Using model-based iterative reconstruction, CT colonography can be a very low radiation dose method of screening. This article applauds the United States Preventive Services Task Force (USPSTF) approval, cited as a “big win for patients.”
Study concludes that ultralow-dose CT may substitute for standard-dose CT in some COPD patients
There are at least three different generations of iterative reconstruction, all of which enable substantial CT dose reductions without compromise of diagnostic power. While earlier versions of IR yielded 30% dose reductions, those with model-based IR or some blend thereof can result in 50-80% patient radiation dose reductions – with even better spatial and low contrast resolution. Access the full article on this study.
This article pretty well confirms what many have felt: model-based iterative reconstruction (MBIR) lowers radiation dose by 70-80% compared to adaptive statistical iterative reconstruction (ASIR), without loss of diagnostic power/information. While the images do indeed look different because there is much less noise and because of a slightly different pattern in the remaining noise, all the findings are there. Further, the anatomy and the findings are displayed as well or better.
So, in a young patient (under age 45) – especially if they are likely to be getting multiple exams – use of model-based iterative reconstruction is well worth the longer reconstruction time.
(To read more about CT enterography, Radiologyinfo.org is a great resource for patients.)
This article highlights that it is possible to achieve much lower radiation dose CT scans for commonly employed types of CT studies – the CT for urinary tract stones is one of the most common.
While not done everywhere, attention to detail can produce remarkable reductions in patient radiation without compromising diagnostic power.
Use of a lower kVp will actually make stones a bit brighter.
Careful attention to patient centering in the gantry can make a difference of up to 40% in dose.
And the use of iterative reconstruction techniques is now widely accepted to not compromise detection, yet with marked dose reduction – whether it be statistical iterative reconstruction, model based iterative reconstruction, or some blend of the two.
Radiologists and technologists both need to understand the importance of these tricks and the physics behind each.
This interesting paper talks about the use of iterative reconstruction to help lower the radiation dose of screening CT colonography.
Of course, as with all screening exams, the first order of priorities is to do no harm – hence the motivation to keep the radiation dose especially low.
The challenge is to lower dose without compromising diagnostic power.
For about the past two years, here at UW Medicine (Seattle) we have been using Model Based Iterative Reconstruction (VEO, GE Healthcare) for all our CT colonography exams. As recommended in this article, we also keep the kVp low – 80 or 100, which also helps to reduce the dose.
The result is a very low dose exam, but with excellent image quality and low image noise. This helps to make great coronal/sagittal reconstructions plus very nice 3D fly-through on the post-processing workstation.
This is a major advance as American healthcare evolves from reactive to preventive.
But a key to success in this lung cancer screening program is keeping the radiation dose of each exam as low as possible – certainly well below one mSv. Ideally, a low dose approach would involve model based or some other form of iterative reconstruction. All the other techniques to minimize dose should be employed together. Fortunately, this is an application where very low kVp will work well (70-100).
Next – and possibly even more impactful: coverage for screening CT colonography.
This very wise philosophy for implementing iterative dose reduction in any CT program was well presented at the recent MDCT meeting of the ISCT in San Francisco in June. A key component is to have regular and measurable ways for radiologists to regularly grade or score image quality as dose is ramped down slowly with increasing amounts of iterative reconstruction. With Model Based Iterative Reconstruction (MBIR), it may be possible to drop dose up to 60% compared to otherwise low dose adaptive statistical iterative reconstruction methods (ASIR) – but not in one jump. It takes time to get accustomed to the slightly different look of images with iterative reconstruction.
At least a month’s worth of experience should accrue before passing judgment on image quality. It is also important to guard against anecdotal cases used to render judgments, so experience over time is important. But with a methodical approach, a lot of progress can be achieved in overall dose reduction.
There are some who say that iterative reconstruction should be reserved only for younger patients and not used on older cancer patients who already have serious disease.
But many patients with malignancies are younger or are being treated for cure.
This article suggests that an iterative reconstruction technique (such as model-based iterative reconstruction, MBIR) which can reduce patient radiation dose by 50% may have salubrious utility in patients with lymphomas – who often are younger, who get multiple CT scans, and who are being treated for cure.
This may apply to other malignancies as well.
At the 2014 ISCT-sponsored MDCT meeting in San Francisco – dose reduction was a key theme during all four days.
Iterative reconstruction was a common theme of an overall dose reduction program. While adaptive statistical iterative reconstruction (ASIR) now has been well-shown to reduce average doses by up to 40% without impact on image quality, the hot topic was model-based iterative reconstruction (MBIR) in its various forms.
