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Objective  To identify the whole-body MRI (WB-MRI) image type(s) with the highest value for assessment of multiple myeloma, in order to optimise acquisition protocols and read times.    Methods  Thirty patients with clinically-suspected MM underwent WB-MRI at 3 Tesla. Unenhanced Dixon images [fat-only (FO) and water-only (WO)], post contrast Dixon [fat-only plus contrast (FOC) and water-only plus contrast (WOC)] and diffusion weighted images (DWI) of the pelvis from all 30 patients were randomised and read by three experienced readers. For each image type, each reader identified and labelled all visible myeloma lesions. Each identified lesion was compared with a composite reference standard achieved by review of a complete imaging dataset by a further experienced consultant radiologist to determine truly positive lesions. Lesion count, true positives, sensitivity, and positive predictive value were determined. Time to read each scan set was recorded. Confidence for a diagnosis of myeloma was scored using a Likert scale. Conspicuity of focal lesions was assessed in terms of percent contrast and contrast to noise ratio (CNR).    Results  Lesion count, true positives, sensitivity and confidence scores were significantly higher when compared to other image types for DWI ( P <0.0001 to 0.003), followed by WOC (significant for sensitivity ( P <0.0001 to 0.004), true positives ( P  = 0.003 to 0.049) and positive predictive value ( P < 0.0001 to 0.006)). There was no statistically significant difference in these metrics between FO and FOC. Percent contrast was highest for WOC ( P  = 0.001 to 0.005) and contrast to noise ratio (CNR) was highest for DWI ( P  = 0.03 to 0.05). Reading times were fastest for DWI across all observers ( P < 0.0001 to 0.014).    Discussion  Observers detected more myeloma lesions on DWI images and WOC images when compared to other image types. We suggest that these image types should be read preferentially by radiologists to improve diagnostic accuracy and reporting efficiency.

Whole body MRI in multiple myeloma: Optimising image acquisition and read times