I found that those study_id, series_id pairs have something weird. Slice heights and widths start at 640 and when it is close to middle slice, heights and widths become 608. Why does that happen? I googled it and asked ChatGPT but I couldn't find anything related to this. I couldn't any clue about this. All I know is they are Axial T2 and they have the same sizes. They are probably from the same MRI scanner. You should double check your 3D volume while dealing with this since you might break the alignment between slices.
\narray([[ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ]])\nI found that those study_id, series_id pairs have something weird. Slice heights and widths start at 640 and when it is close to middle slice, heights and widths become 608. Why does that happen? I googled it and asked ChatGPT but I couldn't find anything related to this. I couldn't any clue about this. All I know is they are Axial T2 and they have the same sizes. They are probably from the same MRI scanner. You should double check your 3D volume while dealing with this since you might break the alignment between slices.
\narray([[ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [ , ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ],\n [, ]])\nYes, I have noticed that the Axial T2 series are composed of different groups of DICOM slices. I have tried to group them based on ImageOrientationPatient attribute, but so far, I could not find a suitable way to consistently group them.
Usually we define the matrix size (i.e. height and width) of the image and then run the sequence. So I'm not sure why some images within the same sequence have different sizes.
", "rawMarkdown": "Usually we define the matrix size (i.e. height and width) of the image and then run the sequence. So I'm not sure why some images within the same sequence have different sizes.", "votes": null }, { "id": "2931967", "postDate": "07/22/2024 13:33:40", "content": "Does that kind of an artifact may happen while exporting dicom files?
", "rawMarkdown": "Does that kind of an artifact may happen while exporting dicom files?", "votes": null }, { "id": "2932359", "postDate": "07/22/2024 21:12:11", "content": "I have never encountered it in my experience.
", "rawMarkdown": "I have never encountered it in my experience.", "votes": null } ], "comments": [ { "id": 2930716, "author_name": "coderrkj", "author_url": "", "post_date": "07/21/2024 10:44:29", "content": "Yes, I have noticed that the Axial T2 series are composed of different groups of DICOM slices. I have tried to group them based on ImageOrientationPatient attribute, but so far, I could not find a suitable way to consistently group them.
Usually we define the matrix size (i.e. height and width) of the image and then run the sequence. So I'm not sure why some images within the same sequence have different sizes.
", "votes": null, "replies": [ { "id": 2931967, "author_name": "gunesevitan", "author_url": "", "post_date": "07/22/2024 13:33:40", "content": "Does that kind of an artifact may happen while exporting dicom files?
", "votes": null, "replies": [ { "id": 2932359, "author_name": "vaillant", "author_url": "", "post_date": "07/22/2024 21:12:11", "content": "I have never encountered it in my experience.
", "votes": null, "replies": [] } ] } ] } ], "raw_markdown_by_id": { "2930556": "I found that those study_id, series_id pairs have something weird. Slice heights and widths start at 640 and when it is close to middle slice, heights and widths become 608. Why does that happen? I googled it and asked ChatGPT but I couldn't find anything related to this. I couldn't any clue about this. All I know is they are Axial T2 and they have the same sizes. They are probably from the same MRI scanner. You should double check your 3D volume while dealing with this since you might break the alignment between slices.\n\n```python\narray([[ 74782131, 3401861580],\n [ 114899184, 1364910156],\n [ 335455502, 790180412],\n [ 582364168, 3242313646],\n [ 594735110, 4075111689],\n [ 766494595, 519827232],\n [ 809072026, 2594153114],\n [ 979209761, 3101981332],\n [1258848546, 178314290],\n [1271819130, 396937199],\n [1537608176, 2603019387],\n [1538136131, 533252904],\n [1603568458, 3451074679],\n [1973833645, 1328374636],\n [1992037544, 2051789894],\n [1995123254, 1133656256],\n [2083466060, 2440822236],\n [2334206006, 4191635045],\n [2470505035, 1297079442],\n [2568819355, 3366910731],\n [2755347468, 3708423406],\n [2780132468, 4151611107],\n [2795583238, 3365980706],\n [3339741647, 4195187002],\n [3542237003, 458336097],\n [3651144029, 1415702790],\n [3707028884, 3238050001],\n [3884015124, 1519537115],\n [3885334932, 4045988081],\n [3930841971, 2216414062],\n [3956571539, 4190692765],\n [4024872715, 2479444365],\n [4165566893, 807064932],\n [4165566893, 4033318924],\n [4193490688, 549563660]])\n```", "2930716": "Yes, I have noticed that the Axial T2 series are composed of different groups of DICOM slices. I have tried to group them based on `ImageOrientationPatient` attribute, but so far, I could not find a suitable way to consistently group them.", "2931855": "Usually we define the matrix size (i.e. height and width) of the image and then run the sequence. So I'm not sure why some images within the same sequence have different sizes.", "2931967": "Does that kind of an artifact may happen while exporting dicom files?", "2932359": "I have never encountered it in my experience." }, "source": "meta" }