single-bevel needle tip gives a longer length of tissue but has higher deection, the multi-bevel design helps to balance bending moments but decreases the length of the tissue sampling [29]. The operator must be familiar with the character­istics of the needle in use and make appropriate adjustments to account for deection during the targeting and ring processes.

separate container for each site. Submitting more than two cores in a single container increases the probability of tissue core fragmentation [33]. More details on biopsy core optimization are dis­cussed in Chap. 22 of this textbook by Iczkowski.

The most important requirement of an ideal FB is accurate localization with an imaging modality that is reproducible and has less operator­dependent variation. This rapid and widespread adoption of mpMRI for PCa management has resulted in large volumes of mpMRI but not all of this MRI demonstrates high image quality or highly accurate image interpretation [54]. This variability of quality poses a signicant risk as producing diagnostic images of suboptimal qual­ity could lead to downstream challenges in image­directed biopsy or even biopsy being not performed in cases where it should be performed. Even when the quality is deemed acceptable, the variety in equipment, practices, and image inter­pretation can complicate mpMRI standardization. Studies have demonstrated that the PPVs for a given PI-RADS score can vary not only among different institutions but also among individual radiologists [9, 55]. Some of these variations can be attributed to factors such as the pretest proba­bility of patients undergoing mpMRI and the expertise of the radiologist. However, it should be mentioned that protocols to obtain prostate MRI can be heterogeneous worldwide and mostly set based on each institution’s radiologists and physi­cists. Although PI-RADS v2.1 has provided the minimum technical requirements for performing prostate mpMRI (which are important to follow) and systems such as Prostate Imaging Quality (PI-QUAL) have attempted to standardize assess­ment of MR image quality, it is evident that even adherence to these technical standards does not ensure high quality, as certain MRI machines may achieve better results with technical parameters beyond the recommended guidelines [56]. This is a huge opportunity for QI in imaging and, conse­quently, in FB to move toward a more standard­ized mpMRI where new technology such as deep learning could be used to improve image quality, reduce image acquisition time, and help to stan­dardize the appearance of prostate MR images. This will undoubtedly help in rectifying inter­reader discrepancies pave the way for articial intelligence aided detection to become a reality.