Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

Purpose To combine deep learning and biology-based modeling to predict the response of locally advanced, triple-negative breast cancer before initiating neoadjuvant chemotherapy (NAC). Materials and Methods In this retrospective study, a biology-based mathematical model of tumor response to NAC was constructed and calibrated on a patient-specific basis using imaging data from patients enrolled in the MD Anderson A Robust TNBC Evaluation FraMework to Improve Survival trial (ARTEMIS; ClinicalTrials.gov registration no.

Accelerated motion correction with deep generative diffusion models.

Purpose: The aim of this work is to develop a method to solve the ill-posed inverse problem of accelerated image reconstruction while correcting forward model imperfections in the context of subject motion during MRI examinations.

Methods: The proposed solution uses a Bayesian framework based on deep generative diffusion models to jointly estimate a motion-free image and rigid motion estimates from subsampled and motion-corrupt two-dimensional (2D) k-space data.

Results: We demonstrate the ability to reconstruct motion-free images from accelerated two-dimensional (2D) Cartesian and non-Cartesian scans without any external reference signal.

Correcting synthetic MRI contrast-weighted images using deep learning.

Synthetic magnetic resonance imaging (MRI) offers a scanning paradigm where a fast multi-contrast sequence can be used to estimate underlying quantitative tissue parameter maps, which are then used to synthesize any desirable clinical contrast by retrospectively changing scan parameters in silico. Two benefits of this approach are the reduced exam time and the ability to generate arbitrary contrasts offline. However, synthetically generated contrasts are known to deviate from the contrast of experimental scans.

High-fidelity direct contrast synthesis from magnetic resonance fingerprinting.

Purpose: This work was aimed at proposing a supervised learning-based method that directly synthesizes contrast-weighted images from the Magnetic Resonance Fingerprinting (MRF) data without performing quantitative mapping and spin-dynamics simulations.

Methods: To implement our direct contrast synthesis (DCS) method, we deploy a conditional generative adversarial network (GAN) framework with a multi-branch U-Net as the generator and a multilayer CNN (PatchGAN) as the discriminator. We refer to our proposed approach as N-DCSNet.

Federated End-to-End Unrolled Models for Magnetic Resonance Image Reconstruction.

Image reconstruction is the process of recovering an image from raw, under-sampled signal measurements, and is a critical step in diagnostic medical imaging, such as magnetic resonance imaging (MRI). Recently, data-driven methods have led to improved image quality in MRI reconstruction using a limited number of measurements, but these methods typically rely on the existence of a large, centralized database of fully sampled scans for training. In this work, we investigate federated learning for MRI reconstruction using end-to-end unrolled deep learning models as a means of training global models across multiple clients (data sites), while keeping individual scans local.

An untrained deep learning method for reconstructing dynamic MR images from accelerated model-based data.

Purpose: To implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.

Methods: The ConvDecoder (CD) neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle data. Fully-sampled variable-flip angle k-space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low-rank (LR) reconstruction, and compressed sensing with L1-wavelet regularization (L1).

High fidelity deep learning-based MRI reconstruction with instance-wise discriminative feature matching loss.

Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning- (DL) based reconstructions.

Methods: A novel patch-based Unsupervised Feature Loss (UFLoss) is proposed and incorporated into the training of DL-based reconstruction frameworks in order to preserve perceptual similarity and high-order statistics. The UFLoss provides instance-level discrimination by mapping similar instances to similar low-dimensional feature vectors and is trained without any human annotation.

Implicit data crimes: Machine learning bias arising from misuse of public data.

SignificancePublic databases are an important resource for machine learning research, but their growing availability sometimes leads to "off-label" usage, where data published for one task are used for another. This work reveals that such off-label usage could lead to biased, overly optimistic results of machine-learning algorithms. The underlying cause is that public data are processed with hidden processing pipelines that alter the data features.

Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization.

Accelerated multi-coil magnetic resonance imaging reconstruction has seen a substantial recent improvement combining compressed sensing with deep learning. However, most of these methods rely on estimates of the coil sensitivity profiles, or on calibration data for estimating model parameters. Prior work has shown that these methods degrade in performance when the quality of these estimators are poor or when the scan parameters differ from the training conditions.

A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI.

Purpose: With rising safety concerns over the use of gadolinium-based contrast agents (GBCAs) in contrast-enhanced MRI, there is a need for dose reduction while maintaining diagnostic capability. This work proposes comprehensive technical solutions for a deep learning (DL) model that predicts contrast-enhanced images of the brain with approximately 10% of the standard dose, across different sites and scanners.

Methods: The proposed DL model consists of a set of methods that improve the model robustness and generalizability.

Quantitative anatomy mimicking slice phantoms.

Purpose: To present a reproducible methodology for building an anatomy mimicking phantom with targeted T and T contrast for use in quantitative magnetic resonance imaging.

Methods: We propose a reproducible method for creating high-resolution, quantitative slice phantoms. The phantoms are created using gels with different concentrations of NiCl and MnCl to achieve targeted T and T values.

Deep learning-based methods may minimize GBCA dosage in brain MRI.

Objectives: To evaluate the clinical performance of a deep learning (DL)-based method for brain MRI exams with reduced gadolinium-based contrast agent (GBCA) dose to provide better understanding of the readiness and limitations of this method.

