In MR imaging, techniques for acquisition of reduced data (Rapid MR imaging) are being explored to obtain high-quality images to satisfy the conflicting requirements of simultaneous high spatial and temporal resolution, required for functional studies. The term “rapid” is used because reduction in the volume of data acquisition leads to faster scans. The objective is to obtain high acceleration factors, since it indicates the ability of the technique to yield high-quality images with reduced data (in turn, reduced acquisition time). Reduced data acquisition in conventional (sequential) MR scanners, where a single receiver coil is used, can be achieved either by acquiring only certain k-space regions or by regularly undersampling the entire data in k-space. In parallel MR scanners, where multiple receiver coils are used to acquire high-SNR data, reduced data acquisition is typically accomplished using regular undersampling. Optimal region selection in the 3D k-space (restricted to ky - kz plane, since kx is the readout direction) needs to satisfy “maximum energy compaction” and “minimum acquisition” requirements. In this thesis, a novel star-shaped truncation window is proposed to increase the achievable acceleration factor. The proposed window monotonically cuts down the acquisition of the number of k-space samples with lesser energy. The truncation window samples data within a star-shaped region centered around the origin in the ky - kz plane. The missing values are extrapolated using generalized series modeling-based methods. The proposed method is applied to several real and synthetic data sets. The superior performance of the proposed method is illustrated using the standard measures of error images and uptake curve comparisons. Average values of slope error in estimating the enhancement curve are obtained over 5 real data sets of breast and abdomen images, for an acceleration factor of 8. The proposed method results in a slope error of 5%, while the values obtained using rectangular and elliptical windows are 12% and 10%, respectively. k-t BLAST, a popular method used in cardiac and functional brain imaging, involves regular undersampling. However, the method suffers from drawbacks such as separate training scan, blurred training estimates and aliased phase maps. In this thesis, variations to k-t BLAST have been proposed to overcome the drawbacks. The proposed improved k-t BLAST incorporates variable-density sampling scheme, phase information from the training map and utilization of generalized-series extrapolated training map. The advantage of using a variable density sampling scheme is that the training map is obtained from the actual acquisition instead of a separate pilot scan. Besides, phase information from the training map is used, in place of phase from the aliased map; generalized series extrapolated training map is used instead of the zero-padded training map, leading to better estimation of the unacquired values. The existing technique and the proposed variations are applied on real fMRI data volumes. Improvement in PSNR of activation maps of up to 10 dB. Besides, a reduction of 10% in RMSE is obtained over the entire time series of fMRI images. The peak improvement of the proposed method over k-t BLAST is 35%, averaged over 5 data sets. Most image reconstruction techniques in parallel MR imaging utilize the knowledge of coil sensitivities for image reconstruction, along with assumptions of image reconstruction functions. The thesis proposes an image reconstruction technique that neither needs to estimate coil sensitivities nor makes any assumptions on the image reconstruction function. The proposed cartesian parallel imaging using neural networks, called “Composite image Reconstruction And Unaliasing using Neural Networks” (CRAUNN), is a novel approach based on the observation that the aliasing patterns remain the same irrespective of whether the k-space acquisition consists of only low frequencies or the entire range of k-space frequencies. In the proposed approach, image reconstruction is obtained using the neural network framework. Data acquisition follows a variable-density sampling scheme, where low k-space frequencies are densely sampled, while the rest of the k-space is sparsely sampled. The blurred, unaliased images obtained using the densely sampled low k-space data are used to train the neural network. Image is reconstructed by feeding to the trained network, the aliased images, obtained using the regularly undersampled k-space containing the entire range of k-space frequencies. The proposed approach has been applied to the Shepp-Logan phantom as well as real brain MRI data sets. A visual error measure for estimating the image quality used in compression literature, called SSIM (Structural SIMilarity) index is employed. The average SSIM for the noisy Shepp-Logan phantom (SNR = 10 dB) using the proposed method is 0.68, while those obtained using GRAPPA and SENSE are 0.6 and 0.42, respectively. For the case of the phantom superimposed with fine grid-like structure, the average SSIM index obtained with the proposed method is 0.7, while those for GRAPPA and SENSE are 0.5 and 0.37, respectively. Image reconstruction is more challenging with reduced data acquired using non-cartesian trajectories since aliasing introduced is not localized. Popular technique for non-cartesian parallel imaging CGSENSE suffers from drawbacks like sensitivity to noise and requirement of good coil estimates, while radial/spiral GRAPPA requires complete identical scans to obtain reconstruction kernels for specific trajectories. In our work, the proposed neural network based reconstruction method, CRAUNN, has been shown to work for general non-cartesian acquisitions such as spiral and radial too. In addition, the proposed method does not require coil estimates, or trajectory-specific customized reconstruction kernels. Experiments are performed using radial and spiral trajectories on real and synthetic data, and compared with CGSENSE. Comparison of error images shows that the proposed method has far lesser residual aliasing compared to CGSENSE. The average SSIM index for reconstructions using CRAUNN with spirally and radially undersampled data, are comparable at 0.83 and 0.87, respectively. The same measure for reconstructions using CGSENSE are 0.67 and 0.69, respectively. The average RMSE for reconstructions using CRAUNN with spirally and radially undersampled data, are comparable at 11.1 and 6.1, respectively. The same measure for reconstructions using CGSENSE are 16 and 9.18, respectively.

