Left ventricular sphericity indexes by gated single photon emission computed tomography myocardial perfusion imaging: potential opportunities and challenges for cardiac remodeling and clinical prognosis—a narrative review

Introduction

Left ventricular (LV) remodeling refers to the adaptive changes in myocardium and vascular system in response to adverse hemodynamic, metabolic, and inflammatory stimuli. This process involves a gradual transformation of ventricular mass, tissue composition, and volume, ultimately leading to alterations in the structural and functional properties of the heart (1). This self-sustaining adaptive mechanism represents the initial phase in the progression toward heart failure (HF) and is associated with LV dysfunction, arrhythmias, and major adverse cardiovascular events (MACEs). These conditions can develop independently of coronary artery disease (CAD) (2-4). Currently, most studies of LV remodeling (LVR) focus on using LV ejection fraction (LVEF) and LV volume parameters to predict the patient outcomes, conduct risk stratification, and guide treatment decisions. Nevertheless, there is a comparatively less emphasis on the changes of LV shape, an alternative approach for quantifying the LV geometric configuration (5). This review summarizes the morphological changes associated with LVR, quantified through gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (G-MPI), and highlights the significance of LV sphericity indexes (LVSI) in evaluating treatment efficacy and risk stratification of cardiac events. Furthermore, it aims to propose several critical and unresolved questions, establish a conceptual framework for future research, and propose the integration of LVSI into mainstream clinical practice. We present this article in accordance with the Narrative Review reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1459/rc).

Methods

The search strategy is summarized in Table 1. A comprehensive literature search was conducted to identify relevant publications pertaining to LVSI. PubMed was searched for articles published using a combination of keywords, including “left ventricular sphericity index”, “shape index”, “eccentricity index”, “left ventricular remodeling”, and “prognosis”. Both original studies and review articles were considered. Additionally, the references of retrieved articles were manually screened to identify further relevant publications. The final selection of studies included in this narrative review was based on their relevance to the topic, originality, and contribution to the understanding the clinical value of LVSI.

Pathophysiological changes and related mechanisms of LVR

The term “remodeling” was introduced in 1984 to describe the structural changes in both infarcted and non-infarcted myocardium following acute myocardial infarction (AMI) (6). The process of LVR refers to how the heart adapt to different stimuli, such as mechanical stress, hormonal influences, and genetic factors, by altering the size, shape, and function of LV (7). This adaptation can be either beneficial or detrimental to cardiac performance (7). During conditions such as pregnancy or endurance exercise, LVR is classified as physiological, adaptive, or compensated. Conversely, when the heart experiences pathological stress from pressure or volume overload, this process is termed maladaptive or decompensated LVR (8).

LVR refers to complex changes in the structure and function of the heart, often triggered by conditions such as chronic pressure or volume overload, myocardial infarction (MI), and HF. Chronic pressure overload, as observed in arterial hypertension or aortic valve stenosis, typically leads to concentric hypertrophy of the LV, with notable gender differences in LVR (9). Females generally maintain higher peak LV pressures and exhibit a lower LV mass index, resulting in a more concentric pattern of hypertrophy (10). In contrast, males demonstrate increased extracellular volume, indicating a greater degree of fibrosis (9). Conversely, chronic volume overload, seen in conditions such as aortic and mitral regurgitation, induces eccentric LVR, with females exhibiting a more pronounced increase in extracellular volume, suggesting a heightened risk of fibrosis in these scenarios (9). In brief, pressure overload leads to an increase in wall thickness, while volume overload results in an elongation of the LV (2).

After MI, decompensated LVR of the cardiac structure significantly increases the risk of HF and reduces life expectancy (7). This complex process involves changes in LV geometry and fibrosis, leading to both systolic and diastolic dysfunction (2). Inflammatory responses and immune system activation are critical for healing post-MI; however, chronic inflammation can impair cardiac function (7,11-13). Ischemia, reperfusion injury, and reactive oxygen species further exacerbate myocardial damage. Metabolic changes and neurohormonal activation, such as the sympathetic nervous system and the renin-angiotensin-aldosterone system, also influence LVR and the onset of HF (7). The cardio-renal axis plays a crucial role, with the interplay between the heart and kidneys affecting disease progression and LVR (14,15).

