Citations

Abstract Aims Radiation therapy (RT) is an integral component of cancer therapy but associated with adverse events. Our goal was to establish risk prediction models for major adverse cardiovascular and cerebrovascular events (MACCE) after chest RT. Methods and results A retrospective study of lung/breast cancer patients who had chest RT with planning CT at Mayo Clinic between 01/2010 and 01/2014. Predictive models were developed based on weighted independent predictors using a derivation (406 lung and 711 breast cancer) and validation cohort (179 lung and 234 breast cancer). Patient characteristics, pre-RT CT for coronary artery calcification (CAC), and post-RT MACCE data were reviewed. Post-RT MACCE occurred in 6.1 and 5.6% in the derivation and validation cohort over a mean follow-up of 42 ± 13 months. Post-therapy model (C2AD2) included CAC (two points), MACCE history (two points), age ≥74 (three points), DM (two points), and mean heart radiation dose ≥ 850 mGy (two points), and pre-therapy model (C2AD) included post-therapy model parameters minus mean heart radiation dose. Both models stratified patients into three risk groups: low (0–2), intermediate (3–5), and high (≥6). Post-RT MACCE across these groups were 2.7, 8.9, and 19.8% in the derivation, and 3.9, 6.6, and 16.4% in the validation cohort for post-therapy model (C2AD2) and 2.8, 9.2, and 20.4% in the derivation and 3.7, 9.2, and 13.2% in the validation cohort for pre-therapy model. Both models showed statistically significant graded survival outcome. Conclusion Post-therapy (C2AD2) and pre-therapy (C2AD) models are simple, easy to use and effective tools to stratify breast and lung cancer patients undergoing chest radiation for post-RT MACCE.

AbstractPurposeRobust implementation of spiral imaging requires efficient deblurring. A deblurring method was previously proposed to separate and deblur water and fat simultaneously, based on image‐space kernel operations. The goal of this work is to improve the performance of the previous deblurring method using kernels with better properties.MethodsFour types of kernels were formed using different models for the region outside the collected k‐space as well as low‐pass preconditioning (LP). The performances of the kernels were tested and compared with both phantom and volunteer data. Data were also synthesized to evaluate the SNR.ResultsThe proposed “square” kernels are much more compact than the previously used circular kernels. Square kernels have better properties in terms of normalized RMS error, structural similarity index measure, and SNR. The square kernels created by LP demonstrated the best performance of artifact mitigation on phantom data.ConclusionsThe sizes of the blurring kernels and thus the computational cost can be reduced by the proposed square kernels instead of the previous circular ones. Using LP may further enhance the performance.

Abstract Purpose To demonstrate T 2 ‐weighted (single‐echo) spin‐echo (SE) imaging with near‐optimal acquisition efficiency by applying SNR‐efficient RF slice encoding and spiral readout. Methods A quadratic‐phase (frequency swept) excitation RF pulse replaced the conventional excitation in T 2 ‐weighted SE sequence to excite a thick slab that is internally spatially encoded by a variable phase along the slice direction. Highly overlapping slabs centered at every desired slice location were acquired in multiple passes, such that the entire imaging volume was excited by contiguous slabs in any given pass. Following 90° excitation, each slab was refocused with a conventional 180° RF to produce a SE signal, followed by a spiral in‐out readout. A noise‐insensitive reconstruction removed the quadratic phase in the spatial frequency domain, yielding desired slice resolution and improved SNR. Results Increasing the RF frequency sweep (hence, excitation width) allowed more frequent encoding of each slice over the multiple passes, improving final image SNR, until crosstalk ensued at excessive slab widths compared to their center‐to‐center spacing. With an optimized slab width, the proposed technique used all passes to acquire every prescribed slice, with substantially improved SNR over conventional SE or 2D‐turbo‐spin‐echo (TSE) scans. Quantitative SNR measurements indicated similar SNR as 3D‐TSE, but radiologist scoring favored 3D‐TSE, mainly because of spiral‐related artifacts and possibly because of regularized reconstructions in 3D‐TSE. Conclusion Using SNR‐efficient slice excitation scheme and spiral readout helped eliminate SNR and temporal inefficiencies in conventional T 2 ‐weighted imaging, yielding SNR independent of TR or number of passes.