Virtual Reality has once again emerged as a platform with great potential for exploratory research. An expectation for the next generation virtual reality platforms is to be used as a tool for graphical designers as a new way to access the virtual world and interact with digital content. Just as mobile applications are developed for smaller screens with touch capabilities and desktop applications for computer screens with the input of mouse and keyboard, the interfaces of VR applications need to be designed with the capabilities and limitations of the platform in mind. A common component in modern graphic editing software is layer management: having the final output of the application divided into sub-components. This thesis explores how layer management can best be implemented in room-scale Virtual Reality with a focus on selection, navigation and manipulation through an iterative design study. The study concludes that, to improve the learnability of a system, interactions should be based on real-world interaction for naturalistic tasks while drawing inspiration from desktop applications for more abstract tasks. Furthermore, the environment needs to be adjusted to the systems designated tasks as well as the physical characteristics of the user. Lastly, as previous studies have suggested, amplifying the movement of manipulated objects in relation to the movement of the controller decreases the required movement of the user, reducing fatigue and increasing the user’s reach. However, this amplification was perceived as a reduction in precision, which some users valued more than movement reduction. Therefore, the amplification factor should be adjusted in relation to the operation’s precision requirements. While directly applicable to layer management in VR, these findings could serve as guidelines for applications utilizing 2D content in a virtual environment, room-scale VR applications designed to perform abstract tasks, and productivity tools for Virtual Reality. / Virtual Reality (VR) har på senare år sett ett uppsving både vad gäller teknisk utveckling och intresse hos konsumenter. Den nya generationens VR-plattformar har stor potential för utforskande studier både vad gäller användningsområden och interaktionsgränssnitt. En av förväntningarna är att VR ska kunna användas inom grafisk formgivning som ett verktyg för att tillgå- och interagera med digitalt innehåll på nya sätt. Precis som mobila applikationer är utvecklade för mindre touch-skärmar och PC-applikationer är designade för mus- och tangentbordsinteraktion med återkoppling genom en datorskärm så kommer gränssnitten för framtidens VR applikationer att vara utformade efter denna plattforms specifikationer. En vanlig komponent i dagens grafiska redigeringsprogramvaror är bildlagerhantering; att ha en bild eller bildruta uppdelad i mindre delar där varje del kan redigeras och påverkas som en separat enhet. Denna uppsats utforskar genom en iterativ designstudie hur bildlagerhantering kan implementeras i en VR miljö med fokus på navigation, val av objekt och manipulation av objekt. Studien visar att interaktionsgränssnitten som baseras på interaktioner med verkligheten och interaktion med traditionella PC applikationer för naturliga respektive abstrakta operationer gör interaktionerna lättare att lära sig och förstå. Förutom de uppgifter som systemet är ämnat att utföra bör även utformningen av den virtuella miljön ta hänsyn till de fysiska egenskaperna hos användaren. Som tidigare studier har visat så kan ett förstärkt utslag av manipulerade objekt i förhållande till kontrollen minska den rörelse som krävs av användaren för att utföra en uppgift och därigenom minska trötthet och öka användarens effektiva räckvidd. Denna förstärkning uppfattades dock som en reducering av precision, vilket vissa användare värdesatte mer än reducering av krävd rörelse. Därför ska förstärkningsgraden sättas i relation till den precision som krävs av varje operation. Studiens resultat är direkt applicerbara för lagerhantering i VR men kan också användas som riktlinjer för VR applikationer som hanterar 2D-innehåll i en 3D miljö samt VR applikationer med syfte att användas som produktivitetsverktyg.