Contemporary Cardiac SPECT Imaging-Innovations and Best Practices: An Information Statement from the American Society of Nuclear Cardiology

Noninvasive Multimodality Imaging for the Assessment of Anomalous Coronary Artery

PURPOSE OF REVIEW

Anomalous aortic origin of a coronary artery (AAOCA) is a rare congenital coronary anomaly with the potential to cause myocardial ischemia and adverse cardiac events. The presence of AAOCA anatomy itself does not necessarily implicate a need for revascularization. Therefore, the purpose of this review is to assess how noninvasive comprehensive anatomic- and physiologic evaluation may guide patient management.

RECENT FINDINGS

The assessment of AAOCA includes an accurate description of the anomalous origin/vessel course including anatomical high-risk features such as a slit-like ostium, proximal narrowing, elliptic vessel shape, acute take-off angle, intramural course, and possible concomitant coronary atherosclerosis and hemodynamics. Various cardiac imaging modalities offer unique advantages and capabilities in visualizing these anatomical and functional aspects of AAOCA. This review explored the role of noninvasive multimodality imaging in the characterization of AAOCA by highlighting the strengths, limitations, and potential applications of the current different cardiac imaging methods, with a focus on the pathophysiology of myocardial ischemia and stress testing protocols.

Emerging molecular imaging targets and tools for myocardial fibrosis detection

Myocardial fibrosis is the heart's common healing response to injury. While initially seeking to optimize the strength of diseased tissue, fibrosis can become maladaptive, producing stiff poorly functioning and pro-arrhythmic myocardium. Different patterns of fibrosis are associated with different myocardial disease states, but the presence and quantity of fibrosis largely confer adverse prognosis. Current imaging techniques can assess the extent and pattern of myocardial scarring, but lack specificity and detect the presence of established fibrosis when the window to modify this process may have ended. For the first time, novel molecular imaging methods, including gallium-68 (68Ga)-fibroblast activation protein inhibitor positron emission tomography (68Ga-FAPI PET), may permit highly specific imaging of fibrosis activity. These approaches may facilitate earlier fibrosis detection, differentiation of active vs. end-stage disease, and assessment of both disease progression and treatment-response thereby improving patient care and clinical outcomes.

Increasing angular sampling through deep learning for stationary cardiac SPECT image reconstruction

BACKGROUND

The GE Discovery NM (DNM) 530c/570c are dedicated cardiac SPECT scanners with 19 detector modules designed for stationary imaging. This study aims to incorporate additional projection angular sampling to improve reconstruction quality. A deep learning method is also proposed to generate synthetic dense-view image volumes from few-view counterparts.

METHODS

By moving the detector array, a total of four projection angle sets were acquired and combined for image reconstructions. A deep neural network is proposed to generate synthetic four-angle images with 76 ([Formula: see text]) projections from corresponding one-angle images with 19 projections. Simulated data, pig, physical phantom, and human studies were used for network training and evaluation. Reconstruction results were quantitatively evaluated using representative image metrics. The myocardial perfusion defect size of different subjects was quantified using an FDA-cleared clinical software.

RESULTS

Multi-angle reconstructions and network results have higher image resolution, improved uniformity on normal myocardium, more accurate defect quantification, and superior quantitative values on all the testing data. As validated against cardiac catheterization and diagnostic results, deep learning results showed improved image quality with better defect contrast on human studies.

CONCLUSION

Increasing angular sampling can substantially improve image quality on DNM, and deep learning can be implemented to improve reconstruction quality in case of stationary imaging.

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

Data Management and Network Architecture Effect on Performance Variability in Direct Attenuation Correction via Deep Learning for Cardiac SPECT: A Feasibility Study

Attenuation correction (AC) is important for accurate interpretation of SPECT myocardial perfusion imaging (MPI). However, it is challenging to perform AC in dedicated cardiac systems not equipped with a transmission imaging capability. Previously, we demonstrated the feasibility of generating attenuation-corrected SPECT images using a deep learning technique (SPECTDL) directly from non-corrected images (SPECTNC). However, we observed performance variability across patients which is an important factor for clinical translation of the technique. In this study, we investigate the feasibility of overcoming the performance variability across patients for the direct AC in SPECT MPI by proposing to develop an advanced network and a data management strategy. To investigate, we compared the accuracy of the SPECTDL for the conventional U-Net and Wasserstein cycle GAN (WCycleGAN) networks. To manage the training data, clustering was applied to a representation of data in the lower-dimensional space, and the training data were chosen based on the similarity of data in this space. Quantitative analysis demonstrated that DL model with an advanced network improves the global performance for the AC task with the limited data. However, the regional results were not improved. The proposed data management strategy demonstrated that the clustered training has potential benefit for effective training.

