Ischemic cholangiopathy (IC) is the leading cause of inferior long-term outcomes following donation after circulatory death (DCD) liver transplant. Biliary strictures related to IC are nonanastomotic strictures (NASs) by definition and involve the donor hepatic ducts proximal to the anastomosis, compared with postsurgical anastomotic strictures that form due to fibrotic healing. IC-related NASs can be microangiopathic with patent hepatic artery or macroangiopathic with occluded or stenotic hepatic artery. Recently, IC with NASs have been described to have four distinct patterns at imaging: diffuse necrosis, multifocal progressive, confluence dominant, and minor form, which correlate clinically with graft prognosis. Severe IC can lead to ductal wall breakdown with subsequent bile leaks that can cause significant patient morbidity, with imaging playing a vital role in diagnosis and guiding intervention. IC also predisposes the transplanted liver to biliary stasis and subsequent formation of stones, casts, and sludge. Some cases of posttransplant biliary stricturing are not IC but are a sequela of reflux cholangitis seen with choledochojejunal anastomosis. Other biliary findings in the posttransplant liver can be explained by sphincter of Oddi dysfunction that results from denervation. The authors describe and comprehensively categorize the various IC types and their imaging patterns at MRI and MR cholangiopancreatography, review the prognostic significance of these imaging patterns, and discuss imaging features of additional biliary complications associated with IC after DCD liver transplant. ©RSNA, 2024 Supplemental material is available for this article.

Decades of experience supports LDLT as a favorable strategy to reduce waitlist mortality. The multiple regenerative pathways of hepatocytes and other hepatic cells justify the rationale behind it. Nonetheless, living liver donation is still underused and its broader implementation is challenging, mostly due to variability in practices leading to concerns related to donor safety. A non-systematic literature search was conducted for peer-reviewed original articles related to pre-operative evaluation of living liver donor candidates. Eligible studies were synthesized upon consensus for discussion in this up-to-date review. Review of the literature demonstrate that the importance of preoperative assessment of vascular, biliary and liver volume to ensure donor safety and adequate surgical planning for graft procurement is widely recognized. Moreover, data indicates that anatomic variants in vascular and biliary systems in healthy donors are common, present in up to 50% of the population. Therefore, comprehensive mapping and visualizations of each component is needed. Different imaging modalities are reported across practices and are discussed in detail. Lastly, assessment of liver volume must take into account several technical and donor factors that increase the chances of errors in volume estimation, which occurs in up to 10% of the cases. Experience suggests that maximizing donor safety and lessening their risks is a result of integrated experience between hepatobiliary and transplant surgery, along with multidisciplinary efforts in performing a comprehensive pre-operative donor assessment. Although technical advances have increased the accuracy of volume estimation, over- or under-estimation remains a challenge that needs further attention.

Hepatic artery thrombosis (HAT) is a devastating vascular complication following liver transplantation, requiring prompt diagnosis and rapid revascularization treatment to prevent graft loss. At present, imaging modalities such as ultrasound, computed tomography, and magnetic resonance play crucial roles in diagnosing HAT. Although imaging techniques have improved sensitivity and specificity for HAT diagnosis, they have limitations that hinder the timely diagnosis of this complication. In this sense, the emergence of artificial intelligence (AI) presents a transformative opportunity to address these diagnostic limitations. The develo pment of machine learning algorithms and deep neural networks has demon strated the potential to enhance the precision diagnosis of liver transplant com plications, enabling quicker and more accurate detection of HAT. This article examines the current landscape of imaging diagnostic techniques for HAT and explores the emerging role of AI in addressing future challenges in the diagnosis of HAT after liver transplant.