Consensus is now developing around MBIR being capable of 50-70% dose reductions incremental to adaptive statistical iterations. While image appearance may be somewhat different from that of filtered back projection, it is now pretty clear that such different appearance does not compromise diagnostic power. Indeed, with experience, some radiologists have developed a preference for the image appearance of MBIR.
CT to search for urinary tract stone is a very commonly performed procedure because both negative and positive results may have significant impact on subsequent patient care. Often the patients are younger since stones can occur at any age.
This article presents very encouraging news about significantly lowering the dose of a CT for urinary stones by using statistical iterative reconstruction – yet with acceptable image quality and no loss of diagnostic power.
This report adds to a rapidly growing body of data about both statistical iterative reconstruction and model based iterative reconstruction for various types of CT exams. This body of data almost uniformly reports substantial patient radiation dose reduction in the 30% to 60% range with equal or even better image quality.
We already know that low-dose CT is a valuable tool for reducing mortality rates, but now there’s evidence that it might reduce financial costs as well. A new analysis of the 2010 National Lung Screening Trial (NLST) shows that low-dose CT is a cost-effective diagnostic tool for patients at high-risk of lung cancer, according to AuntMinnie.com.
The Medical Imaging and Technology Alliance (MITA) released a statement saying the organization welcomes the analysis and “looks forward to ongoing collaboration with patient advocates and others in the imaging community to ensure access to this lifesaving technology.”
In my opinion, though, the key question in whether low-dose screening for lung cancer is cost effective is: what is the cost of working up the false positives? That cost needs to be subtracted from the cost benefit of the lives saved. This new analysis suggests that low-dose screening is indeed cost effective. One thing no cost analysis considers: the value of a negative exam to a very worried patient.
Further new twist: we now can do ultra-low-dose lung cancer screening using fully model-based iterative reconstruction techniques. This technique enables a 60 percent radiation dose reduction (down to the sub-0.5 mSv range) below that of even recent low-dose CT – further substantially decreasing any downside from lung cancer screening in high-risk patients.
A University of Washington study featured in the August issue of JAMA Pediatrics claims that 4 million annual pediatric CT scans of the head, spine, abdomen and pelvis are predicted to cause nearly 5,000 future cancers, according to HealthImaging.com. However, the study goes on to state that the risk can be mitigated by CT dose reduction and appropriate imaging initiatives which have the potential to prevent more than half of the projected radiation-related cancers. Practices like eliminating unnecessary scans and targeting high-dose scans are called out in the study.
I believe that the best way to reduce radiation dose from CT in children is to not do studies which are inappropriate or which have a very low chance of producing impactful diagnostic information. The next best way to reduce dose is to pay close attention to all the tricks of technique: accurate patient centering in the gantry, use of radiation shields, use of 80 or 100 kVp, minimizing Z axis scan length, etc. Then newer technology will greatly further reduce dose – automated tube current modulation, iterative reconstruction – especially fully model-based iterative reconstruction. Together these can reduce radiation dose by 70-80 percent. Scanning in kids above 6-8 mSv should be a thing of the past and sub-1.0 mSv scans should be common.
A new risk model for lung cancer was recently highlighted in the August 21 issue of Annals of Internal Medicine. According to the report, the Liverpool Lung Project (LLP) risk model was developed to determine, based on specific and sophisticated assessments, which individuals would benefit most from CT lung screening.
The LLP risk model has a strong ability to predict lung cancer, and, according to principal investigators, does so better than smoking duration or family history. In fact, this data has been confirmed by researchers from the University of Liverpool, as well as several U.K. centers, the U.S. National Cancer Institute, and the Harvard School of Public Health.
Unlike some other major diseases, like breast cancer and heart disease, lung cancer, thus far, has lacked adequate identification tools to determine which patients should be targeted to maximize screening benefits, and minimize its potential harms. Identification of those with the highest risk for lung cancer, a disease which now kills upwards of 1 million annually, will make the best use of the benefit-harm ratio.
Though other risk models have been created, none have been able to successfully apply to all of the world’s population. The LLP could overcome these challenges, though, as it accounts for important risk factors that others skip, including history of pneumonia, non-lung cancer, and asbestos exposure, among family history and smoking history.
The model certainly appears a good way to improve patient selection. As always, the key inscreening exams is to do no harm. Even for those patients deemed appropriate for screening by the LLP, the best approach is with ultra-low dose CT— such as done with model based iterative reconstruction.
To learn more about the LLP, please click here!