HF is characterized by the transformation of the LV from an ellipsoidal to a spherical shape, which impairs contraction and may worsen mitral regurgitation (16,17). Remodeling affects not only the LV function but also the right ventricle function and left atrium (18). Right ventricular remodeling often results from LV failure, which increases afterload by elevating pulmonary venous and ultimately pulmonary arterial pressure, or from valvular disease (18). Increased LV stiffness can lead to remodeling of the left atrium and the development of atrial fibrillation (19). Furthermore, changes in cardiac energy metabolism during remodeling, such as reduced energy production and shifts in substrate utilization, can worsen the severity of HF (2,20).

The process of LVR leads to a progressive departure from the cardiac natural, efficient bullet-shaped configuration, resulting in an increased LVSI. The typical oval shape of the LV may transform into a more spherical form due to the progression of eccentric hypertrophy or following MI (17). Numerous studies indicate that parameters describing the shape of LV can improve the differentiation between normal and pathological conditions (21,22). This is because deviations in LV shape can be detected before noticeable alterations in LV systolic function (21).

Imaging techniques for LVSI

General concept

Techniques for assessing LVSI vary among nuclear imaging, echocardiography, and cardiac magnetic resonance imaging (MRI), resulting in distinct normative ranges for each modality (23,24). The terminology used to describe ventricular morphology also differs (Figure 1). We use the terms shape index (SI) and eccentricity index (EI) to remain consistent with the terminology used in existing SPECT G-MPI studies (3,25). SI refers to the ratio of the LV short-axis to the LV long-axis (25,26). Additionally, some studies define LVSI as a ratio calculated by dividing the end-diastolic volume (EDV) or end-systolic volume (ESV) by the volume of a sphere whose diameter equals the LV long axis (21,27).

Figure 1 Quantitative assessment of LVSI using various imaging modalities and its clinical applications in G-MPI. MPI derived SI is defined as the ratio of the maximum short-axis diameter to the long-axis length measured from the endocardial surface of the 3D contours. EDSI and ESSI were obtained at the end-diastole and end-systole, respectively. EI is derived from an iterative approach to constructing a 3D ellipsoidal model that best fits the mid-myocardial segment of the LV. Measurements are obtained for the diameter of the short-axis and the long-axis length. 3D, three-dimensional; CAD, coronary artery disease; CHF, congestive heart failure; CRT, cardiac resynchronization therapy; D, the maximum short-axis dimensions of the LV; ED, end-diastolic; EDSI, end-diastolic shape index; EI, eccentricity index; ES, end-systolic; ESEI, end-systolic eccentricity index; ESSI, end-systolic shape index; IDC, idiopathic dilated cardiomyopathy; INOCA, ischemia with non-obstructive coronary artery disease; L, the maximum long-axis length of the LV; LV, left ventricular; LVSI, left ventricular sphericity indexes; G-MPI, gated myocardial perfusion imaging; MPI, myocardial perfusion imaging; MRI, magnetic resonance imaging; SI, shape index.

Radionuclide-based molecular imaging

Assessments of myocardial perfusion, metabolism, and function can be obtained operator-independently by radionuclide molecular imaging. Figure 1 provides a diagrammatic representation of LVSI (28). Both SI and EI are measurements related to the sphericity of the LV. EI captures the ventricular structure in three-dimensional (3D), whereas SI reflects the most extreme two-dimensional (2D) representation of the ventricle’s shape (25). The primary literature concerning nuclear imaging of LVSI are summarized in Table 2.

