حروف مقطعات کے حوالے سے مستشرق نولڈ یکے اور آٹو لوتھ کی آراء کا تجزیاتی مطالعہ

Mystical letters are among the miracles of Quran. These mystical letters are present at the start of Quranic Surah’s (Chapters). These are among the mutashabihat (Analogies) of Quran. Muslim scholars tried to define their meanings. Like other aspects of Quran and Hadith orientalists talk about mystical letters of the Quran. This article analyses the theories of Noldeke and Otto Loth regarding mysterious letters of Quran. What are their views about mystical letters of the Quran? Are their views according to the Islamic point of view of mystical letters? What are the deviations and differences as compared to traditional Islamic point of view of mystical letters? This research has been analytical by nature, both qualitative and analytical methods have been implemented.  Analyses of the views of both of the scholars in the light of traditional Islamic concept of mystical letters, shows that Orientalists including Noldeke and Otto Loth thought that mystical letters are not the part of revelation. According to them these are the names of the sources from which different chapters of the Quran had been taken during its compilation. These are on the same pattern as mystical letters are present in the Jewish books. Holy Prophet had copied them. The present study argues that Quran being the book of Lord is unchanged and mystical letters are a part of it. It is further highlighted that even some orientalists argue that the opinion of Noldeke and Otto Loth is not correct.

Motion Correction in Compressively Sampled Dynamic Mr Imaging

Magnetic Resonance Imaging (MRI) is a non-invasive but slow imaging modality for studying different anatomical and functional aspects of human body. However, it is difficult for a patient to remain motionless during the slow MR acquisition process. The subject motion is one of the main hurdles in MRI due to the fact that the respiratory motion is faster compared to acquisition process resulting in ghosted and blurry recovered images. Cardiac and abdominal MR imaging is mostly affected by respiratory motion. In this thesis, compressive sensing (CS) based new approaches are developed to tackle the respiratory motion in cardiac and abdominal MRI examination. The cost function used by CS based MR recovery algorithms include ?1-norm penalty to exploit the transformed domain sparsity of the acquired MR data. The initial part of dissertation presents a comparison of surrogate functions used to approximate the l1-norm penalty. The experimental work shows that the hyperbolic tangent based function outperforms its competing function in the recovery of static MR images for different acceleration rates and various Gaussian noise levels. Based on these findings, an iterative thresholding algorithm utilizing hyperbolic tangent based ?1-norm approximation is developed to recover free breathing dynamic MR images from sub-sampled k-space data. A block matching algorithm, known as Adaptive Rood Pattern Search (ARPS) is then used to estimate and correct respiratory motion among the recovered images. In the next part, an adaptive thresholding parameter utilizing the MR data statistics is derived and used in wavelet domain shrinkage to recover both static and dynamic MR images. A novel iterative shrinkage thresholding (IST) algorithm based on the derived adaptive parameter is also proposed. Results show that the MR recovery using adaptive threshold is more effective in the presence of motion as compared to fixed threshold value. The final part presents the reduction of motion artifacts in the recovery of under-sampled abdominal and liver dynamic contrast enhanced (DCE) MR images using data binning and low-rank plus sparse (L+S) decomposition. In the data binning, radial k-space data is acquired continuously using golden-angle radial sampling pattern and grouped into various motion states or bins. The respiratory signal for binning is extracted directly from radially acquired k-space data. A compressed sensing (CS)-based L+S matrix decomposition model is then used to reconstruct good quality DCE MR images. The proposed techniques are validated using simulated and clinical MRI data.