Cardiac amyloidosis detection by early bisphosphonate (99mTc-HMDP) scintigraphy

Chemical design of radioiodinated probes with a metabolizable linkage for target-selective imaging of systemic amyloidosis

INTRODUCTION

Systemic amyloidosis is a rare disease caused by the deposition of amyloid fibrils in various organs. Amyloid-targeted radiopharmaceuticals have been developed and applied to diagnose systemic amyloidosis peripherally; however, high-contrast imaging has not been achieved because of the high background signals in normal organs. To overcome this problem, we designed an amyloid-targeted radioiodinated probe 1 with a metabolizable linkage (ester bond) to release of radiolabeled metabolites (m-iodohippuric acid) in normal organs that could be rapidly excreted in the urine.

METHODS

Compound 1 was synthesized by conjugating 2-(4-(methylamino)phenyl)benzo[d]thiazol-6-ol, an amyloid-targeting compound, with m-iodohippuric acid. [125I]1 was synthesized via iododestannylation using a tributyltin precursor. Mouse models of amyloid A (AA) amyloidosis, a type of systemic amyloidosis, were prepared by administering amyloid-enhancing factor to mice and used for in vitro autoradiography using organ sections and in vivo evaluation.

RESULTS

[125I]1 was obtained with a radiochemical yield of 59% and radiochemical purity of over 95%. An in vitro autoradiographic study demonstrated that [125I]1 specifically binds to amyloid in the splenic tissue. Upon administration to normal mice, [125I]1 was distributed to organs throughout the body, followed by the rapid excretion of radioactivity in the urine as m-[125I]iodohippuric acid. Furthermore, ex vivo autoradiography showed that [125I]1 bound to the amyloid formed around the follicles in the spleens of AA amyloidosis model mice.

CONCLUSION

These results suggest that the interposition of a metabolizable linkage between an amyloid-targeting moiety and a radiolabeled hippuric acid would be useful in the design of radiopharmaceuticals for high-contrast imaging of systemic amyloidosis.

Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography

In this review, we will summarize a selection of articles on single-photon emission computed tomography published in the Journal of Nuclear Cardiology in 2022. The aim of this review is to concisely recap major advancements in the field to provide the reader a glimpse of the research published in the journal over the last year. This review will place emphasis on myocardial perfusion imaging using single-photon emission computed tomography summarizing advances in the field including in prognosis, non-perfusion variables, attenuation compensation, machine learning and camera design. It will also review nuclear imaging advances in amyloidosis, left ventricular mechanical dyssynchrony, cardiac innervation, and lung perfusion. We encourage interested readers to go back to the original articles, and editorials, for a comprehensive read as necessary but hope that this yearly review will be helpful in reminding readers of articles they have seen and attracting their attentions to ones they have missed.

Cardiac amyloidosis characterization by kinetic model fitting on [18F]florbetaben PET images

OBJECTIVE

To evaluate the feasibility of kinetic modeling-based approaches from [18F]-Flobetaben dynamic PET images as a non-invasive diagnostic method for cardiac amyloidosis (CA) and to identify the two AL- and ATTR-subtypes.

METHODS AND RESULTS

Twenty-one patients with diagnoses of CA (11 patients with AL-subtype and 10 patients with ATTR-subtype of CA) and 15 Control patients with no-CA conditions underwent PET/CT imaging after [18F]Florbetaben bolus injection. A two-tissue-compartment (2TC) kinetic model was fitted to time-activity curves (TAC) obtained from left ventricle wall and left atrium cavity ROIs to estimate kinetic micro- and macro-parameters. Combinations of kinetic parameters were evaluated with the purpose of distinguishing Control subjects and CA patients, and to correctly label the last ones as AL- or ATTR-subtype. Resulting sensitivity, specificity, and accuracy for Control subjects were: 0.87, 0.9, 0.89; as far as CA patients, the sensitivity, specificity, and accuracy were respectively 0.9, 1, and 0.97 for AL-CA patients and 0.9, 0.92, 0.97 for ATTR-CA patients.

CONCLUSION

Pharmacokinetic analysis based on a 2TC model allows cardiac amyloidosis characterization from dynamic [18F]Florbetaben PET images. Estimated model parameters allows to not only distinguish between Control subjects and patients, but also between AL- and ATTR-amyloid patients.

Regional Characterization of the Gottingen Minipig Brain by [18 F]FDG Dynamic Pet Modeling

Purpose

To determine the best kinetic model to be applied on dynamic brain [18 F]FDG PET images by characterizing the regional brain glucose metabolism of normal Göttingen minipigs.

Methods

Nine Göttingen minipigs were scanned with a clinical PET/CT tomograph, starting from the injection of an intravenous bolus of [18 F]FDG, for about 25 min. Dynamic images were reconstructed and nine brain regions of interest (ROI), plus a vascular region, were defined and time-activity curves (TAC) were determined.

Three kinetic models were considered for fitting with experimental TACs: one-tissue compartment model 1TC, two-tissue irreversible compartment model 2TCi and two-tissue reversible model 2TC. Akaike Information Criterion was considered to evaluate the goodness of each model fitting. Regional and global kinetic parameter values were evaluated, in addition to the partition coefficient, net influx rate and retention index (RI).

