A Vision for Global CT Radiation Dose Optimization.

The topic of CT radiation dose management is receiving renewed attention since the recent approval by CMS for new CT dose measures. Widespread variation in CT dose persists in practices across the world, suggesting that current dose optimization techniques are lacking. The author outlines a proposed strategy for facilitating global CT radiation dose optimization. CT radiation dose optimization can be defined as the routine use of CT scan parameters that consistently produce images just above the minimum threshold of acceptable image quality for a given clinical indication, accounting for relevant patient characteristics, using the most dose-efficient techniques available on the scanner. To accomplish this, an image quality-based target dose must be established for every protocol; for nonhead CT applications, these target dose values must be expressed as a function of patient size. As variation in outcomes is reduced, the dose targets can be decreased to more closely approximate the minimum image quality threshold. Maintaining CT radiation dose optimization requires a process control program, including measurement, evaluation, feedback, and control. This is best accomplished by local teams made up of radiologists, medical physicists, and technologists, supported with protected time and needed tools, including analytics and protocol management applications. Other stakeholders critical to facilitating CT radiation dose management include researchers, funding agencies, industry, regulators, accreditors, payers, and the ACR. Analogous coordinated approaches have transformed quality in other industries and can be the mechanism for achieving the universal goal of CT radiation dose optimization.

Proceedings From the 2022 ACR-RSNA Workshop on Safety, Effectiveness, Reliability, and Transparency in AI.

Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has been limited adoption of such AI tools into clinical practice. During a 1-day workshop in November 2022, co-organized by the ACR and the RSNA, participants outlined experiences and problems with implementing AI in clinical practice, defined the needs of various stakeholders in the AI ecosystem, and elicited potential solutions and strategies related to the safety, effectiveness, reliability, and transparency of AI algorithms. Participants included radiologists from academic and community radiology practices, informatics leaders responsible for AI implementation, regulatory agency employees, and specialty society representatives. The major themes that emerged fell into two categories: (1) AI product development and (2) implementation of AI-based applications in clinical practice. In particular, participants highlighted key aspects of AI product development to include clear clinical task definitions; well-curated data from diverse geographic, economic, and health care settings; standards and mechanisms to monitor model reliability; and transparency regarding model performance, both in controlled and real-world settings. For implementation, participants emphasized the need for strong institutional governance; systematic evaluation, selection, and validation methods conducted by local teams; seamless integration into the clinical workflow; performance monitoring and support by local teams; performance monitoring by external entities; and alignment of incentives through credentialing and reimbursement. Participants predicted that clinical implementation of AI in radiology will continue to be limited until the safety, effectiveness, reliability, and transparency of such tools are more fully addressed.

The ACR Mammography Positioning Improvement Collaborative: A Multicenter Improvement Program Within a Learning Network Framework.

PURPOSE/OBJECTIVE: To share the experience and results of the first cohort of the ACR Mammography Positioning Improvement Collaborative, in which participating sites aimed to increase the mean percentage of screening mammograms meeting the established positioning criteria to 85% or greater and show at least modest evidence of improvement at each site by the end of the improvement program. METHODS: The sites comprising the first cohort of the collaborative were selected on the basis of strength of local leadership support, intra-organizational relationships, access to data and analytic support, and experience with quality improvement initiatives. During the improvement program, participating sites organized their teams, developed goals, gathered data, evaluated their current state, identified key drivers and root causes of their problems, and developed and tested interventions. A standardized image quality scoring system was also established. The impact of the interventions implemented at each site was assessed by tracking the percentage of screening mammograms meeting overall passing criteria over time. RESULTS: Six organizations were selected to participate as the first cohort, beginning with participation in the improvement program. Interventions developed and implemented at each site during the program resulted in improvement in the average percentage of screening mammograms meeting overall passing criteria per week from a collaborative mean of 51% to 86%, with four of six sites meeting or exceeding the target mean performance of 85% by the end of the improvement program. Afterward, all respondents to the postprogram survey indicated that the program was a positive experience. CONCLUSION: Using a structured improvement program within a learning network framework, the first cohort of the collaborative demonstrated that improvement in mammography positioning performance can be achieved at multiple sites simultaneously and validated the hypothesis that local sites' shared experiences, insights, and learnings would not only improve performance but would also build a community of improvers collaborating to create the best experience for technologists, staff, and patients.

Improving Prostate MR Image Quality in Practice-Initial Results From the ACR Prostate MR Image Quality Improvement Collaborative.

OBJECTIVE: Variability in prostate MRI quality is an increasingly recognized problem that negatively affects patient care. This report aims to describe the results and key learnings of the first cohort of the ACR Learning Network Prostate MR Image Quality Improvement Collaborative. METHODS: Teams from five organizations in the United States were trained on a structured improvement method. After reaching a consensus on image quality and auditing their images using the Prostate Imaging Quality (PI-QUAL) system, teams conducted a current state analysis to identify barriers to obtaining high-quality images. Through plan-do-study-act cycles involving frontline staff, each site designed and tested interventions targeting image quality key drivers. The percentage of examinations meeting quality criteria (ie, PI-QUAL score ≥4) was plotted on a run chart, and project progress was reviewed in weekly meetings. At the collaborative level, the goal was to increase the percentage of examinations with PI-QUAL ≥4 to at least 85%. RESULTS: Across 2,380 examinations audited, the mean weekly rates of prostate MR examinations meeting image quality criteria increased from 67% (range: 60%-74%) at baseline to 87% (range: 80%-97%) upon program completion. The most commonly employed interventions were MR protocol adjustments, development and implementation of patient preparation instructions, personnel training, and development of an auditing process mechanism. CONCLUSION: A learning network model, in which organizations share knowledge and work together toward a common goal, can improve prostate MR image quality at multiple sites simultaneously. The inaugural cohort's key learnings provide a road map for improvement on a broader scale.