Publications

2021

Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O. White Matter Changes in Psychosis Risk Relate to Development and Are Not Impacted by the Transition to Psychosis. Mol Psychiatry. 2021;26(11):6833–44.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of white matter abnormalities in a large, longitudinal cohort of CHR individuals comprising the North American Prodrome Longitudinal Study (NAPLS-3). Multi-shell diffusion magnetic resonance imaging data were collected across multiple timepoints (1-5 over 1 year) in 286 subjects (aged 12-32 years): 25 CHR individuals who transitioned to psychosis (CHR-P; 61 scans), 205 CHR subjects with unknown transition outcome after the 1-year follow-up period (CHR-U; 596 scans), and 56 healthy controls (195 scans). Linear mixed effects models were fitted to infer the impact of age and illness-onset on variation in the fractional anisotropy of cellular tissue (FAT) and the volume fraction of extracellular free water (FW). Baseline measures of white matter microstructure did not differentiate between HC, CHR-U and CHR-P individuals. However, age trajectories differed between the three groups in line with a developmental effect: CHR-P and CHR-U groups displayed higher FAT in adolescence, and 4% lower FAT by 30 years of age compared to controls. Furthermore, older CHR-P subjects (20+ years) displayed 4% higher FW in the forceps major (p < 0.05). Prospective analysis in CHR-P did not reveal a significant impact of illness onset on regional FAT or FW, suggesting that transition to psychosis is not marked by dramatic change in white matter microstructure. Instead, clinical high risk for psychosis-regardless of transition outcome-is characterized by subtle age-related white matter changes that occur in tandem with development.

Slator PJ, Palombo M, Miller KL, Westin CF, Laun F, Kim D, Haldar JP, Benjamini D, Lemberskiy G, Martins JP de A, Hutter J. Combined Diffusion-Relaxometry Microstructure Imaging: Current Status and Future Prospects. Magn Reson Med. 2021;86(6):2987–3011.

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 * . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Behjat H, Westin CF, Aganj I. Cortical Surface-Informed Volumetric Spatial Smoothing of fMRI Data via Graph Signal Processing. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:3804–8.

Conventionally, as a preprocessing step, functional MRI (fMRI) data are spatially smoothed before further analysis, be it for activation mapping on task-based fMRI or functional connectivity analysis on resting-state fMRI data. When images are smoothed volumetrically, however, isotropic Gaussian kernels are generally used, which do not adapt to the underlying brain structure. Alternatively, cortical surface smoothing procedures provide the benefit of adapting the smoothing process to the underlying morphology, but require projecting volumetric data on to the surface. In this paper, leveraging principles from graph signal processing, we propose a volumetric spatial smoothing method that takes advantage of the gray-white and pial cortical surfaces, and as such, adapts the filtering process to the underlying morphological details at each point in the cortex.

Szczepankiewicz F, Sjölund J, Armellina ED, Plein S, Schneider JE, Teh I, Westin CF. Motion-Compensated Gradient Waveforms for Tensor-Valued Diffusion Encoding by Constrained Numerical Optimization. Magn Reson Med. 2021;85(4):2117–26.

PURPOSE: Diffusion-weighted MRI is sensitive to incoherent tissue motion, which may confound the measured signal and subsequent analysis. We propose a "motion-compensated" gradient waveform design for tensor-valued diffusion encoding that negates the effects bulk motion and incoherent motion in the ballistic regime. METHODS: Motion compensation was achieved by constraining the magnitude of gradient waveform moment vectors. The constraint was incorporated into a numerical optimization framework, along with existing constraints that account for b-tensor shape, hardware restrictions, and concomitant field gradients. We evaluated the efficacy of encoding and motion compensation in simulations, and we demonstrated the approach by linear and planar b-tensor encoding in a healthy heart in vivo. RESULTS: The optimization framework produced asymmetric motion-compensated waveforms that yielded b-tensors of arbitrary shape with improved efficiency compared with previous designs for tensor-valued encoding, and equivalent efficiency to previous designs for linear (conventional) encoding. Technical feasibility was demonstrated in the heart in vivo, showing vastly improved data quality when using motion compensation. The optimization framework is available online in open source. CONCLUSION: Our gradient waveform design is both more flexible and efficient than previous methods, facilitating tensor-valued diffusion encoding in tissues in which motion would otherwise confound the signal. The proposed design exploits asymmetric encoding times, a single refocusing pulse or multiple refocusing pulses, and integrates compensation for concomitant gradient effects throughout the imaging volume.