SI

SI was defined as the ratio of the maximum short-axis to long-axis dimensions of the LV (25). The process began by identifying the maximum in-plane dimension of the LV on the 3D contours obtained through the Quantitative Gated SPECT (QGS) algorithm for each short-axis plane. By utilizing the endocardial surface as the boundary and determining the greatest distance between any two endocardial points within the same plane. This method was then applied to identify the maximum short-axis endocardial measurement from all slices of the images captured at end-diastole. The maximum endocardial short-axis dimension at end-systole was determined by measuring the distance between the points on the endocardium corresponding to the location of the largest short-axis extent found during diastole. To measure the longest myocardial axis, the greatest distance from the most apical aspect of the endocardial surface to the center of the cardiac valve plane was calculated separately for both end-systolic and end-diastolic states. The end-systolic SI (ESSI) and end-diastolic SI (EDSI) were then calculated as the ratio of the maximum short-axis dimension to the maximum long-axis dimension for the end-systolic and end-diastolic states, respectively, derived from the rest and stress images. Additionally, SI was calculated using Quantitative Perfusion SPECT (QPS) software for non-gated data. The SI value ranges from 0 to 1, a higher value indicates a more spherical shape, which is associated with worse clinical outcomes.

EI

EI was calculated through an iterative process that shapes a 3D ellipsoidal model to optimally match the mid-myocardial segment of the LV. This process involves measuring the short-axis diameters (x and y) and the long-axis length (z) (Figure 1). The EI was then determined using the formula (1 − (xy/z²))^0.5. It serves as an index of LV elongation, with value ranges from 0 to 1. A lower value indicates a more spherical shape and a worse prognosis. EI was also obtained using QGS and QPS software, both at rest and during stress.

Software analysis and methodology

In 1997, Bonow (34) first suggested that automated contour-generating algorithms might be affected by areas of absent perfusion or subendocardial ischemia, which could lead to reduced endocardial tracer uptake, making it difficult to accurately identify the endocardial border. This could cause an overestimation of the severity of wall motion abnormalities and an underestimation of the chordal shortening and LVEF (34). Several studies have reported acceptable accuracy of automated contour detection in patients with extensive perfusion defects; however, these findings should be interpreted with caution (35-37). Although the validation studies were typically conducted in controlled settings at expert centers, the prevailing evidence suggests that these algorithms are generally robust, likely because they utilize the myocardial surface curvature surrounding the defect to ensure a smooth gradient transition (28), the generalizability of these findings to all clinical software platforms and broader patient populations requires further validation due to the approximation of wall thickness accounts for the inherent limitations of SPECT resolution (28).

Most previous studies utilized the QGS and QPS software packages (Cedars-Sinai Medical Center) to automatically calculate LV volumes, LV function, and LVSI (1,3,25,28). Although software versions vary, their core algorithms remain consistent. Standard protocols typically involve ordered subsets expectation maximization (OSEM) reconstruction with appropriate filters and frame gating. It is important to note that these automated algorithms, while highly reproducible, have inherent limitations. Their accuracy may be reduced in patients with severe perfusion defects, where the algorithm might extrapolate the endocardial border. Additionally, LVSI values may vary slightly between software versions and platforms. Therefore, consistent imaging and processing protocols within institutions are essential to ensure reliable and comparable results.

Other imaging techniques

During echocardiographic examinations, the linear dimensions of the LV are obtained following the guidelines established by the American Society of Echocardiography (38). Standard apical four-chamber views are acquired to measure the maximum longitudinal and transverse diameters of the LV at the end of systole and diastole. Previous studies have utilized various formulas to calculate LVSI (27,39-42) (Table 3). One method involves analyzing 2D echocardiographic images to derive the LV EDV/ESV, then dividing them by the volume of a sphere whose diameter is corresponds to the LV’s major end-diastolic long-axis (27,41). An alternative, a simpler approach to assessing LV configuration is the ratio of the longitudinal and transverse diameters (39). However, this method can be challenging to interpret in cases with regional wall motion abnormalities of the LV (39). In cardiac MRI, the endocardial contour of the LV myocardium is delineated at end-diastole and end-systole (23). In the four-chamber view, the long-axis diameter of the LV is measured at both end-diastole and end-systole, defined as the distance between the mitral valve plane and the apex of the LV (23,27). Similarly, the transverse diameter of the LV is measured in the four-chamber view at end-diastole and end-systole (23). LVSI at end-diastole and end-systole is calculated using a specific formula (Table 3). Additionally, some studies use a simplified method to calculate the LV spherical parameter by dividing the maximum diameter of the LV short-axis view by the long-axis diameter (21). Previous investigations have also described the SI as the ratio of the LV long-axis length to the LV diameter at both end-systole and end-diastole (43).

