Multi-sensor Learning Enables Information Transfer across Different Sensory Data and Augments Multi-modality Imaging.

Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images under the guidance of mutual information or spatially registered hardware, which limits the accuracy and utility of multi-modality imaging. Here, we investigate a data-driven multi-modality imaging (DMI) strategy for synergetic imaging of CT and MRI.

Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy.

Background And Purpose: Intravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT).

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care.

A novel process for efficient utilization of bamboo fiber resource in dissolving pulp production by fiber fractionation: Laboratory study and mill trials.

The rational use of bamboo to make dissolving pulp can offer up new opportunities for cellulose production, alleviating wood scarcity. Bamboo contains a high content of non-fiber cells, which presents technical challenges in dissolving pulp production by the conventional process. In this study, a process concept of separating hemicelluloses is presented by fiber fractionation and purification for cleaner production of bamboo dissolving pulp: bamboo kraft pulp was fractionated into long-fiber and short-fiber fractions.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

Background: Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)-guided radiation treatment precision. Data-driven sparse MRI reconstruction, however, requires large-scale training datasets for prior learning, which is time-consuming and challenging to acquire in clinical settings.

Purpose: To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR-guided radiotherapy.

Adaptive Region-Specific Loss for Improved Medical Image Segmentation.

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e.

Modeling linear accelerator (Linac) beam data by implicit neural representation learning for commissioning and quality assurance applications.

Background: Linear accelerator (Linac) beam data commissioning and quality assurance (QA) play a vital role in accurate radiation treatment delivery and entail a large number of measurements using a variety of field sizes. How to optimize the effort in data acquisition while maintaining high quality of medical physics practice has been sought after.

Purpose: We propose to model Linac beam data through implicit neural representation (NeRP) learning.

Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.

Purpose: To develop a geometry-informed deep learning framework for volumetric MRI with sub-second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time.

Methods: A 2D-3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k-space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space.

Volumetric prediction of breathing and slow drifting motion in the abdomen using radial MRI and multi-temporal resolution modeling.

Abdominal organ motions introduce geometric uncertainties to radiotherapy. This study investigates a multi-temporal resolution 3D motion prediction scheme that accounts for both breathing and slow drifting motion in the abdomen in support of MRI-guided radiotherapy. Ten-minute MRI scans were acquired for 8 patients using a volumetric golden-angle stack-of-stars sequence.

Modeling intra-fractional abdominal configuration changes using breathing motion-corrected radial MRI.

Abdominal organ motions introduce geometric uncertainties to gastrointestinal radiotherapy. This study investigated slow drifting motion induced by changes of internal anatomic organ arrangements using a 3D radial MRI sequence with a scan length of 20 min. Breathing motion and cyclic GI motion were first removed through multi-temporal resolution image reconstruction.

Abdominal synthetic CT generation from MR Dixon images using a U-net trained with 'semi-synthetic' CT data.

Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status.

Female pelvic synthetic CT generation based on joint intensity and shape analysis.

Using MRI for radiotherapy treatment planning and image guidance is appealing as it provides superior soft tissue information over CT scans and avoids possible systematic errors introduced by aligning MR to CT images. This study presents a method that generates Synthetic CT (MRCT) volumes by performing probabilistic tissue classification of voxels from MRI data using a single imaging sequence (T1 Dixon). The intensity overlap between different tissues on MR images, a major challenge for voxel-based MRCT generation methods, is addressed by adding bone shape information to an intensity-based classification scheme.

Synthetic CT for MRI-based liver stereotactic body radiotherapy treatment planning.

A technique for generating MRI-derived synthetic CT volumes (MRCTs) is demonstrated in support of adaptive liver stereotactic body radiation therapy (SBRT). Under IRB approval, 16 subjects with hepatocellular carcinoma were scanned using a single MR pulse sequence (T1 Dixon). Air-containing voxels were identified by intensity thresholding on T1-weighted, water and fat images.

A female pelvic bone shape model for air/bone separation in support of synthetic CT generation for radiation therapy.

Separating bone from air in MR data is one of the major challenges in using MR images to derive synthetic CT. The problem is further complicated when the anatomic regions filled with air are altered across scans due to air mobility, for instance, in pelvic regions, thereby the air regions estimated using an ultrashort echo time (UTE) sequence are invalid in other image series acquired for multispectral classification. This study aims to develop and investigate a female pelvic bone shape model to identify low intensity regions in MRI where air is unlikely to be present in support of synthetic CT generation without UTE imaging.

Preparation of a novel inorganic polymer coagulant from oil shale ash.

In this paper, a novel inorganic polymer coagulant was prepared from oil shale ash, and was adopted to treat municipal sewage. Effect of coagulants dosage on the turbidity and Chemical Oxygen Demand (COD) removal were examined. In addition, the structure and morphology of the prepared coagulants were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and infra-red spectra (FTIR), furthermore, the zeta potential of the sewage and the microscopic images of flocks were measured.