No abstract

Purpose The diffusion‐weighted SPLICE (split acquisition of fast spin‐echo signals) sequence employs split‐echo rapid acquisition with relaxation enhancement (RARE) readout to provide images almost free of geometric distortions. However, due to the varying T2$$ {}_2 $$‐weighting during k‐space traversal, SPLICE suffers from blurring. This work extends a method for controlling the spatial point spread function (PSF) while optimizing the signal‐to‐noise ratio (SNR) achieved by adjusting the flip angles in the refocusing pulse train of SPLICE. Methods An algorithm based on extended phase graph (EPG) simulations optimizes the flip angles by maximizing SNR for a flexibly chosen predefined target PSF that describes the desired k‐space density weighting and spatial resolution. An optimized flip angle scheme and a corresponding post‐processing correction filter which together achieve the target PSF was tested by healthy subject brain imaging using a clinical 1.5 T scanner. Results Brain images showed a clear and consistent improvement over those obtained with a standard constant flip angle scheme. SNR was increased and apparent diffusion coefficient estimates were more accurate. For a modified Hann k‐space weighting example, considerable benefits resulted from acquisition weighting by flip angle control. Conclusion The presented flexible method for optimizing SPLICE flip angle schemes offers improved MR image quality of geometrically accurate diffusion‐weighted images that makes the sequence a strong candidate for radiotherapy planning or stereotactic surgery.

Objective: In the field of radiation oncology, the benefit of MRI goes beyond that of providing high soft-tissue contrast images for staging and treatment planning. With the recent clinical introduction of hybrid MRI linear accelerators (MR-Linacs) it has become feasible to map physiological parameters describing diffusion, perfusion, and relaxation during the entire course of radiotherapy, for example. However, advanced data analysis tools are required for extracting qualified prognostic and predictive imaging biomarkers from longitudinal MRI data. In this study, we propose a new prediction framework tailored to exploit temporal dynamics of tissue features from repeated measurements. We demonstrate the framework using a newly developed decomposition method for tumor characterization. Approach: Two previously published MRI datasets with multiple measurements during and after radiotherapy, were used for development and testing: T$_2$-weighted multi-echo images obtained for two mouse models of pancreatic cancer, and diffusion-weighted images for patients with brain metastases. Initially, the data was decomposed using the novel monotonous slope non-negative matrix factorization (msNMF) tailored for MR data. The following processing consisted of a tumor heterogeneity assessment using descriptive statistical measures, robust linear modelling to capture temporal changes of these, and finally logistic regression analysis for stratification of tumors and volumetric outcome. Main Results: The framework was able to classify the two pancreatic tumor types with an \textit{area under curve} AUC of 0.999, $P<0.001$ and predict the tumor volume change with a correlation coefficient of 0.513, $P=0.034$. A classification of the human brain metastases into responders and non-responders resulted in an AUC of 0.74, $P=0.065$. Significance: A general data processing framework for analyses of longitudinal MRI data has been developed and applications were demonstrated by classification of tumor type and prediction of radiotherapy response. Further, as part of the assessment, the merits of msNMF for tumor tissue decomposition were demonstrated.