Lampinen B, Lätt J, Wasselius J, van Westen D, Nilsson M. Time Dependence in Diffusion MRI Predicts Tissue Outcome in Ischemic Stroke Patients. Magn Reson Med. 2021;86(2):754–64.

PURPOSE: Reperfusion therapy enables effective treatment of ischemic stroke presenting within 4-6 hours. However, tissue progression from ischemia to infarction is variable, and some patients benefit from treatment up until 24 hours. Improved imaging techniques are needed to identify these patients. Here, it was hypothesized that time dependence in diffusion MRI may predict tissue outcome in ischemic stroke. METHODS: Diffusion MRI data were acquired with multiple diffusion times in five non-reperfused patients at 2, 9, and 100 days after stroke onset. Maps of "rate of kurtosis change" (k), mean kurtosis, ADC, and fractional anisotropy were derived. The ADC maps defined lesions, normal-appearing tissue, and the lesion tissue that would either be infarcted or remain viable by day 100. Diffusion parameters were compared (1) between lesions and normal-appearing tissue, and (2) between lesion tissue that would be infarcted or remain viable. RESULTS: Positive values of k were observed within stroke lesions on day 2 (P = .001) and on day 9 (P = .023), indicating diffusional exchange. On day 100, high ADC values indicated infarction of 50 ± 20% of the lesion volumes. Tissue infarction was predicted by high k values both on day 2 (P = .026) and on day 9 (P = .046), by low mean kurtosis values on day 2 (P = .043), and by low fractional anisotropy values on day 9 (P = .029), but not by low ADC values. CONCLUSIONS: Diffusion time dependence predicted tissue outcome in ischemic stroke more accurately than the ADC, and may be useful for predicting reperfusion benefit.

Sedghi A, Donnell LJO, Kapur T, Learned-Miller E, Mousavi P, Wells WM. Image Registration: Maximum Likelihood, Minimum Entropy and Deep Learning. Med Image Anal. 2021;69:101939.

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.

Ning L, Szczepankiewicz F, Nilsson M, Rathi Y, Westin CF. Probing Tissue Microstructure by Diffusion Skewness Tensor Imaging. Sci Rep. 2021;11(1):135.

Probing the cellular structure of in vivo biological tissue is a fundamental problem in biomedical imaging and medical science. This work introduces an approach for analyzing diffusion magnetic resonance imaging data acquired by the novel tensor-valued encoding technique for characterizing tissue microstructure. Our approach first uses a signal model to estimate the variance and skewness of the distribution of apparent diffusion tensors modeling the underlying tissue. Then several novel imaging indices, such as weighted microscopic anisotropy and microscopic skewness, are derived to characterize different ensembles of diffusion processes that are indistinguishable by existing techniques. The contributions of this work also include a theoretical proof that shows that, to estimate the skewness of a diffusion tensor distribution, the encoding protocol needs to include full-rank tensor diffusion encoding. This proof provides a guideline for the application of this technique. The properties of the proposed indices are illustrated using both synthetic data and in vivo data acquired from a human brain.