Comparison between different imaging methods

Initial assessments of LVSI utilized either invasive angiocardiographic films or echocardiography (45-47). Echocardiography is widely available and cost-effective. Advanced technologies can also be used for early detection of LV dysfunction due to their ability to rapidly quantify complex cardiac motion. However, manual assessments are subject to interobserver variability and depend on the skill level of the operator. Cardiac MRI is considered as the gold standard for volumetric and functional assessment, offering superior spatial resolution and tissue characterization without radiation exposure. Its ability to precisely delineate the endocardial border enables highly accurate 3D shape analysis. Nevertheless, MRI is less accessible, more expensive, requires longer acquisition time, and is contraindicated in certain patients with implanted metallic medical devices. G-MPI provides an automated and comprehensive assessment of LV geometry through computer software. Its deterministic operation ensures high reproducibility, except when contours must be defined manually. Consequently, it surpasses manual or semi-automated methods that require user input, such as selecting slices, delineating the LV, or pinpointing the center cavity, in terms of reliability and consistency.

While 2D imaging provides a simple and accessible estimate of LV shape, it is inherently limited by geometric assumptions and plane selection, especially in hearts with severe regional wall motion abnormalities. In contrast, 3D-based methods utilize full volumetric data to derive LVSI. The 3D approach is inherently more sensitive to localized shape alterations than assessments, providing a more precise depiction of subtle changes in LV geometry. However, 3D methods require specialized equipment and software, and their normative values are modality-specific (28).

Clinical applications of G-MPI LVSI

Prognostic prediction

Predictive value of LVSI in CAD

It is well known that adverse LVR is significantly associated with MACE in CAD, leading to a deterioration in cardiac function and contributing to the increased risks of cardiovascular morbidity and mortality across various cardiovascular disorders (2,3).

Recent studies have elucidated the complex characteristics of ischemia with non-obstructive CAD (INOCA), highlighting its diverse clinical presentations and its distinct association with increased risks of MACE and elevated overall mortality rates (48,33). Our preliminary investigation revealed that LVSI greater than 0.65 was significantly and independently correlated with the incidence of MACE in INOCA patients, suggesting its potential as a reliable marker for the clinical assessment and management of INOCA (33). However, this finding was based solely on resting state, further validation under stress condition is warranted.

Multiple studies have further investigated the value of LVSI in the risk stratification and prognosis prediction for patients with suspected or known CAD (25,28,31,32). The pioneering study by Abidov et al. (28) was instrumental in evaluating the prognostic significance of LVSI, as assessed by G-MPI in patients with suspected CAD and normal myocardial perfusion. An abnormal ESSI (>0.54) was associated with reduced event-free survival. Age, diabetes, and ESSI were identified as independent predictors of cardiac events. In 2021, Miller and colleagues (25) first identified post-stress alterations of LVSI as novel risk markers, demonstrating their superiority compared to absolute values measured at rest or during stress alone. After adjusting for multiple covariates, they found that post-stress changes in LVSI were independently correlated with an increased risk of MACE and significantly improved the risk classification. The alterations in LV morphology indicated by these indexes demonstrated substantial prognostic utility and should be incorporated into the risk estimation protocols for patients undergoing G-MPI. This advancement in understanding the predictive capacity of post-stress changes in ventricular morphology has the potential to enhance the clinical decision-making in CAD patients. Wu et al. conducted an investigation for the prognostic significance of resting LVSI in 727 patients with CAD and preserved LVEF (32). This study revealed that abnormal LVSI (SI_QPS >0.65) served as reliable predictors of MACE, even after adjusting for total perfusion deficit and LVEF. These findings underscore the importance of incorporating LVSI into clinical assessments for patients with suspected or known CAD, potentially enhancing the risk prediction and clinical decision-making (32). In recent years, with the increasing use of dedicated cardiac Cadmium-Zinc-Telluride (CZT) cameras for SPECT imaging has provided an opportunity to achieve more accurate assessments of LV structure and mechanics. Gimelli et al. (31) investigated 456 patients with chest pain who underwent G-MPI with a CZT camera by calculating LVSI at rest and stress, respectively.

