2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance

Nazir, Muhummad Sohaib; Shome, Joy; Villa, Adriana; Ryan, M. J.; Kassam, Ziyan; Razavi, Reza; Kozerke, Sebastian; Ismail, Tevfik F; Perera, Divaka; Chiribiri, Amedeo; Plein, Sven

doi:10.1093/ehjci/jeab103

Cited by 6 publications

(5 citation statements)

References 25 publications

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“…Three dimensional (3D) MR perfusion imaging techniques offer increased spatial coverage, but commonly have prolonged acquisition times leading to increased susceptibility to breathing motion ( 9 ) and limited in-plane spatial resolution ( 10 ). Furthermore, 2D high resolution images were shown to be more sensitive than 3D images with lower resolution to ischemia ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Adam,

Kowalik,

Tyler

et al. 2024

Front. Cardiovasc. Med.

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BackgroundSimultaneous multi-slice (SMS) bSSFP imaging enables stress myocardial perfusion imaging with high spatial resolution and increased spatial coverage. Standard parallel imaging techniques (e.g., TGRAPPA) can be used for image reconstruction but result in high noise level. Alternatively, iterative reconstruction techniques based on temporal regularization (ITER) improve image quality but are associated with reduced temporal signal fidelity and long computation time limiting their online use. The aim is to develop an image reconstruction technique for SMS-bSSFP myocardial perfusion imaging combining parallel imaging and image-based denoising using a novel noise map estimation network (NoiseMapNet), which preserves both sharpness and temporal signal profiles and that has low computational cost.MethodsThe proposed reconstruction of SMS images consists of a standard temporal parallel imaging reconstruction (TGRAPPA) with motion correction (MOCO) followed by image denoising using NoiseMapNet. NoiseMapNet is a deep learning network based on a 2D Unet architecture and aims to predict a noise map from an input noisy image, which is then subtracted from the noisy image to generate the denoised image. This approach was evaluated in 17 patients who underwent stress perfusion imaging using a SMS-bSSFP sequence. Images were reconstructed with (a) TGRAPPA with MOCO (thereafter referred to as TGRAPPA), (b) iterative reconstruction with integrated motion compensation (ITER), and (c) proposed NoiseMapNet-based reconstruction. Normalized mean squared error (NMSE) with respect to TGRAPPA, myocardial sharpness, image quality, perceived SNR (pSNR), and number of diagnostic segments were evaluated.ResultsNMSE of NoiseMapNet was lower than using ITER for both myocardium (0.045 ± 0.021 vs. 0.172 ± 0.041, p < 0.001) and left ventricular blood pool (0.025 ± 0.014 vs. 0.069 ± 0.020, p < 0.001). There were no significant differences between all methods for myocardial sharpness (p = 0.77) and number of diagnostic segments (p = 0.36). ITER led to higher image quality than NoiseMapNet/TGRAPPA (2.7 ± 0.4 vs. 1.8 ± 0.4/1.3 ± 0.6, p < 0.001) and higher pSNR than NoiseMapNet/TGRAPPA (3.0 ± 0.0 vs. 2.0 ± 0.0/1.3 ± 0.6, p < 0.001). Importantly, NoiseMapNet yielded higher pSNR (p < 0.001) and image quality (p < 0.008) than TGRAPPA. Computation time of NoiseMapNet was only 20s for one entire dataset.ConclusionNoiseMapNet-based reconstruction enables fast SMS image reconstruction for stress myocardial perfusion imaging while preserving sharpness and temporal signal profiles.

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Section: Introductionmentioning

confidence: 99%

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Adam,

Kowalik,

Tyler

et al. 2024

Front. Cardiovasc. Med.

