Category Archives: Imaging

Imaging

Updated: How To Detect Bucket Handle Injuries With CT

Leave a comment

A bucket-handle injury is a relatively uncommon complication of blunt trauma to the abdomen. It only occurs in a few percent of patients, but is much more likely if they have a seat belt sign.  The basic pathology is that the bowel mesentery (small bowel of sigmoid colon) gets pulled away from the intestinal wall.

This injury is problematic because it may take a few days for the bowel itself to die and perforate. Patients with no other injuries could potentially be discharged from the hospital before they become overtly symptomatic, leading to delayed treatment.

Here’s an image from my personal collection with not one, but four bucket-handle injuries.

Typical patients with suspected blunt intestinal injury are observed with good serial exams and a daily WBC count. If this begins to rise after 24 hours, there is a reasonable chance that this injury is present.

CT scan has not really been that reliable in past studies. There may be some “dirty mesentery”, which is contused and has a hematoma within it. But without a more convincing exam, it is difficult to convince yourself to operate immediately on these patients.

A paper was published by a group of radiologists at Duke University. It appears to be a case report disguised as a descriptive paper. It looks like they picked a few known bucket-handle injuries from their institution and back-correlated them with CT findings.

The authors called out the usual culprits:

  • Fluid between loops of bowel
  • Active bleeding in the mesentery
  • Bowel wall perfusion defects

But they also noted that traumatic abdominal wall hernias were highly associated with seat belt sign as well. These are rare, but should bring intestinal injury to mind when seen.

With newer scanners, radiologists are better able to detect subtle areas of hypoperfusion as well. This is a fairly good indicator of injury, especially when adjacent bowel appears normally perfused. Here are two examples. The black arrows denote active extravasation, and the white ones an area of hypoperfusion.

The authors add bowel wall hypoperfusion as another finding that may point to a bucket-handle type injury.

A recent paper demonstrates the value of the current generation of high-quality scanners. A collection of California and Denver centers implemented a multicenter, prospective, observational study of patients with seat belt signs. The developed a list of positive findings, which included:

  • abdominal wall soft tissue contusion (radiographic seat belt sign)
  • free peritoneal fluid
  • bowel wall thickening
  • mesenteric stranding
  • mesenteric hematoma
  • bowel dilation
  • pneumatosis
  • pneumoperitoneum

A total of 754 patients with visible seat belt sign were enrolled and all went to CT scan. Any of the findings listed above were associated with a statistically significant likelihood of hollow viscus injury. The highest likelihood was associated with:

  • free peritoneal fluid – 42x more likely
  • bowel dilation – 21x
  • free fluid with no solid organ injury – 20x
  • bowel wall thickening – 19x
  • radiographic seat belt sign – 3x

Any of the radiographic findings strongly suggested that an injury could be present. However, if none were present, it was very unlikely that there were any significant injuries. The authors suggested that if such patients had no other injuries requiring hospitalization, they could potentially be discharged home. However, those patients should be counseled to return for evaluation immediately if they have any change in their abdominal or systemic status.

Bottom line: Some patients with a visible seat belt sign might be eligible for discharge from the ED if they have a totally negative abdominal CT and no other injuries requiring hospitalization. If they have any finding, they should be admitted for observation.

If your patient has an unconcerning exam and any of the findings listed above, perform serial exams and get a WBC the next morning. If the exam worsens, operate. If the WBC rises, consider laparoscopy to see if you need to make a bigger incision. And if you see any evidence of hypoperfused bowel, consider laparoscopy right away. 

References:

  • Excluding Hollow Viscus Injury for Abdominal Seat Belt Sign Using Computed Tomography. JAMA Surg. 2022 Sep 1;157(9):771-778. doi: 10.1001/jamasurg.2022.2770. PMID: 35830194; PMCID: PMC9280606.
  • CT findings of traumatic bucket-handle mesenteric injuries. Am J Radiol 209:W360-@364, 2017.
  • Multidetector CT of blunt abdominal trauma. Radiology 265(3):678–693, 2012.
Print Friendly, PDF & Email
Imaging

More On MRI And External Fixators

Leave a comment

I’ve covered the problem of performing MRI on patients with external fixators. This is typically a problem that arises in head-injured patients with extremity or pelvic fixators for concomitant fractures.