G-MPI is considered a benchmark technique for assessing myocardial ischemia. However, in patients with multivessel CAD, the accuracy of MPI may be reduced (30). Additional evaluation of LV function, including changes such as stress-induced LV dilation and a decline in systolic capacity in comparison with rest study, helps identify individuals with multivessel CAD and predicts unfavorable clinical outcomes (31). One study found a strong association between CAD severity and stress-related structural and functional changes of LV in patients with normal LV systolic function (30). Patients with multivessel CAD often exhibit post-stress LV stunning, dilation, and reduced EI, but only after exercise stress testing, not after vasodilator stress (30). This suggests that an exercise stress test may be necessary to induce the level of ischemia that leads to temporary LV structural remodeling and dysfunction. The findings indicate that reduced EI after exercise stress can specifically predict multivessel CAD by identifying patients with a temporary increase in LV sphericity, independent of LV functional and perfusion information (30).

Collectively, functional alterations in LVEF and volumes typically manifest later in the disease continuum. However, LVSI capture early morphological changes that precede significant functional deterioration. The transition from an elliptical to a spherical LV geometry represents a maladaptive response to hemodynamic stress, which may occur even in the presence of preserved LVEF. Thus, LVSI offer a sensitive and early biomarker of remodeling, potentially identifying at-risk patients before overt systolic dysfunction or volume expansion becomes apparent.

LVR and congestive HF (CHF)

LVR is a predictive indicator or variable for CHF. Previous studies have primarily utilized information obtained from echocardiography to emphasize the clinical and prognostic implications associated with LV geometric structures in the pathophysiology of CHF (28,46). In a study involving 186 individuals at low risk of CAD (<5%), prior research found that patients with CHF had higher LVSI compared to normal control subjects (EDSI, 0.64±0.07 vs. 0.60±0.05; ESSI, 0.48±0.09 vs. 0.42±0.06; all P<0.05). The repeatability of LVSI was verified in 52 individuals with ischemic cardiomyopathy, demonstrating excellent consistency. These preliminary findings suggest that LVSI may be clinically important for managing patients with CHF; however, further prospective studies are needed to confirm its clinical utility (28).

Diabetes and early LV changes

Diabetes is a major risk factor for LVR and HF, often causing significant myocardial changes before symptoms appear (49). Diabetes induces LVR through multiple mechanisms, including hyperglycemia-induced myocardial cell injury, endocrine disorders, inflammation, oxidative stress, non-enzymatic glycation, and myocardial metabolic abnormalities, all of which adversely affect cardiac function (50). These changes can lead to HF, making the early identification of diabetic patients at risk for developing LVR crucial for improving outcomes (4). Nappi et al. (1) analyzed 1,168 patients with normal myocardial perfusion and ejection fraction on stress G-MPI, comparing diabetic and nondiabetic individuals for the first time. After propensity score matching for clinical variables and coronary risk factors, diabetic patients exhibited higher EDSI and ESSI (>0.54), indicating early LVR. This suggests that G-MPI can be valuable for the early detection of LV changes in diabetic patients, even before clinical symptoms emerge (1).

Predicting therapy response using LVSI

β-adrenergic blocking agents have demonstrated the potential to improve symptoms and survival in HF patients; however, their effects vary among individuals, making it challenging to accurately predict patient responses (29). In the study by Fukuchi et al., application of LVSI provided a novel approach to evaluating the geometric cardiac shape in 38 patients with idiopathic dilated cardiomyopathy (IDC) before and after β-blocker therapy (29). The results showed that patients with a poor response (LVEF increase of less than 10%) had a smaller LVSI, whereas those with a good response (LVEF increase of 10% or more) had a larger LVSI. This significant difference suggests that LVSI could be used to assess the responsiveness to β-blocker therapy or other treatment strategy.