The Effect of an Energy Window with an Ellipsoid Phantom on the Differential Defect Contrast on Myocardial SPECT Images

Good quality single-photon emission computed tomography (SPECT) images are required to achieve a perfect diagnosis and determine the severity of defects within the myocardial wall. There are many techniques that can support the diagnosis of defect formations in acquired images and contribute to avoiding errors before image construction. The main aim of this study was to determine the effect of energy width (15%, 20%, and 25%) on defect contrast in myocardial SPECT images correlated with the decentralization of positioning of a phantom. A phantom of polyethylene plastic was used to mimic the myocardial wall of the left ventricle. The phantom consists of two chambers, inner and outer. Two rectangular pieces of plastic were placed in anterior and inferior locations in the mid-region of the myocardial phantom to simulate myocardial infarction (defects). The average defect contrast for all phantom positions using 15% to 20% energy was (1.2, 1.6) for the anterior region and (1.1, 2) for the inferior region, respectively. Additionally, the energy window width was >25% with a large displacement of the positioning off center, leading to loss of the defect contrast in myocardial SPECT images, particularly in the inferior region. The study showed decreasing defect contrast in both locations, anterior and inferior, with increasing energy window width correlated with eccentricity positioning of the phantom on an imaging table.

Design Study of an Ultrahigh Resolution Brain SPECT System Using a Synthetic Compound-Eye Camera Design With Micro-Slit and Micro-Ring Apertures

In this paper, we discuss the design study for a brain SPECT imaging system, referred to as the HelmetSPECT system, based on a spherical synthetic compound-eye (SCE) gamma camera design. The design utilizes a large number (  ∼ 500 ) of semiconductor detector modules, each coupled to an aperture with a very narrow opening for high-resolution SPECT imaging applications. In this study, we demonstrate that this novel system design could provide an excellent spatial resolution, a very high sensitivity, and a rich angular sampling without scanning motion over a clinically relevant field-of-view (FOV). These properties make the proposed HelmetSPECT system attractive for dynamic imaging of epileptic patients during seizures. In ictal SPECT, there is typically no prior information on where the seizures would happen, and both the imaging resolution and quantitative accuracy of the dynamic SPECT images would provide critical information for staging the seizures outbreak and refining the plans for subsequent surgical intervention.We report the performance evaluation and comparison among similar system geometries using non-conventional apertures, such as micro-ring and micro-slit, and traditional lofthole apertures. We demonstrate that the combination of ultrahigh-resolution imaging detectors, the SCE gamma camera design, and the micro-ring and micro-slit apertures would offer an interesting approach for the future ultrahigh-resolution clinical SPECT imaging systems without sacrificing system sensitivity and FOV.

Head-to-head comparison of a CZT-based all-purpose SPECT camera and a dedicated CZT cardiac device for myocardial perfusion and functional analysis

PURPOSE

To compare the outputs of a novel all-purpose SPECT camera equipped with CZT detectors (Discovery NM/CT 670) with the state-of-the-art represented by a dedicated CZT (Alcyone, Discovery 530c) cardiac camera in patients submitted to myocardial perfusion imaging (MPI).

METHODS

We included 19 patients that underwent sequential low-dose 99mTc-tetrofosmin (148-185 MBq during stress and 296-370 MBq at rest) MPI with Alcyone and Discovery 670 cameras. Quantitative (% tracer's uptake) and semi-quantitative analyses of perfusion data were performed for each scan. Moreover, major left ventricular (LV) functional and structural parameters were derived from each camera and compared.

RESULTS

The two cameras showed excellent correlation for segmental myocardial % uptake at stress (R = 0.90; P < 0.001) and at rest (R = 0.88; P < 0.001) with narrow Bland-Altman limits of agreement. The level of diagnostic agreement of Discovery 670 and Alcyone cameras regarding perfusion analysis was excellent (Cohen's κ 0.85). Similarly, the two cameras showed excellent correlation in the evaluation of LV ejection fraction (R = 0.95), peak filling rate (R = 0.97), and mass (R = 0.98).

CONCLUSIONS

Our preliminary results suggest that MPI with an all-purpose Discovery 670 CZT-SPECT camera is feasible, comparing well with the current state-of-the-art technology.