MRI is an indispensable tool for the evaluation of head, spine, and soft tissue trauma. However, a great deal of effort is required to ensure that any patient scheduled for this test is “MRI compatible.” The fear is that any retained metallic fragments may move or heat up once the magnets are activated.

But what about trauma patients with external fixators? That is one big hunk of metal inserted deep into your patient. There are three major concerns:

  • Is the material ferromagnetic? If so, it will move when the magnets are activated and may cause internal injury. These days, many fixator sets are not ferromagnetic, avoiding this problem.
  • Can currents be induced in the material, causing heating? This is not much of a problem for small, isolated objects. However, external fixators are configured so that current loops can be created. The fluctuating magnetic fields can induce currents that, in turn, will heat the surrounding tissue. And thinner materials (narrow pins) result in more current and heating.
  • Will the metal degrade image quality?

Thankfully, there is a continuing trickle of evidence that is accumulating to give us some guidance. One paper from 2017 described a retrospective case series from four trauma centers. The authors performed MRIs on 38 patients with 44 external fixators. The adverse events they monitored for were catastrophic hardware pullout, thermal injury to the skin, field distortions that impaired the images, and damage to the magnet casing.

Twelve patients with 13 external fixators had MIR performed with the hardware inside the MRI bore, and 27 patients had the study with the fixator outside the bore. Most MRIs were performed to evaluate the cervical spine. There were no adverse events.

A recent Massachusetts General Hospital study involved a larger group (97 patients with 110 fixators). The fixators were located on the ankles, knee, femur, and pelvis. Most were performed on a 1.5T MRI, although a few were done on a 3T machine. Again, most scans were performed for head or cervical spine evaluation. Two of the 97 studies were terminated early due to patient discomfort. In both cases, the frame was outside the MRI bore.

The biggest challenge in our clinical practice is that there is no standard ex-fix configuration. Our orthopedic colleagues get to unleash their creativity, trying to devise the appropriate architecture to hold bones together so they can heal properly. This makes developing standardized guidelines regarding what can and can’t go into the scanner difficult.

We do know from clinical simulation studies that heating is influenced by ex-fix configuration. Increasing pin depth (thicker extremities) and closer pin spacing produces smaller temperature rises. For example, pins placed in a 15cm bar at a depth of 11cm produced a temperature rise of 2 degrees, but pins placed along a 30cm bar at a depth of 2cm showed a rise of 6 degrees.

However, a growing body of literature shows that the heating effects are relatively small and get smaller as the distance from the magnet increases. And non-ferromagnetic materials move very little, if at all, and do not interfere with the image. So as long as nonferromagnetic materials are used, the patients are probably safe as long as basic principles are adhered to:

  • Other diagnostic options should be considered and/or exhausted prior to using MRI.
  • Informed consent must be obtained, explaining that the potential risks are not completely understood.
  • The fixator must be tested with a handheld magnet so that all ferromagnetic components can be identified and removed.
  • All traction bows must be removed.
  • Ice bags or cooling packs should be placed at all skin-pin interfaces.
  • The external fixator should remain at least 7cm outside the bore at all times, if possible. If any portion must be inside the bore, monitoring efforts should be stepped up even more.

Bottom line: MRI of patients with external fixators can be safely accomplished. Consult your radiologists and physicists to develop a policy that is specific to the scanners used at your hospital. 

References:

  1. Magnetic Resonance Imaging of Trauma Patients Treated With Contemporary External Fixation Devices: A Multicenter Case Series. Journal of Orthopaedic Trauma, 31 (11), e375-e380. doi: 10.1097/BOT.0000000000000954.
  2. Magnetic Resonance Imaging of Trauma Patients Treated With Contemporary External Fixation Devices: A Multicenter Case Series. J Orthop Trauma. 2017 Nov;31(11):e375-e380. doi: 10.1097/BOT.0000000000000954. PMID: 28827510.

 

Print Friendly, PDF & Email
Imaging, New technology

New Technology: Using AI To Interpret Pelvic X-rays

Leave a comment

Look out, radiologists! The computers are coming for you!

Radiologists use their extensive understanding of human anatomy and combine it with subtle findings they see on x-ray shadow pictures. In doing this, they can identify a wide variety of diseases, anomalies, and injuries. But as we have seen with vision systems and game playing (think chess), computers are getting pretty good at doing this as well.