Results

Both 2TCi and 2TC models turned out to be good choices for the next analysis. Parameter values were very similar between the different brain regions, with similar values to when the brain as a whole is considered (kinetic parameters mean values, from 2TCi model: K1 = 1.0 ml/g/min, k2 = 0.49 min− 1, k3 = 0.034 min− 1, K1/k2 = 2.14ml/g, Ki =0.069 ml/g/min; from 2TC model: K1 = 1.10 ml/g/min, k2 = 0.54 min− 1, k3 = 0.058 min− 1, k4 = 0.039 min− 1, K1/k2 = 2.18 ml/g, Ki = 0.10 ml/g/min; RI mean ± sd: 0.147 ± 0.037 min− 1), with the exception of the cerebellum (mean values from the 2TCi model: K1 = 0.52 ml/g/min, k2 = 0.56 min− 1, k3 = 0.025 min− 1, K1/k2 = 0.98ml/g, Ki=0.022 ml/g/min; from 2TC model: K1 = 0.54 ml/g/min, k2 = 0.61 min− 1, k3 = 0.044 min− 1, k4 = 0.038 min− 1, K1/k2 = 0.95ml/g, Ki=0.032 ml/g/min; RI mean ± sd: 0.071 ± 0.018 min− 1).

Conclusion

The two-tissue model is able to describe the regional brain metabolism in Göttingen minipigs. Compared to the 2TCi model, in the 2TC model the k4 micro-parameter was also evaluated. This led to adjustments of the other microparameters, especially k3 and consequently the net influx rate Ki. For healthy minipigs, the glucose metabolism was similar in all of the brain regions analyzed, with the exception of the cerebellum, where the FDG uptake was lower.

Diagnosis of cardiac amyloid transthyretin (ATTR) amyloidosis by early (soft tissue) phase [99mTc]Tc-DPD whole body scan: comparison with late (bone) phase imaging

OBJECTIVES

Although expert consensus recommendations suggest 2-3 h as the time interval between bone-seeking radiotracers injection and acquisition, it has been reported that images obtained early after [^99mTc]Tc-HMDP administration are sufficient to diagnose cardiac amyloidosis. We evaluated the diagnostic performance of [^99mTc]Tc-DPD early phase whole body scan with respect to late phase imaging.

METHODS

We qualitatively and semiquantitatively reviewed [^99mTc]Tc-DPD imaging of 53 patients referred for suspect cardiac amyloidosis. Findings of early and late phase images were compared with SPECT results (considered the standard-of-reference) determining sensitivity and specificity for visual analysis of each phase imaging and for each semiquantitative index.

RESULTS

SPECT imaging was negative for cardiac accumulation in 25 patients and positive in 28. Visual analysis of early phase whole body scan had an extremely significant capability to predict SPECT results; nevertheless, complete agreement was not reached. Visual analysis of late phase imaging showed slightly better results. Semiquantitative analysis of early phase images, namely heart to mediastinum ratio, performed better than semiquantitative analysis of late phase images.

CONCLUSION

Visual analysis of [^99mTc]Tc-DPD early phase whole body scan is promising in diagnosing cardiac amyloidosis; further studies are needed to confirm our results in different clinical scenarios.

KEY POINTS

• Visual analysis of early phase planar imaging using [^99mTc]Tc-DPD is accurate to diagnose cardiac amyloidosis and may be satisfactory at least in frail patients with high cardiac burden of amyloid fibrils.

99mTechnetium-labeled cardiac scintigraphy for suspected amyloidosis: a review of current and future directions

Cardiac amyloidosis (CA) is an underdiagnosed form of restrictive cardiomyopathy leading to a rapid progression into heart failure. Evaluation of CA requires a multimodality approach making use of echocardiography, cardiac magnetic imaging, and nuclear imaging. Technetium (Tc)-labeled cardiac scintigraphy has witnessed a resurgence in its application for the workup of CA. Advancements in disease-modifying therapies have fueled the rapid adoption of cardiac scintigraphy using bone tracers and the need for transformative novel studies. The goal of this review is to present diagnostic utility, currently recommended protocols, as well as a glimpse into the rapid evolution of Tc-labeled cardiac scintigraphy in the diagnosis of CA.

Deep-learning-based cardiac amyloidosis classification from early acquired pet images

The objective of the present work was to evaluate the potential of deep learning tools for characterizing the presence of cardiac amyloidosis from early acquired PET images, i.e. 15 min after [18F]-Florbetaben tracer injection. 47 subjects were included in the study: 13 patients with transthyretin-related amyloidosis cardiac amyloidosis (ATTR-CA), 15 patients with immunoglobulin light-chain amyloidosis (AL-CA), and 19 control-patients (CTRL). [18F]-Florbetaben PET/CT images were acquired in list mode and data was sorted into a sinogram, covering a time interval of 5 min starting 15 min after the injection. The resulting sinogram was reconstructed using OSEM iterative algorithm. A deep convolutional neural network (CAclassNet) was designed and implemented, consisting of five 2D convolutional layers, three fully connected layers and a final classifier returning AL, ATTR and CTRL scores. A total of 1107 2D images (375 from AL-subtype patients, 312 from ATTR-subtype, and 420 from Controls) have been considered in the study and used to train, validate and test the proposed network. CAclassNet cross-validation resulted with train error mean ± sd of 2.001% ± 0.96%, validation error of 4.5% ± 2.26%, and net accuracy of 95.49% ± 2.26%. Network test error resulted in a mean ± sd values of 10.73% ± 0.76%. Sensitivity, specificity, and accuracy evaluated on the test dataset were respectively for AL-CA sub-type: 1, 0.912, 0.936; for ATTR-CA: 0.935, 0.897, 0.972; for control subjects: 0.809, 0.971, 0.909. In conclusion, the proposed CAclassNet model seems very promising as an aid for the clinician in the diagnosis of CA from cardiac [18F]-Florbetaben PET images acquired a few minutes after the injection.