Methods: Eighty-three consecutive patients (from March 2019 to August 2019) who underwent brain contrast-enhanced (CE) MRI were included. Three 3D T1-weighted images with zero-dose, low-dose (10%), and full-dose (100%) GBCA were collected.

Targeted rapid knee MRI exam using T shuffling.

Background: MRI is commonly used to evaluate pediatric musculoskeletal pathologies, but same-day/near-term scheduling and short exams remain challenges.

Purpose: To investigate the feasibility of a targeted rapid pediatric knee MRI exam, with the goal of reducing cost and enabling same-day MRI access.

Study Type: A cost effectiveness study done prospectively.

Variable-Density Single-Shot Fast Spin-Echo MRI with Deep Learning Reconstruction by Using Variational Networks.

Purpose To develop a deep learning reconstruction approach to improve the reconstruction speed and quality of highly undersampled variable-density single-shot fast spin-echo imaging by using a variational network (VN), and to clinically evaluate the feasibility of this approach. Materials and Methods Imaging was performed with a 3.0-T imager with a coronal variable-density single-shot fast spin-echo sequence at 3.

Self-Calibrating Wave-Encoded Variable-Density Single-Shot Fast Spin Echo Imaging.

Background: It is highly desirable in clinical abdominal MR scans to accelerate single-shot fast spin echo (SSFSE) imaging and reduce blurring due to T decay and partial-Fourier acquisition.

Purpose: To develop and investigate the clinical feasibility of wave-encoded variable-density SSFSE imaging for improved image quality and scan time reduction.

Study Type: Prospective controlled clinical trial.

Fast comprehensive single-sequence four-dimensional pediatric knee MRI with T shuffling.

Purpose: To develop and clinically evaluate a pediatric knee magnetic resonance imaging (MRI) technique based on volumetric fast spin-echo (3DFSE) and compare its diagnostic performance, image quality, and imaging time to that of a conventional 2D protocol.

Materials And Methods: A 3DFSE sequence was modified and combined with a compressed sensing-based reconstruction resolving multiple image contrasts, a technique termed T Shuffling (T Sh). With Institutional Review Board (IRB) approval, 28 consecutive children referred for 3T knee MRI prospectively underwent a standard clinical knee protocol followed by T Sh.

T shuffling: Sharp, multicontrast, volumetric fast spin-echo imaging.

Purpose: A new acquisition and reconstruction method called T Shuffling is presented for volumetric fast spin-echo (three-dimensional [3D] FSE) imaging. T Shuffling reduces blurring and recovers many images at multiple T contrasts from a single acquisition at clinically feasible scan times (6-7 min).

Theory And Methods: The parallel imaging forward model is modified to account for temporal signal relaxation during the echo train.

Comprehensive motion-compensated highly accelerated 4D flow MRI with ferumoxytol enhancement for pediatric congenital heart disease.

Purpose: To develop and evaluate motion-compensation and compressed-sensing techniques in 4D flow MRI for anatomical assessment in a comprehensive ferumoxytol-enhanced congenital heart disease (CHD) exam.

Materials And Methods: A Cartesian 4D flow sequence was developed to enable intrinsic navigation and two variable-density sampling schemes: VDPoisson and VDRad. Four compressed-sensing methods were developed: A) VDPoisson scan reconstructed using spatial wavelets; B) added temporal total variation to A; C) VDRad scan using the same reconstruction as in B; and D) added motion compensation to C.

Resection Arthroplasty for Resistant Ulcers Underlying the Hallux in Insensate Diabetics.

Background: Foot ulcers carry considerable morbidity in patients with peripheral neuropathy and frequently lead to foot amputation. The purpose of this study was to present our experience treating recalcitrant ulcers underlying the hallux interphalangeal joint in patients with diabetes mellitus (DM)-related neuropathy with a first metatarsophalangeal (MTPJ1) resection arthroplasty.

Methods: We retrospectively reviewed the computerized medical files of patients with diabetic neuropathy treated with a MTPJ1 resection arthroplasty.

Hard to heal pressure ulcers (stage III-IV): efficacy of injected activated macrophage suspension (AMS) as compared with standard of care (SOC) treatment controlled trial.

The objective of this study was to compare local injections of AMS with SOC treatments for stage III and IV pressure ulcers in elderly patients. It was designed as historically prospective 2-arms non-parallel open controlled trial, and conducted in a department of geriatric medicine and rehabilitation of a university affiliated tertiary hospital. We studied 100 consecutive elderly patients with a total of 216 stage III or IV pressure ulcers, 66 patients were assigned to the AMS group and had their wounds injected, while 38 patients were assigned to the SOC group.

A comparison between CO2 laser surgery with and without lateral fold vaporization for ingrowing toenails.

Background: An ingrowing toenail is an excessive lateral nail growth into the nail fold. It acts as a foreign body and exerts a local pressure sore-like effect, which may result in inflammation and granulation. Several treatment modalities exist, including chemical ablation and different surgical procedures.

Galactorrhea complicating wound healing following reduction mammaplasty.

Galactorrhea complicating wound healing following reduction mammaplasty occurs rarely; only isolated cases have been reported in recent years. We report the case of a 25-year-old woman who presented with delayed healing and dehiscence of surgical wounds 3 weeks following vertical scar reduction mammaplasty. During surgical debridement, spontaneous discharge of milk in the wound was noted.