Cardiac resynchronization therapy (CRT) has become a vital treatment tool for managing patients with advanced symptomatic HF. Super-responders exhibit significant improvements in quality of life, HF symptoms, LV function, along with reverse LVR following CRT, and are expected to achieve better long-term outcomes (22,51). A pioneer study, which included 199 patients from multiple centers who met standard CRT criteria, investigated the predictive value of LVSI measured by G-MPI in CRT patients (22). The results demonstrated that LVSI was a significant independent predictor of super-response to CRT, LV morphology may play a critical role in determining the effectiveness of CRT. A typical G-MPI imaging illustrating LV shape and sphericity characteristics of both before and after CRT is presented in Figure 2.

Figure 2 Representative MPI images taken before and after CRT treatment are presented. The images display a serial three-dimensional representation of the gated end-diastolic and end-systolic (grid), both prior and following treatment. The LV of the patient before treatment appeared spherical (sphericity parameters are indicated in the figure, LVEF 8%, EDV 246 mL, ESV 226 mL). After treatment, the shape of the LV exhibited an ellipse form (the values of shape index decreased while the eccentricity index increased, LVEF 32%, EDV 75 mL, ESV 51 mL). ANT, anterior; APEX, apex; CRT, cardiac resynchronization therapy; ED, end-diastolic; EDSI, end-diastolic shape index; EDV, end-diastolic volume; EI, eccentricity index; ES, end-systolic; ESEI, end-systolic eccentricity index; ESSI, end-systolic shape index; ESV, end-systolic volume; INF, inferior; LV, left ventricular; LVEF, left ventricular ejection fraction; MPI, myocardial perfusion imaging; SEPT, septal; SI, shape index.

Clinical implementation and limitations

The integration of LVSI into routine clinical practice faces significant challenges, especially the lack of evidence supporting the clinical application. It is important to note that the vast majority of studies on G-MPI derived LVSI are retrospective. While these studies consistently demonstrate strong and independent prognostic value, the evidence has not yet been incorporated into formal clinical guideline recommendations, which require further validation from large, prospective, and multicenter trials.

Beyond the need for higher-level evidence, several methodological challenges remain. First, variability in imaging acquisition protocols across institutions (including differences in camera systems, radiopharmaceuticals, and acquisition time) can affect the reproducibility and comparability of LVSI. Second, fully automated algorithms may lack accuracy, while manual contouring is time-consuming and subject to both inter- and intra-observer variability. Furthermore, the reproducibility of LVSI across different centers has yet to be established. Besides, global or regional ventricular dysfunction may result in alterations in ventricular loading conditions, which could induce changes in LVSI at stress (25). Variations observed in stress LVSI among patients undergoing exercise and pharmacological stress might reflect disparities in the underlying mechanisms (25). Differences in resting values indicate that population characteristics also influence the accuracy (25). When assessing the LVSI in G-MPI, it is crucial to consider the significant differences between rest and stress states, as well as the impact of gender and age on parameter interpretation.

Future research and technology integration

Measuring LVSI are crucial yet complex, involving various methods that require standardization to ensure consistent results. Recently, the application of artificial intelligence and machine learning (ML) in MPI analysis has shown promise for enhancing the diagnostic accuracy and personalized treatments. Specific applications include the development of automated segmentation tools to accurately delineate ventricular contours. Additionally, ML algorithms can facilitate predictive modeling by integrating LVSI with clinical and perfusion data to improve the risk stratification and predict MACE. However, developing robust and generalizable algorithms requires large, multi-center datasets. It is also essential to address the ethical and privacy concerns related to the data sharing and algorithm transparency to ensure the trustworthy clinical adoption.

Conclusions

The integration of LVSI into routine G-MPI enhances the prognostic value without additional radiation or imaging. These indexes provide clinically relevant insights and are recommended for inclusion in the standard reporting to improve the outcome predictions and guide therapeutic decisions in the management of cardiovascular disease. Further investigation is warranted to standardize and optimize their application.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1459/rc

Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 82171994).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-1459/coif). All authors report the funding from the National Natural Science Foundation of China (grant No. 82171994). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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