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“… 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 These have enabled 3D whole heart myocardial perfusion, 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 although a recent study has shown that 2D high resolution scans with smaller temporal footprint are more sensitive for detecting ischemia. 50 Recently, simultaneous multislice (SMS) imaging has gained interest in CMR for improved coverage with minimal loss in image quality and SNR. 21 , 23 , 51 , 52 , 53 , 54 Yet, ultra‐high acceleration rates are still limited when SMS imaging is combined with in‐plane acceleration due to noise amplification.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, compressed sensing, low‐rank methods, and their combinations have been adopted to perfusion CMR reconstruction to enable higher acceleration rates 18–40 . These have enabled 3D whole heart myocardial perfusion, 41–49 although a recent study has shown that 2D high resolution scans with smaller temporal footprint are more sensitive for detecting ischemia 50 . Recently, simultaneous multislice (SMS) imaging has gained interest in CMR for improved coverage with minimal loss in image quality and SNR 21,23,51–54 .…”

Section: Introductionmentioning

confidence: 99%

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Demirel

Shenoy

Moeller

et al. 2022

Magnetic Resonance in Med

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To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). Methods: First-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT. Results: Results on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. Conclusion: PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.

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“…There are also a number of benefits associated with increased in‐plane spatial resolution, such as reduced dark rim artifacts, 12,13 improved detection of subendocardial perfusion defects, 14,15 and assessment of transmural perfusion gradients 15,16 . In addition, we have recently shown that high‐resolution CMR perfusion imaging provides a greater ischemic burden compared to 3D whole‐heart imaging (with lower in‐plane spatial resolution) and, therefore, may be more sensitive for the detection of ischemia 17 . Thus, there is a desire to achieve full LV coverage and high in plane spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

“…15,16 In addition, we have recently shown that high-resolution CMR perfusion imaging provides a greater ischemic burden compared to 3D whole-heart imaging (with lower in-plane spatial resolution) and, therefore, may be more sensitive for the detection of ischemia. 17 Thus, there is a desire to achieve full LV coverage and high in plane spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous multislice steady‐state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T

McElroy

Ferrazzi

Nazir

et al. 2022

Magnetic Resonance in Med

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To implement and evaluate a simultaneous multi-slice balanced SSFP (SMS-bSSFP) perfusion sequence and compressed sensing reconstruction for cardiac MR perfusion imaging with full left ventricular (LV) coverage (nine slices/heartbeat) and high spatial resolution (1.4 × 1.4 mm 2 ) at 1.5T. Methods:A preliminary study was performed to evaluate the performance of blipped controlled aliasing in parallel imaging (CAIPI) and RF-CAIPI with gradient-controlled local Larmor adjustment (GC-LOLA) in the presence of fat. A nine-slice SMS-bSSFP sequence using RF-CAIPI with GC-LOLA with high spatial resolution (1.4 × 1.4 mm 2 ) and a conventional three-slice sequence with conventional spatial resolution (1.9 × 1.9 mm 2 ) were then acquired in 10 patients under rest conditions. Qualitative assessment was performed to assess image quality and perceived signal-to-noise ratio (SNR) on a 4-point scale (0: poor image quality/low SNR; 3: excellent image quality/high SNR), and the number of myocardial segments with diagnostic image quality was recorded. Quantitative measurements of myocardial sharpness and upslope index were performed.Results: Fat signal leakage was significantly higher for blipped CAIPI than for RF-CAIPI with GC-LOLA (7.9% vs. 1.2%, p = 0.010). All 10 SMS-bSSFP perfusion datasets resulted in 16/16 diagnostic myocardial segments. There were no significant differences between the SMS and conventional acquisitions in terms of image quality (2.6 ± 0.6 vs. 2.7 ± 0.2, p = 0.8) or perceived SNR (2.8 ± 0.3 vs. 2.7 ± 0.3, p = 0.3). Inter-reader variability was good for both image quality (ICC = 0.84) and perceived SNR (ICC = 0.70). Myocardial sharpness was improved using the SMS sequence compared to the conventional Sarah McElroy and Giulio Ferrazzi contributed equally to this work.

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2D high resolution vs. 3D whole heart myocardial perfusion cardiovascular magnetic resonance

Cited by 6 publications

References 25 publications

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Fast reconstruction of SMS bSSFP myocardial perfusion images using noise map estimation network (NoiseMapNet): a head-to-head comparison with parallel imaging and iterative reconstruction

Signal intensity informed multi‐coil encoding operator for physics‐guided deep learning reconstruction of highly accelerated myocardial perfusion CMR

Simultaneous multislice steady‐state free precession myocardial perfusion with full left ventricular coverage and high resolution at 1.5 T

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