Is it only a matter of time until computer artificial intelligence (AI) starts reading x-rays?  Look at how good they already are at interpreting EKGs. The trauma group at Stanford paired up with the Chang Gung Memorial Hospital in Taiwan to test the use of AI for interpreting images to identify a specific set of common pelvic fractures.

The Stanford group used a deep learning neural network (XCeption) to analyze source x-rays (standard A-P pelvis images) from Chang Gung. These x-rays were divided into training and testing cohorts. The authors also applied different degrees of blurring, brightness, rotation, and contrast adjustment to the training set in order to help the AI overcome these issues when interpreting novel images.

The AI interpreted the test images with a very high degree of sensitivity, specificity, accuracy, and predictive values, with all of them over 0.90. The algorithms generated a “heat map” that showed the areas that were suspicious for fracture. Here are some examples with the original x-ray on the left and the heat map on the right:

The top row shows a femoral neck fracture, the middle row an intertrochanteric fracture, and the bottom row another femoral neck fracture with a contralateral implant. All were handily identified by the AI.

AI applications are usually only as good as their training sets. In general, the bigger the better so they can gain a broader experience for more accurate interpretation. So it is possible that uncommon, subtle fractures could be missed. But remember, artificial intelligence is meant to supplement the radiologist, not replace him or her. You can all breathe more easily now.

This technology has the potential for broader use in radiographic interpretation. In my mind, the best way to use it is to first let the radiologist read the images as they usually do. Once they have done this, then turn on the heat map so they can see any additional anomalies the AI has found. They can then use this information to supplement the initial interpretation.

Expect to see more work like this in the future. I predict that, ultimately, the picture archiving and communications systems (PACS) software providers will build this into their product. As the digital images are moving from the imaging hardware to the digital storage media, the AI can intercept it and begin the augmented interpretation process. The radiologist will then be able to turn on the heat map as soon as the images arrive on their workstation.

Stay tuned! I’m sure there is more like this to come!

Reference: Practical computer vision application to detect hip fractures on pelvic X-rays: a bi-institutional study.  Trauma Surgery and Acute Care Open 6(1), http://dx.doi.org/10.1136/tsaco-2021-000705.

Print Friendly, PDF & Email
Abstracts, Imaging

Best Of EAST 2023 #9: CT Of The Lumbar Spine

Leave a comment

It is becoming clearer and clearer that patients with suspicion for fractures of the thoracic (T) and lumbar (L) spine should be imaged only with CT scan. Conventional imaging just doesn’t have enough sensitivity, even in younger patients with healthy bones. But when we obtain CT of the T&L spines there is a choice: just look at the axial / helical slices, or have the computer reconstruct additional images in the sagittal and coronal planes. The belief is that this multiplanar imaging will assist in finding subtle fractures that might not be seen on axial views.

The group at Rutgers in New Jersey tried to determine if adding the reconstructions amounted to overkill. They performed a retrospective review of patients at their Level I center over a six-year period. They focused on studies performed in patients who had T and/or L fractures who also had both CT of the chest, abdomen, and pelvis (CAP) and thoracic and lumbar reconstructions. Additional data were obtained from a review of the medical record and trauma registry.

Here are the factoids:

  • A total of 494 patients had both CAP and reconstructions
  • There were 1254 fractures seen on CAP, and an additional 129 fractures seen with recons (total of 1394)
  • The majority of additional injuries not detected on CT CAP were transverse process fractures
  • The number of other fracture patterns not seen on CT CAP were statistically “not significant”
  • However, these numerically “not significant” fractures included 51 vertebral body fractures, 6 burst fractures, 3 facet fractures, and 2 pedicle fractures
  • No unstable fractures were missed on CT CAP
  • More MRIs were performed in the patients who had recons, there were more spine consultations, and 11% underwent operative fixation vs. 2% for CTA only (!!)

The authors concluded that CT CAP alone was sufficient to identify clinically significant thoracic and lumbar fractures. They also stated that clinically insignificant injuries identified with reconstructions were more likely to undergo MRI and use excess resources. They urged us to be selective with the use of T&L reformats.

Bottom line: Wow! I have a lot of questions about this abstract! And I really disagree with the findings.