Beers A, Brown J, Chang K, Hoebel K, Patel J, Ly I, Tolaney SM, Brastianos P, Rosen B, Gerstner ER, Kalpathy-Cramer J. DeepNeuro: An Open-Source Deep Learning Toolbox for Neuroimaging. Neuroinformatics. 2021;19(1):127–40.
Translating deep learning research from theory into clinical practice has unique challenges, specifically in the field of neuroimaging. In this paper, we present DeepNeuro, a Python-based deep learning framework that puts deep neural networks for neuroimaging into practical usage with a minimum of friction during implementation. We show how this framework can be used to design deep learning pipelines that can load and preprocess data, design and train various neural network architectures, and evaluate and visualize the results of trained networks on evaluation data. We present a way of reproducibly packaging data pre- and postprocessing functions common in the neuroimaging community, which facilitates consistent performance of networks across variable users, institutions, and scanners. We show how deep learning pipelines created with DeepNeuro can be concisely packaged into shareable Docker and Singularity containers with user-friendly command-line interfaces.
Nitsch J, Sack J, Halle MW, Moltz JH, Wall A, Rutherford AE, Kikinis R, Meine H. MRI-Based Radiomic Feature Analysis of End-Stage Liver Disease for Severity Stratification. Int J Comput Assist Radiol Surg. 2021;16(3):457–66.
PURPOSE: We aimed to develop a predictive model of disease severity for cirrhosis using MRI-derived radiomic features of the liver and spleen and compared it to the existing disease severity metrics of MELD score and clinical decompensation. The MELD score is compiled solely by blood parameters, and so far, it was not investigated if extracted image-based features have the potential to reflect severity to potentially complement the calculated score. METHODS: This was a retrospective study of eligible patients with cirrhosis ([Formula: see text]) who underwent a contrast-enhanced MR screening protocol for hepatocellular carcinoma (HCC) screening at a tertiary academic center from 2015 to 2018. Radiomic feature analyses were used to train four prediction models for assessing the patient’s condition at time of scan: MELD score, MELD score [Formula: see text] 9 (median score of the cohort), MELD score [Formula: see text] 15 (the inflection between the risk and benefit of transplant), and clinical decompensation. Liver and spleen segmentations were used for feature extraction, followed by cross-validated random forest classification. RESULTS: Radiomic features of the liver and spleen were most predictive of clinical decompensation (AUC 0.84), which the MELD score could predict with an AUC of 0.78. Using liver or spleen features alone had slightly lower discrimination ability (AUC of 0.82 for liver and AUC of 0.78 for spleen features only), although this was not statistically significant on our cohort. When radiomic prediction models were trained to predict continuous MELD scores, there was poor correlation. When stratifying risk by splitting our cohort at the median MELD 9 or at MELD 15, our models achieved AUCs of 0.78 or 0.66, respectively. CONCLUSIONS: We demonstrated that MRI-based radiomic features of the liver and spleen have the potential to predict the severity of liver cirrhosis, using decompensation or MELD status as imperfect surrogate measures for disease severity.
Wu C, Diao B, Ungi T, Sedghi A, Kikinis R, Mousavi P, Fichtinger G. Development of an Open-Source System for Prostate Biopsy Training in Senegal. SPIE Medical Imaging. 2021;.
PURPOSE: Prostate cancer is the second most common cancer diagnosed in men. The rate is disproportionately high among men in sub-Saharan Africa where, unlike in North America and Western Europe, the screening process for prostate cancer has historically not been routine. Currently, as awareness regarding prostate health increases, more patients in this region are being referred to trans-rectal ultrasound guided prostate biopsy, a diagnosis procedure which requires a strong understanding of prostate zonal anatomy. To aid in the instruction of this procedure, prostate biopsy training programs need to be implemented. Unfortunately, current TRUS-guided training tools are not ideal for reproducibility in these Western African countries. To answer this challenge, we are developing an affordable and open-source training simulator for TRUS-guided prostate biopsy, for use in Senegal.  In this paper, we present the implementation of the training simulator’s virtual interface, highlighting the generation and evaluation of the critical training component of zonal anatomy overlaid on TRUS. METHODS: For the simulator’s dataset, we registered TRUS and MRI volumes together to obtain the zonal segmentation from the MRI volumes. After generating ten pairings of TRUS overlaid with zonal segmentation, we designed and implemented a virtual TRUS training system, developed in open-source software. The objective of our simulator is to teach trainees to accurately identify the prostate’s anatomical zones in TRUS. To confirm the system’s usability for training zonal identification, we conducted a two-part survey on the quality of the zonal overlays with 7 urology experts. In the first part, they assessed the zonal overlay for visual correctness by rating 10 images from one patient’s TRUS with registered overlay on a 5-point Likert scale. For the second part, they labelled 10 plain TRUS volumes with zonal anatomy and the labels were compared to the labels of our overlay. RESULTS: On average, experts rated the zonal overlay’s visual accuracy at 4 out of 5. Furthermore, 7 out of 7 experts labelled the peripheral, anterior, and transitional zones in the same regions we overlaid them, and 5 out of 7 labelled the central zone in the same region we overlaid it. CONCLUSION: We created the prototype of a TRUS imaging simulator in open-source software. A vital training component, zonal overlay, was generated using publicly accessible data and validated by expert urologists for prostate zone identification, confirming the concept.