Purpose The combination of SENSE and spiral imaging with fat/water separation enables high temporal efficiency. However, the corresponding computation increases due to the blurring/deblurring operation across the multi‐channel data. This study presents two alternative models to simplify computational complexity in the original full model (model 1). The performances of the models are evaluated in terms of the computation time and reconstruction error. Methods Two approximated spiral MRI reconstruction models were proposed: the comprehensive blurring before coil operation (model 2) and the regional blurring before coil operation (model 3), respectively, by altering the order of coil‐sensitivity encoding process to distribute signals among the multi‐channel coils. Four subjects were recruited for scanning both fully sampled T 1 ‐ and T 2 ‐weighted brain image data with simulated undersampling for testing the computational efficiency and accuracy on the approximation models. Results Based on the examples, the computation time can be reduced to 31%–47% using model 2, and to 39%–56% using model 3. The quality of the water image remains unchanged among the three models, whereas the primary difference in image quality is in the fat channel. The fat images from model 3 are consistent with those from model 1, but those from model 2 have higher normalized error, differing by up to 4.8%. Conclusion Model 2 provides the fastest computation but exhibits higher error in the fat channel, particularly in the high field and with long acquisition window. Model 3, an abridged alternative, is also faster than the full model and can maintain high accuracy in reconstruction.

PurposeTo show that the acoustic noise of spiral MRI can be reduced by derating the gradients with minimal penalty to image quality and scan time, and to illustrate an algorithm for optimal choice of derating parameters.Theory and MethodsAcoustic noise level was measured and compared for various values of maximum gradient amplitude and slew rate for T1‐weighted spin‐echo spiral scans while maintaining image contrast, FOV and resolution, and readout time. A full gradient trajectory and a derated gradient (undersampled) trajectory were chosen for a volunteer scan followed by parallel imaging‐aided reconstruction to illustrate comparable image SNR. Two auto‐derating methods, which prioritize slew rate and gradient amplitude, respectively, were derived using analytical results from the WHIRLED PEAS variant of spiral waveforms and compared in their acoustic noise level under test use cases.ResultsDerating the gradients made the scan quieter by 16.6 dB(A) on average than a full gradient trajectory and required an undersampling factor R = 2 in order to maintain scan time, with no appreciable penalty in image SNR. Prioritizing reduced slew rate resulted in maximal loudness reduction.ConclusionScanner gradients can often be derated to reduce the acoustic noise for spiral MRI with minimal penalty in scan time and image quality with the help of parallel imaging. An automatic slew‐priority derating method that maximizes loudness reduction is given.

PurposeAn accurate field map is essential to separate fat and water signals in a dual‐echo chemical shift encoded spiral MRI scan. A rapid low‐resolution B0 map prescan is usually performed before each exam. Occasional inaccuracy in these field map estimates can lead to misclassification of the water and fat signals as well as blurring artifacts in the reconstruction. The present work proposes a self‐consistent model to evaluate residual field offsets according to the image data to improve the reconstruction quality and facilitate the scan efficiency.Theory and MethodsThe proposed method compares the phase differences of the two‐echo data after correcting for fat frequency offsets. A more accurate field map is approximated according to the phase discrepancies and improved image quality. Experiments were conducted with simulated off‐resonance on a numerical phantom, five volunteer head scans, and four volunteer abdominal scans for validation.ResultsThe initial reconstruction of the demonstrated examples exhibit blurring artifacts and misregistration of fat and water because of the inaccuracy of the field map. The proposed method updates the field map to amend the fat and water estimation and improve image quality.ConclusionsThis work presents a model to improve the quality of fat‐water imaging of the spiral MRI by estimating a better field map from the acquired data. It allows reducing the field map pre‐scans before each spiral scan under normal circumstances to increase scan efficiency.

PurposeIn quantitative susceptibility mapping (QSM) using magnetic resonance imaging, image reconstruction methods usually aim at suppressing streaking artifacts. In this study, a streaking detection method is proposed for evaluating and optimizing quantitative susceptibility maps.MethodsNine healthy subjects participated in this study and underwent three‐dimensional multi‐echo gradient echo scans. Regularized iterative algorithm was used for reconstruction of tissue susceptibility maps in all subjects. Streaking detection was applied to evaluate streaking artifact in tissue susceptibility maps. In addition, an optimization process for QSM reconstruction by streaking detection was applied and was compared with matching noise level method.ResultsIt is shown that the proposed streaking detection technique effectively delineates streaking artifact in tissue susceptibility maps. In QSM reconstruction, optimization by streaking detection successfully determines the regularization factor that balances between streaking artifact suppression and tissue texture preservation. ROI analyses of brain tissue susceptibility show that optimization by streaking detection achieves results in good agreement with that from matching noise level method.ConclusionsStreaking detection enables direct visualization of streaking patterns in tissue susceptibility maps. It can be applied both for evaluating QSM reconstruction quality and for comparing different reconstruction algorithms. Furthermore, streaking detection can be incorporated into an optimization process of QSM reconstruction. Therefore, we conclude that the proposed method will add value to reconstruction of QSM.