Detecting Image Quality in Medical Imaging

Suboptimal image quality interferes with image interpretation, leading to incorrect or uncertain diagnosis, as well as increased interpretative time, and potentially suboptimal patient care. Non-diagnostic quality image may require repeating the study or may trigger a recommendation of a repeat exam or different study modality (e.g., CT or MRI), adding to healthcare cost and inconveniencing the patient. Automatic detection of image quality allows for correction of the problem at the time of the imaging exam or shortly thereafter and could result in better diagnosis and patient care while reducing repeat imaging.

Obese patient comes to the emergency room after a fall due to knee pain. Adjusting kVpand mAs can achieve reasonable image quality for diagnosis of fracture on a radiograph. Standard kVpand mAs may not be sufficient for an obese patient due to increased scatter. However, the emergency room is really busy, with multiple studies waiting to be done, the patient is in pain and has difficulty getting into proper position, given all the distractions standard settings are used. The radiologist views the study, notices poor quality, and gives the disclaimer of “cannot rule out fracture.” Emergency room doctor then orders a CT to evaluate for fracture. If the algorithm can detect these cases retrospectively, they can be reviewed by a quality improvement committee and can be remedied by procedures & policies (e.g., for BMI > 30, use adjusted settings). If the algorithm can catch these cases in real-time before the technologist completes the study, additional images can be obtained while the patient is still in the X-ray suite, and subsequent CT may not need to be performed.

Patient with hip replacement hardware presents for CT for abdominal pain. The patient forgets to mention the hardware, as it is in the hip and pain is abdominal. The technologist and radiologist do not have this information as the referring physician is outside the system. The referring physician does not indicate in the CT abdomen/pelvis order that the patient has hip replacement hardware. Thepatient gets a CT of the abdomen/pelvis without metal suppression protocol. There are marked metal artifacts obscuring the pelvis, limiting evaluation. Radiologist cannot see structures such as bladder, reproductive organs, , and pelvic small bowel. Patient needs to return for a repeat study, or the clinician needs to make decision based on the low-quality study. Clinician may order another study (e.g. endoscopic ultrasound, GI fluoroscopic study) for evaluation of unseen structures. Another possibility is that the radiologist may mistake an artifact as an abnormality, and may recommend MRI or other additional imaging studies for follow up. If the algorithm can catch these cases retrospectively, they can be reviewed by a quality improvement committee and can be remedied by procedure & policies (strict review of all hardware before CT, use of scout image to guide CT protocol selection for hardware). If the algorithm can catch these cases in real-time before the technologist completes the study, additional images can be acquired with the proper metal suppression protocol while the patient is still in the CT suite.

• Patient with hip replacement arrives in ED with abdominal pain

• CT acquired using routine protocol (no metal suppression)

• Algorithm analyzes scout images; detects presence of metal implants; flags / notice technologist of choosing alternative imaging protocol with metal artifact reduction algorithms

• Technologist receives alert; realizes that hip hardware is present; obtains new images through the pelvis with metal suppression

• All images are tracked for review; radiologist is able to make accurate diagnosis with high quality images; no repeat imaging necessary

Motion artifact (CT, DR, MR):

present, absent

Hardware artifact (CT, MR):

present, absent

Suboptimal beam penetration due to body habitus (DR, CT):

present absent

Suboptimal FOV for indication (CT, MR, DR):

present, absent

ring artifacts (CT):

present, absent

Shading (CT):

present, absent

cupping/capping (CT):

present, absent

Patient anatomy outside the field of view(CT):

present, absent

Clinical note: causes shading and streaks

Procedure

All Imaging

Views

All

Data Type

DICOM

Modality

X-ray, CT, MRI

RadElement ID

Definition

Presence of hardware artifact

Data Type

Categorical

Value Set

0- Hardware artifact absent

1- Hardware artifact present

2- Unknown

Units

N/A

RadElement ID

Definition

Presence of motion artifact

Data Type

Categorical

Value Set

0- Motion artifact absent

1- Motion artifact present

2- Unknown

Units

N/A

RadElement ID

Definition

Recommendation for additional imaging based on prior image adequacy

Data Type

Categorical

Value Set

0- Imaging adequate

1- Imaging inadequate; further imaging recommended

2- Unknown

Units

N/A