Recent advances in CT

The CT technology in recent times has focused greatly on reducing dose, increasing the clinical bandwidth and accuracy of detection, and reducing the burden on radiologists and technicians by workflow improvements.

There are two technology advances that have contributed immensely on dramatically improving on the above-mentioned objectives – (i) new detector technology and (ii) deep-learning-based algorithms, loosely called AI.

Across the spectrum of CT scanners, there are new detectors, such as Nano Elite detectors, which have reduced the electronic noise, improved image quality, and lowered the dose.

A lot of changes have happened like in the X-ray tube, which is called the heart of a CT scanner, and others like segmented anode, direct cooling, spiral groove technology, which adds trouble-free life of the tube, and consequently the life of the CT scanner system also.

On the top of that to reduce or minimize down time of the system, the latest X-ray tubes are coming with inbuilt chips, which register critical event happening inside the tube, like arcing, inside temperature, pressure, etc. The events are monitored remotely by a specialist, and action is taken proactively in case a failure is suspected in the near future. In current times, the advancement enables the X-ray tube and other CT scanner parts availability at sites in less time in order to maintain higher uptime, which earlier sometimes took more than a week’s time.

The most significant leap, however, has been with the spectral detector CT, which has not only reduced up to 70–80 percent, but also improved early detection of lesions and improved the clinical reach in oncology, emergency, pediatric imaging, and cardiac imaging. While CT exams can add to a patient’s lifetime exposure to ionizing radiation, they can also be more beneficial in cases where magnetic resonance imaging (MRI) or ultrasound might not be able to detect early-stage cancers. CT technology has gone beyond slice game and moved to spectral technology where spectral CT is delivering valuable clinical insights, such as improved tissue characterization and visualization or delineation of tumors.

For example, a patient received a CT scan after complaints of abdominal pain. The conventional images revealed a dilated duct. According to the site, this patient would need another ultrasound or MR scan to identify what was causing the dilation. Spectral results identified a tumor in the head of the pancreas, and the patient was referred for an endoscopic ultrasound biopsy.

Different results from spectral detector CT also help the radio­logist to reduce the repeated scans of patients and also reduc­tion of contrast to the patients, who cannot tolerate high iodine concentration, for example, a patient with kidney failure.

Conventional CT images showed a small pulmonary embolism in the right lower lung. To confirm, the clinician was able to utilize spectral images, such as iodine overlay and Z-effective, which are available every time a patient is scanned on the spectral detector CT. These always-on spectral results provided a clearer view and confirmation of the lower lung PE, as well as revealed a perfusion defect.

On the other hand, deep-learning algorithms and the computing power have enabled building applications, such as AI-based reconstruction techniques, which reduce the dose more than 50 percent, and help faster reporting for radiologists, making it more productive. The applications built with camera-based workflow support the technicians to prepare the patient faster and with fewer errors, reducing rescanning needs and productive workflows.