Despite being a common and treatable cause of heart failure in older adults, cardiac transthyretin amyloidosis (ATTR-CM) is still not well known. In a prospective cohort study involving hospitalized patients with heart failure with preserved ejection fraction (HFpEF) and left ventricular hypertrophy, wild-type ATTR accounted for approximately 13% of cases. This discovery underscores the imperative for systematic case identification within cardiology and nuclear medicine. [1].

Bone-avid SPECT tracers (99mTc-PYP/DPD/HMDP) enable non-biopsy diagnoses in appropriate clinical contexts, whereas amyloid-targeted PET agents improve diagnostic and research functions by facilitating quantitative evaluation. Translational and initial clinical investigations employing 18F-florbetapir and 18F-florbetaben have illustrated targeted myocardial binding to amyloid, the viability of identifying early or subclinical disease, and associations with disease burden and functionality [2–3]. Combining PET with advanced CMR (native T1, extracellular volume, and late gadolinium enhancement) in a multiparametric way can improve noninvasive phenotyping. When used with clinical and lab data, it may help tell the difference between ATTR and light-chain amyloidosis [4].

Looking ahead, standardized quantitative PET metrics, like SUV-based indices or kinetic parameters, could be used as objective biomarkers to record how well disease-modifying therapies, such as tafamidis and RNA-targeting agents, are working. Still, these endpoints must be tested in groups of people from multiple centers. To ensure that results can be reproduced and compared across centers, it is important to standardize the processes of acquisition, reconstruction, and analysis based on new societal guidelines [4]. Another advancement appropriate for incorporation into nuclear cardiology workflows is deep-learning–based automated ATTR diagnosis utilizing 99mTc-PYP SPECT imaging [5].

In summary, amyloid-targeted PET and hybrid PET/CMR don’t replace the well-worn SPECT pathways; they round them out. Together they make quantitative phenotyping more straightforward and help clinicians actually follow treatment trajectories rather than just snapshot them. Getting these tools into everyday clinics and into trials will hinge on tight, multicenter standardization—ideally with an assist from AI-driven analytics to keep the measurements consistent and, frankly, usable.