ARRT Mammography common mistakes
ARRT Mammography · Understand the traps and how to avoid them.
Recites the QC tests as an unordered memorized list, then guesses on the exam whether a given test is daily, weekly, monthly, quarterly, or annual (and who performs it).
Most candidates learn the QC content by reading the test names top-to-bottom in the manual, so the test is stored without a time or performer 'hook' and cannot be retrieved when the question asks about frequency.
What to do instead
Rebuild the list as a two-column matrix (frequency down the side; technologist vs. medical physicist across the top) drawn from the current MQSA/ACR QC manual in use, then self-quiz by covering one column at a time and stating the empty cell aloud.
Answers MLO and CC image-criteria questions with criteria swapped - e.g., expecting the pectoral muscle to the nipple line on the CC view, or requiring the inframammary fold on the CC view.
The two views are studied in the same sitting and the criteria blend into one undifferentiated checklist instead of two distinct acceptance lists.
What to do instead
Write two separate acceptance checklists and rehearse each view in isolation: MLO = full pectoral muscle to the nipple line, inframammary fold, nipple in profile, no skin folds, open posterior nipple line; CC = maximum medial and lateral tissue, nipple in profile, no skin folds, posterior nipple line within 1 cm of the MLO PNL.
When shown a described image problem (nipple not in profile, skin folds, a blurred image, a flattened inframammary fold), cannot work backward from the appearance to the positioning or technical cause.
Candidates memorize 'perfect image' criteria but never practice the reverse direction - reading a defect and naming the corrective action - which is the format most image-evaluation questions use.
What to do instead
Build a defect-to-cause-to-fix table (e.g., skin folds → tissue not smoothed/traction → smooth and hold with the flat of the hand; blurred → motion → reinforce compression and breath-hold instructions) and drill it by covering the cause column.
Mixes up the BI-RADS breast density categories, confusing 'scattered fibroglandular' with 'heterogeneous,' or assigning a letter based on how the image 'looks' rather than on the defined amount of fibroglandular tissue.
The four categories are learned as adjectives from a report template rather than as threshold-defined categories, so the middle two blur together.
What to do instead
Anchor each letter to its defining descriptor and approximate proportion of fibroglandular tissue, then rehearse with the paired phrasing ('mostly fat' = a, 'scattered' = b, 'heterogeneous' = c, 'extreme' = d) until each letter retrieves its own definition.
Confuses breast pathology and histology terms - e.g., treating LCIS as a direct precursor like DCIS, or being unable to distinguish invasive ductal from invasive lobular carcinoma in a description.
These entities are studied as a vocabulary list rather than linked to their clinical behavior and imaging presentation, so the names survive but the distinctions do not.
What to do instead
Build a three-column pathology table (entity → typical imaging presentation → clinical significance/management implication) and be able to state in one sentence why LCIS and DCIS differ in management.
On magnification questions, cannot state which focal spot is used, how the air gap and grid interact, the resulting magnification factor, or the dose consequence.
Magnification is memorized as a single 'bigger image' fact without the linked chain of focal spot → geometry → scatter control → dose.
What to do instead
Learn magnification as a causal chain: small focal spot → object farther from the detector → air gap removes scatter so the grid is not used → dose increases; then practice the magnification-factor calculation using the SID and object-to-detector distance.
Uses 'dose' interchangeably for entrance skin exposure and average glandular dose, or cannot state the units or the MQSA dose limit for a standard compressed breast.
Dose appears in several chapters (equipment, QC, patient education) and candidates absorb the word without attaching it to a specific quantity or unit.
What to do instead
Write one index card: quantity → what it measures → unit → the regulatory limit and the phantom/breast it is referenced to, and be able to say why AGD, not ESE, is the number that matters for risk.
Treats a screening exam and a diagnostic exam as the same encounter - recommending the screening protocol for a patient with a palpable lump, nipple discharge, or focal pain, or applying diagnostic workup to a routine asymptomatic patient.
The two are learned as 'mammogram' with different paperwork rather than as different clinical intents with different imaging scopes and physician involvement.
What to do instead
Learn them as a contrast pair: indication, whether prior images and a interpreting physician are required, what additional views may be added, and what the patient is told; rehearse by sorting brief patient scenarios into screening vs. diagnostic.
Skips the practical pre-exam patient instruction set - e.g., does not address avoiding deodorant/powder/talc on the day of the exam, does not explain compression adequately, and does not ask about implants, pregnancy, lactation, prior surgery, or prior images.
Patient education feels like 'soft' content and is under-studied relative to physics and positioning, so answers come from intuition rather than protocol.
What to do instead
Create a pre-exam script covering artifacts from topical products, compression rationale and its duration, implant and post-surgical modifications, and prior-image retrieval; practice it as a verbal checklist, since exam items test the content of the conversation.
Cannot attribute an artifact to its source - e.g., labels a CR plate dust or hair artifact as a detector failure, or calls grid lines a processing error.
Artifacts are memorized as a picture-naming exercise without learning where in the imaging chain (patient, receptor, grid, processing, display) each artifact originates.
What to do instead
Organize artifacts by origin in the imaging chain and practice attributing each to a stage with the corrective action; this converts picture recognition into a reasoning path that works even when the image is only described in words.