General Hospital, the algorithm proved highly effective in analyzing the risk for cardiovascular disease and related mortality in high-risk patients undergoing low-dose CT, and was equally sufficient as radiologists in analyzing these images. It also nearly mirrored the performance of dedicated cardiac CT scans when applied to an independent data set collected from 335 patients.

The solution was developed, trained and validated on data from more than 30,000 low-dose CT images from a large data set from the National Lung Screening Trial (NLST), which has affirmed that early detection and treatment with low-dose CT can reduce lung cancer mortality. Its design included making sure it could filter out unwanted artifacts and noise and extract features needed for diagnosis. Researchers also validated it using another 2,085 NLST images.

Heart disease and cancer share common risk factors, including tobacco use, diet, blood pressure and obesity. A multi-institutional study from 2019 found cancer put patients at higher risk of dying from cardiovascular disease based on an analysis of three million cancer cases and 28 different types of malignancies.

Yan and his colleagues are planning more multi-vendor, multi-site studies to further validate the algorithm.

The findings were published in Natures Communications.

Researchers fuse MR and CT images using deep learning technique
Over the summer, researchers in China began touting a new deep learning-based process that “fuses” multi-modal scans to create a higher quality medical image that can improve clinical diagnosis and patient outcomes.

Known as image fusion, the technique automatically identifies and combines information of scans from different modalities to produce a single high-quality image. “Experimental results indicate that the proposed method achieves state-of-the-art performance in terms of both visual quality and quantitative evaluation metrics,” said author Yi Li, with Qingdao University’s College of Data Science and Software Engineering in Qingdao, China, in a statement.

Li and his colleagues used the technique to fuse MR, CT and SPECT images to build an image training database. The database was then used to fuse medical images in batches, with the newly fused images appearing more natural and with sharper edges and higher resolutions. Additionally, detailed information and features of interest were better preserved to some extent, while key information was clearly contrasted and the virtual shadow was effectively removed.

The end result, according to the researchers, is a single image that contains most of the information in the two multi-modal images of CT and MR or MR and SPECT, thereby making up for the deficiencies of each of these images alone.

“The increase in the amount of information undoubtedly brings changes to the improvement of medical imaging diagnoses. More valuable information can be used to support effective diagnosis. It also brings about possibilities for the study of 'automatic diagnosis' technology and conducts tentative research,” wrote the researchers in their study.

Their findings were published in the June issue of the International Journal of Cognitive Computing in Engineering.

First fully mobile AI-powered CT unit launches in West Virginia for lung cancer screening
The WVU Cancer Institute and WVU Medicine announced in August that they are taking lung cancer screening on the road to residents in West Virginia with the launch of their new mobile unit, LUCAS.

LUCAS is the first fully mobile AI-powered CT unit for low-dose lung cancer screening in the U.S. It is capable of traveling statewide without relying on facility-based power, a quality that is expected to improve West Virginians’ access to lung cancer screenings.

“Because we wanted to go to rural areas around West Virginia that may not have facility power sources, it was important that LUCAS have an independent power supply. There is a separate generator that travels as part of the unit. By providing continuous power, we can maintain high throughput,” Dr. Kyle Chapman, lead pulmonologist for lung cancer screening at the WVU Cancer Institute and medical director of LUCAS, told HCB News.

The unit will provide scanning in all 42 of West Virginia’s counties, serving up to 20 patients a day amid COVID-19 protocols. It will refer patients in need of follow-up to facilities closest to their homes. Those without insurance will not be turned away, with grant funds and donations available to pay for their screenings.

The unit incorporates AI-based capabilities by Canon that help decrease its radiation doses by as much as 82% relative to the standard of care. The design for LUCAS is based on the performance and infrastructure of Bonnie’s Bus, which has completed more than 23,000 screening mammograms and identified more than 110 cases of breast cancer since 2009.

Its service is expected to reduce the number of lung cancer diagnoses and related deaths, which the 2019 West Virginia Cancer Burden Report put at approximately 2,047 and 1,460 a year, respectively. Currently, only 22% are diagnosed before cancer has spread, while half that are already have distant metastasis.

“Our hope is that LUCAS will serve as a pilot project. After our launch, we can see what works well and identify areas for improvement before building similar models for expansion, especially in other rural areas,” said Chapman.

Collaborating with the program is Canon Medical Systems, USA and the Lung Cancer Initiative at Johnson & Johnson.

LUCAS will start traveling around West Virginia this month.

Canon enters photon-counting CT partnerships in Japan
In September, Canon Medical Systems announced it had teamed up with the National Cancer Center Japan and EAST Hospital in Kashiwa to further research on the use of photon-counting CT (PCCT).

PCCT is an advanced diagnostic imaging technique designed to identify multiple material components by measuring X-ray photons at different energy levels. Doing so enables it to enhance image resolution and quality, while reducing dose exposure compared to conventional CT.

Canon and the center’s Exploratory Oncology Research & Clinical Trial Center (NCC-EPOC) will use the quantitative capabilities of photon counting CT to assess treatment effects from chemotherapeutic agents on malignant tumors. They will also analyze different tissue characteristics for clinical insights on a wide range of medical fields.

"We hope our PCCT system will bring new clinical benefits to patients all over the world. These benefits are based on the identification of multiple material components, the higher resolution images, and lower exposure doses," Tomokazu Harada, a senior manager of CT marketing and promotion department for Canon Medical Systems, told HCB News.

