Quantitative cardiovascular magnetic resonance perfusion mapping as a guide for diagnosis

Background

There is a lack of gold-standard non-invasive clinical markers derived from quantitative cardiovascular magnetic resonance (CMR) stress perfusion.

Purpose

This study aimed to compare quantification indices testing the hypothesis that they can discriminate possible normal from abnormal groups including microvascular dysfunction (MVD), coronary artery disease (CAD), and non-diagnostic tests due inappropriate response to the stressor agent.

Methods

Four-hundred and thirty-six consecutive patients (n=436, mean age 59.5 yrs) with typical angina and/or risk factors for CAD underwent stress CMR perfusion imaging using a dual-sequence quantitative spiral pulse protocol to estimate quantitative markers on a 1.5 T scanner. Anatomical coronary information, risk factors, and myocardial infarct were evaluated. Myocardial perfusion reserve values (MPR) were adjusted for rate-pressure product. For each perfusion assessment 3 short axis slice locations were imaged per heartbeat over a 60-heartbeat acquisition during an IV bolus of 0.05 mmol/kg of gadolinium contrast. Patients were divided into 4 groups: A) normal perfusion study; B) positive perfusion study due to epicardial coronary artery disease; C) positive perfusion study due to microvascular coronary artery disease; and D) non-diagnostic perfusion study due to inappropriate response to pharmacological stress.

Results

Stress myocardial blood flow (SMBF) and mean adjusted MPR differed between patients with no ischaemia and those clinically diagnosed with MVD (2.41±0.75 vs 1.81±0.52 mL/g/min, p<0.001, 2.78±0.94 vs 2.39±1.02, p=0.009, respectively). Patients deemed to have inadequate hyperaemia as opposed to inducible ischaemia had the lowest mean SBF of 1.25±0.32 vs 1.80±0.61 mL/g/min (p<0.001); a cut-off value of <1.34 mL/g/min had the best predictive diagnostic accuracy for inadequate stress (area under curve [AUC] 0.875). Of note, comparing MVD vs CAD (single, 2-vessels, multivessel disease) without infarction stress pulmonary transit time (PTT) (centroid 6.9±0.72 vs 5.95±0.58, p=0.026), SMBF (1.80 vs 2.10 mL/g/min, p=0.0075), stress endo (1.60 vs 1.94 mL/g/min, p=0.0013), and stress epi (1.94 vs 2.21 mL/g/min, p=0.021) differed significantly between the two groups. The presence of infarction was shown also to be a significant discriminator between the two groups in logistic regression analysis (OR: 8.3, p=0.030).

Conclusions

This study showed fully quantitative stress markers may be useful in discriminating MVD and CAD patients as well as excluding patients with inadequate hyperaemia.

Funding Acknowledgement

Type of funding sources: None.

Artificial intelligence approach improves ischaemia prediction beyond the known pretest probability scores

Background

Pre-test probability (PTP) assessment is crucial in the assessment of patients with suspected ischaemia/coronary artery disease. Presently there is no established role for artificial intelligence in estimating PTP.

Purpose

Comparison of a recently developed memetic pattern based algorithm (MPA) with the diagnostic power of established scores in predicting ischaemia on positron emission tomography myocardial perfusion imaging (PET MPI).

Methods

Consecutive patients undergoing Rubidium PET MPI for routine clinical evaluation of ischaemia were included. The PTP for each patient was estimated by MPA, the Diamond and Forrester scores (DFS), the PTP models from the European Society of Cardiology Guidelines from 2013 (ESC 2013) and 2019 (ESC 2019) and the Framingham scores (FRS). The PET MPI studies were assessed for the presence of ischaemia. Ischaemia was defined as a summed difference score (SDS) ≥2.

Results

The mean age of the 531 patients was 66±11 years, 34% were female, and 50% had known prior coronary artery disease; 208 patients had evidence of ischaemia. No ischaemia was found in 323 patients. The areas under the curve (AUC) are shown in the figure. The artificial intelligence based MPA provided an AUC of 0.76, PTP (ESC 2013) AUC of 0.67, PTP (ESC 2019) AUC 0.67, DFS AUC 0.56, FRS 0.68.

Conclusion

The MPA outperforms the established scores for PTP assessment of the ESC, DFS and FRS in prediction of ischaemia on PET MPI. It has the potential to improve the accuracy of the established diagnostic algorithm for CAD and ischaemia.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): The study was in part funded by the Swiss Heart Foundation.

Splenic switch off as a novel marker for adenosine response in 13N-ammonia PET myocardial perfusion imaging – a pilot study

Background

Positron emission tomography myocardial perfusion imaging (PET MPI) is a robust and excellent tool for assessing ischemia. So far, however, no methodology has been established to distinguish truly reduced MFR due to microvascular dysfunction or three-vessel coronary disease (CAD) from seemingly impaired MFR due to inadequate adenosine response. Conversely, for cardiac stress magnetic resonance (CMR) the adenosine induced splenic switch-off (SSO) sign has been proposed as a potential marker for adequate adenosine response.

Purpose

We assessed the feasibility of detecting SSO in adenosine stress 13N-ammonia PET MPI using SSO in CMR as the standard of reference.

Methods

50 patients underwent simultaneous PET MPI and CMR on a hybrid PET/MR device with co-injection of 13N-ammonia and a gadolinium-based contrast agent during rest and adenosine-induced stress. In CMR, SSO was assessed qualitatively and quantitatively by calculating the ratio of the peak signal intensity of the spleen during stress over rest (SIR). Similarly, in PET MPI the splenic signal activity ratio (SAR) was calculated as the proportion of the maximal standard uptake value of the spleen in stress and rest. Additionally, MFR was quantified in PET MPI.

Results

Visual SSO in CMR was present in 37 (74%) patients, whereas 13 patients had no SSO. The median SIR in CMR was significantly lower in patients with visual SSO compared to patients without visual SSO (0.57 [IQR 0.49–0.62] vs. 0.89 [IQR 0.76–0.98]; p<0.001). Similarly, median SAR in PET was significantly lower in patients with visual SSO in CMR compared to patients without visual SSO (0.4 [IQR 0.32–0.45] vs. 0.8 [IQR 0.47–0.98]; p<0.001). SIR correlated significantly with SAR (r=0.4, p<0.05). Mean MFR was significantly higher in patients with visual SSO compared to patients without visual SSO (3.38±0.86 vs. 2.53±0.84; p<0.05).

Conclusion

Similarly to CMR, SSO can be detected in 13N-ammonia PET MPI. This might help distinguish adenosine non-responders from patients with truly impaired MFR due to microvascular dysfunction or multivessel CAD.

Figure 1. Splenic switch off (*) illustrated on transaxial 13N-ammonia PET MPI stress (A) compared to rest perfusion images (B) and similarly in stress (C) and rest (D) short axis CMR (**) in the same patient during adenosine induced stress and co-injection of 13N-ammonia and a gadolinium based contrast agent, acquired on a hybrid PET/MR device.

Funding Acknowledgement

Type of funding source: Public grant(s) – National budget only. Main funding source(s): Swiss National Science Foundation (SNSF)