Patient dosimetry techniques Flashcards
What is the equation for entrance surface dose?
ESD = Tube output (dose/mAs) x mAs x ISL correction x BSF.
What is DAP? How does it vary with distance?
- Dose area product.
- Dose (or integral of dose as it is non-uniform) x Area of beam.
- It gives a measure of the total energy entering the patient assuming all energy is absorbed.
- It is invariant with distance assuming negligible contribution from backscatter, air interaction components and extra-focal radiation.
How is CTDI_air measured? What is the equation for CTDI_air?
- With a 100 mm pencil ionisation chamber.
- CTDI_air = 1/s . int(D(x).dx) where s is the nominal slice width and the integral is that of the dose across the chamber.
How is CTDI_w measured? What is the equation for CTDI_w?
- With a 100 mm pencil ionisation chamber and standard head and body phantoms with insert points for the chamber.
- CTDI_w = 1/3 . CTDI_100centre + 2/3 . CTDI_100periph.
What is CTDI_vol?
CTDI_vol is the CTDI_w corrected for pitch or couch increment and the mAs used in the scan. CTDI_w will be measured for one rotation and for a different mAs, hence the correction required.
What is DLP?
Dose length product is the CTDI_vol multiplied by irradiated length.
What is the difference between incident air kerma and entrance surface air kerma? How are they related?
- Incident air kerma: Air kerma along central beam at position of patient without patient (i.e. no backscatter).
- Entrance surface air kerma: Air kerma along the central beam at position of patient including backscatter from patient.
- Entrance air kerma = Incident air kerma x Backscatter factor.
What three general ways can patient dose estimates be measured or collected?
- Phantom measurements.
- On-patient measurements.
- Equipment dose indicators.
What values would typically be determined using a phantom for radiography and fluoroscopy? What kind of factors should be used when determining patient dosimetry estimates using phantoms?
- Radiography: Entrance air kerma.
- Fluoroscopy: Entrance air kerma rate.
- Clinically relevant factors.
What are the pros/cons of phantom measurements for patient dosimetry estimates?
Pros:
- Clinical staff not required.
- Results for standard sizes which can be easily compared (e.g. for different sets of equipment).
Cons:
- Does not represent actual patient data unless irradiation conditions (parameters, dimensions, setup etc.) are exactly the same.
How is accuracy provided with equipment DAP estimates of patient dose? What are the pros and cons of equipment patient dose measurements like this?
- DAP calibration.
Pros:
- Easy if equipment is installed.
- ‘Real’ patient data.
Cons:
- Calibrations required.
- Results influenced by spread in patient sizes.
What are the pros and cons of electronic dose data collection?
Pros:
- Easy to collect large amounts of data.
- Continuous data collection possible.
- Plenty of software available for data analysis.
Cons:
- Accuracy of data relies on dose indicators and other factors such as examination names etc.
- Not all equipment compatible.
What are some typical uncertainties associated with measuring dose to a phantom or a single patient?
- Measurement setup (e.g. uncertainty in measurement position).
- Calibration, stability and energy dependence of dosimeter (e.g. temperature and pressure corrections).
- Precision of reading.
- Uncertainty in backscatter factors.
- TLD correction factors.
What are some typical uncertainties associated with determining average dose to a cohort of patients?
- Measurement setup (e.g. uncertainty in measurement position).
- Calibration, stability and energy dependence of dosimeter (e.g. temperature and pressure corrections).
- Precision of reading.
- Uncertainty in backscatter factors.
- TLD correction factors.
- Dose variations between patients (e.g. due to size, examination complexity or patient numbers).
What are ‘type A’ uncertainties? How can they be reduced?
- Random errors. Uncertainties relating to the standard deviation on the mean of multiple measurements. They are random and relate to the Gaussian distribution of measurement values.
- They can be reduced by increasing the number of measurements.