You studied fewer than 500 patients with T or L spine fractures over a six year period. This is only about 80 per year, which seems very low. This suggests that many, many patients were being scanned without recons to start with. How did patients get selected out to get recons? Were there specific criteria? I worry that this could add some bias to your study.

The number of fractures seen only on the recon views besides transverse process fractures were deemed “statistically insignificant.” However, looking at the list of them (see bullet point 5 above) they don’t look clinically insignificant. It’s no wonder that recons resulted in more consults, MRI scans, and spine operations!

I worry that your conclusion is telling us to stop looking for fractures so we won’t use so many additional resources. But their use may be in the best interest of the patients!

Here are my questions and comments for the presenter/authors:

  • Why did you decide to do this study? I didn’t realize that not doing the recons was a thing in major blunt trauma. Was there some concern that resources were being wasted? Was there an additional cost for the reconstructions?
  • How many patients only received CT CAP? The greater the number of these, the higher the probability that some non-random selection process is going on that might bias your findings.
  • How did you get separate reports for the non-reconstructed images? Did you have new reads by separate radiologists? Typically, the report contains the impression for the entire study. It would be unusual for the radiologist to comment on the non-recon images, then add additional findings from just the reconstructions.
  • Doesn’t the increased numbers of spine consults, MRIs, and operative procedures in the patients with reconstructions imply that these otherwise occult fractures needed clinically important additional attention? 

I worry that readers of this abstract might take away the wrong message. Unless there is some additional compelling data presented, this study is certainly not enough to make me change my practice!

Reference: UTILITY OF CT THORACOLUMBAR SPINAL RECONSTRUCTION IMAGING IN BLUNT TRAUMA. EAST 2023 Podium paper #20.

Print Friendly, PDF & Email
Abstracts, Imaging

Best Of EAST 2023 #8: Use Of AI To Detect Rib Fractures On CT

Leave a comment

Artificial intelligence systems (AI) are increasingly finding their way into medical practice. They have been used to assist pathologists in screening microscope specimens for years. Although still amazingly complicated, one of the most obvious applications for trauma is in reading x-rays. Counting rib fractures may be helpful for care planning, and characterizing fracture patterns may assist our orthopedic colleagues in evaluating and planning rib plating procedures.

The trauma group at Stanford developed a computer vision system to assist in identifying fractures and their percent displacement.  They used a variation on a neural network deep learning system and trained it on a publicly available CT scan dataset.  They used an index of radiographic similarity (DICE score) to test how well their model matched up against the reading of an actual radiologist.

Here are the factoids:

  • The AI network was trained on a dataset of 5,000 images in 660 chest CT scans that had been annotated by radiologists
  • The model achieved a DICE score of 0.88 after training
  • With a little jiggering of the model (reweighting), the receiver operating characteristic curve improved to 0.99, which is nearly perfect

The left side shows a CT scan rotated 90 degrees; the right side shows the processed data after a fracture was detected.

Bottom line: This paper describes what lies ahead for healthcare in general. The increasing sophistication and accuracy of AI applications will assist trauma professionals in doing their jobs better. But rest easy, they will not take our jobs anytime soon. What we do (for the most part) takes very complex processing and decision making. It will be quite some time before these systems can do anything more that augment what we do.

Expect to see these AI products integrated with PACS viewing systems at some point in the not so distant future. The radiologist will interpret images in conjunction with the AI, which will highlight suspicious areas on the images as an assist. The radiologist can then make sure they have reported on all regions that both they and the AI have flagged.

Here are my questions and comments for the presenter/authors:

  • How can you be sure that your model isn’t only good for analyzing your training and test datasets? If neural networks are overtrained, they get very good at the original datasets but are not so good analyzing novel datasets. Have you tried the on your own data yet?
  • Explain what “class reweighting” is and how it improved your model. I presume you used this technique to compensate for the potential issue mentioned above. But be sure to explain this in simple terms to the audience.
  • Don’t lose the audience with the net details. You will need to give a basic description of how deep learning nets are developed and how they work, but not get too fancy.

This is an interesting glimpse into what is coming to a theater near you, so to speak. Expect to see applications appearing in the next few years.

Reference: AUTOMATED RIB FRACTURE DETECTION AND CHARACTERIZATION ON COMPUTED TOMOGRAPHY SCANS USING COMPUTER VISION. EAST 2023 Podium paper #16.

Print Friendly, PDF & Email