Global CT market to hit $6.14 billion by 2025
The CT market is on its way to being worth $6.14 billion by 2025, with demand driven by providers worldwide looking to upgrade from low-end CT to mid- and high-end CT scanners, according to a September report from Frost & Sullivan.

Currently valued at $4.16 billion, the consulting firm predicts opportunities in the market for traditional and mobile CT, cardiac scanning and AI-powered imaging for cancer diagnostics. It is already witnessing an expansion in clinical applications and new technological innovations being developed, and puts the compound annual growth rate for the rise at 8.1%.

"Due to a large existing installed base of scanners with fewer than 16 slices in emerging and developing economies purchased between 2012 and 2014, pent-up demand for CT replacements propels the growth of higher-slice CT. During the COVID-19 pandemic, mobile CT has potentially influenced purchase and usage to assess lung infection. In the stationary CT, 64 slice CT scanners have a better penetration rate," Poornima Srinivasan, Frost & Sullivan consultant for healthcare & lifesciences, told HCB News.

Driving the trend for these high-end technology scanners are cardiac, neurology and liver imaging, with university and academic centers and public hospitals in North America, Western Europe, and Japan expected to propel revenue for this segment. Demand for 16- to 64-slice scanners are also expected to grow moderately at diagnostic imaging centers in developing regions of LATAM, India and China.

An estimated 375 million CT procedures are performed annually and grow 3%-4% per year. Demand significantly increased in the wake of the pandemic as a majority of countries immediately seeking out and purchasing CT equipment for lung screenings. Despite these new purchases, capacity for traditional unit shipments did not reach its full potential annually. But as a result, Frost & Sullivan expects there to be pent-up demand for CTs to fulfill needs-based requirements.

New applications at play include photon-counting detector technology, machine learning, deep learning and spectral imaging, according to the report. AI-powered CT, in particular, is expected to make a splash by enhancing cancer detection and helping to better handle large volumes of patients. Frost & Sullivan projects regulatory approvals in the next year or two, with a significant uptake by 2025. It advises that CT manufacturers be transparent and flexible in their prices for such devices and partner with startups to reap the benefits early on.

Frost & Sullivan sees both traditional and mobile CT increasing in use, with mobile increasing the number of participants in the market. Top manufacturers that offer a broad range of CTs will especially benefit, it says. Reimbursement approval from current CPT and European regulations are expected to push CT into the cardiac realm. Mayo Clinic in Rochester, in fact, recently performed the first cardiac scan with a new photon-counting detector CT. With it, providers were able to see clear images of the heart and blood vessels, and could even capture a small fraction of one heartbeat by "freezing" the motion.

Siemens gets first FDA clearance for photon-counting CT in US
Marking the first clearance in nearly a decade for any significant advancement in CT, the FDA gave the nod to Siemens' photon-counting scanner NAEOTOM Alpha in October.

Unlike conventional CT scanners, which uses detectors to measure total energy contained in many X-rays at one time, photon-counting detectors measure each individual X-ray that passes through a patient’s body. By "counting" each individual X-ray photon, the scanner can collect more detailed information about the patient. It can then form images that reflect the most useful information to help clinicians make better decisions around diagnosis and treatment, and personalize care.

NAEOTOM Alpha is designed to use photon-counting to produce detailed 3D images. The images can be used to train physicians in diagnosing patients and by staff to make diagnoses, prepare treatment, and in radiation therapy planning.

“Today’s action represents the first major new technology for computed tomography imaging in nearly a decade and underscores the FDA’s efforts to encourage innovation in areas of scientific and diagnostic progress,” Laurel Burk, assistant director of the diagnostic X-ray systems team in the FDA’s Center for Devices and Radiological Health, said in a statement.

In addition to collecting more information on patients, photon-counting’s higher contrast-to-noise ratio enhances image resolution and quality, while reducing dose exposure, and corrects artifacts. It also creates access to quantitative imaging capabilities, which provides more numerical data on tumors and signs of cancer for more personalized care.

That’s why Canon recently teamed up with the National Cancer Center Japan and EAST Hospital in Kashiwa to research the use of photon-counting CT. Together, they will use the its quantitative capabilities to assess treatment effects from chemotherapeutic agents on malignant tumors and analyze different tissue characteristics for clinical insights in a wide range of medical fields.

Mayo Clinic in Rochester, Minnesota also recently used it for the first time in cardiac scanning as part of a collaboration with Siemens Healthineers. The technology is fast enough to provide clear images of the heart and blood vessels and can capture a small fraction of one heartbeat by "freezing" the motion.

For Mayo, this was a third-generation research system incorporating the technology. The first photon-counting detector CT system there and in the world was installed in 2014, with the first research studies in humans beginning in August 2015. A second-generation prototype was installed in 2020 and addressed many of the design limitations of the first, including a 20% reduction in slice thickness, a greater field of view, better data handling speeds and the ability to perform image reconstruction online. The third now offers cardiac gating capabilities.

Massachusetts General Hospital also just incorporated photon-counting in a new research pilot program in NeuroLogica’s OmniTom Elite CT with Photon Counting Detector technology. The technology will be used in ICU settings to help diagnose patients at the point-of-care, while limiting the need for extra transport.

Siemens NAEOTOM Alpha was evaluated on the 510(k) premarket clearance pathway.